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https://www.databricks.com/dataaisummit/speaker/kyle-hale/# | Kyle Hale - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingKyle HaleProduct Specialist at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/praveen-vemulapalli | Praveen Vemulapalli - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingPraveen VemulapalliDirector- Technology at AT&TBack to speakersPraveen is the Director-Technology for Chief Data Office at AT&T. He oversees and manages AT&T's Network Traffic Data and Artificial Intelligence platforms. Responsible for 5G Analytics/AI Research & Development (R&D). He also leads the on-premise to cloud transformation of the Core Network Usage platforms. Leading a strong team of Data Engineers, Data Scientists, ML/AI Ops Engineers and Solution Architects.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/blog/2022/02/07/structured-streaming-a-year-in-review.html | An Overview of All the New Structured Streaming Features Developed In 2021 For Databricks & Apache Spark. - The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorStructured Streaming: A Year in Reviewby Steven Yu and Ray ZhuFebruary 7, 2022 in Data EngineeringShare this postAs we enter 2022, we want to take a moment to reflect on the great strides made on the streaming front in Databricks and Apache Spark™ ! In 2021, the engineering team and open source contributors made a number of advancements with three goals in mind:Lower latency and improve stateful stream processingImprove observability of Databricks and Spark Structured Streaming workloadsImprove resource allocation and scalabilityUltimately, the motivation behind these goals was to enable more teams to run streaming workloads on Databricks and Spark, make it easier for customers to operate mission critical production streaming applications on Databricks and simultaneously optimizing for cost effectiveness and resource usage.Goal # 1: Lower latency & improved stateful processingThere are two new key features that specifically target lowering latencies with stateful operations, as well as improvements to the stateful APIs. The first is asynchronous checkpointing for large stateful operations, which improves upon a historically synchronous and higher latency design.Asynchronous CheckpointingIn this model, state updates are written to a cloud storage checkpoint location before the next microbatch begins. The advantage is that if a stateful streaming query fails, we can easily restart the query by using the information from the last successfully completed batch. In the asynchronous model, the next microbatch does not have to wait for state updates to be written, improving the end-to-end latency of the overall microbatch execution.You can learn more about this feature in an upcoming deep-dive blog post, and try it in Databricks Runtime 10.3 and above.Arbitrary stateful operator improvementsIn a much earlier post, we introduced Arbitrary Stateful Processing in Structured Streaming with [flat]MapGroupsWithState. These operators provide a lot of flexibility and enable more advanced stateful operations beyond aggregations. We’ve introduced improvements to these operators that:Allow initial state, avoiding the need to reprocess all your streaming data.Enable easier logic testing by exposing a new TestGroupState interface, allowing users to create instances of GroupState and access internal values for what has been set, simplifying unit tests for the state transition functions.Allow Initial StateLet’s start with the following flatMapGroupswithState operator:
def flatMapGroupsWithState[S: Encoder, U: Encoder](
outputMode: OutputMode,
timeoutConf: GroupStateTimeout,
initialState: KeyValueGroupedDataset[K, S])(
func: (K, Iterator[V], GroupState[S]) => Iterator[U])
This custom state function maintains a running count of fruit that have been encountered.
val fruitCountFunc =(key: String, values: Iterator[String], state: GroupState[RunningCount]) => {
val count = state.getOption.map(_.count).getOrElse(0L) + valList.size
state.update(new RunningCount(count))
Iterator((key, count.toString))
}
In this example, we specify the initial state to the this operator by setting starting values for certain fruit:
val fruitCountInitialDS: Dataset[(String, RunningCount)] = Seq(
("apple", new RunningCount(1)),
("orange", new RunningCount(2)),
("mango", new RunningCount(5)),
).toDS()
val fruitCountInitial = initialState.groupByKey(x => x._1).mapValues(_._2)
fruitStream
.groupByKey(x => x)
.flatMapGroupsWithState(Update, GroupStateTimeout.NoTimeout, fruitCountInitial)(fruitCountFunc)
Easier Logic TestingYou can also now test state updates using the TestGroupState API.
import org.apache.spark.sql.streaming._
import org.apache.spark.api.java.Optional
test("flatMapGroupsWithState's state update function") {
var prevState = TestGroupState.create[UserStatus](
optionalState = Optional.empty[UserStatus],
timeoutConf = GroupStateTimeout.EventTimeTimeout,
batchProcessingTimeMs = 1L,
eventTimeWatermarkMs = Optional.of(1L),
hasTimedOut = false)
val userId: String = ...
val actions: Iterator[UserAction] = ...
assert(!prevState.hasUpdated)
updateState(userId, actions, prevState)
assert(prevState.hasUpdated)
}
You can find these, and more examples in the Databricks documentation.Native support for Session WindowsStructured Streaming introduced the ability to do aggregations over event-time based windows using tumbling or sliding windows, both of which are windows of fixed-length. In Spark 3.2, we introduced the concept of session windows, which allow dynamic window lengths. This historically required custom state operators using flatMapGroupsWithState.An example of using dynamic gaps:
# Define the session window having dynamic gap duration based on eventType
session_window expr = session_window(events.timestamp, \
when(events.eventType == "type1", "5 seconds") \
.when(events.eventType == "type2", "20 seconds") \
.otherwise("5 minutes"))
# Group the data by session window and userId, and compute the count of each group
windowedCountsDF = events \
.withWatermark("timestamp", "10 minutes") \
.groupBy(events.userID, session_window_expr) \
.count()
Goal #2: Improve observability of streaming workloadsWhile the StreamingQueryListener API allows you to asynchronously monitor queries within a SparkSession and define custom callback functions for query state, progress, and terminated events, understanding back pressure and reasoning about where the bottlenecks are in a microbatch were still challenging. As of Databricks Runtime 8.1, the StreamingQueryProgress object reports data source specific back pressure metrics for Kafka, Kinesis, Delta Lake and Auto Loader streaming sources.An example of the metrics provided for Kafka:
{
"sources" : [ {
"description" : "KafkaV2[Subscribe[topic]]",
"metrics" : {
"avgOffsetsBehindLatest" : "4.0",
"maxOffsetsBehindLatest" : "4",
"minOffsetsBehindLatest" : "4",
"estimatedTotalBytesBehindLatest" : "80.0"
},
} ]
}
Databricks Runtime 8.3 introduces real-time metrics to help understand the performance of the RocksDB state store and debug the performance of state operations. These can also help identify target workloads for asynchronous checkpointing.An example of the new state store metrics:
{
"id" : "6774075e-8869-454b-ad51-513be86cfd43",
"runId" : "3d08104d-d1d4-4d1a-b21e-0b2e1fb871c5",
"batchId" : 7,
"stateOperators" : [ {
"numRowsTotal" : 20000000,
"numRowsUpdated" : 20000000,
"memoryUsedBytes" : 31005397,
"numRowsDroppedByWatermark" : 0,
"customMetrics" : {
"rocksdbBytesCopied" : 141037747,
"rocksdbCommitCheckpointLatency" : 2,
"rocksdbCommitCompactLatency" : 22061,
"rocksdbCommitFileSyncLatencyMs" : 1710,
"rocksdbCommitFlushLatency" : 19032,
"rocksdbCommitPauseLatency" : 0,
"rocksdbCommitWriteBatchLatency" : 56155,
"rocksdbFilesCopied" : 2,
"rocksdbFilesReused" : 0,
"rocksdbGetCount" : 40000000,
"rocksdbGetLatency" : 21834,
"rocksdbPutCount" : 1,
"rocksdbPutLatency" : 56155599000,
"rocksdbReadBlockCacheHitCount" : 1988,
"rocksdbReadBlockCacheMissCount" : 40341617,
"rocksdbSstFileSize" : 141037747,
"rocksdbTotalBytesReadByCompaction" : 336853375,
"rocksdbTotalBytesReadByGet" : 680000000,
"rocksdbTotalBytesReadThroughIterator" : 0,
"rocksdbTotalBytesWrittenByCompaction" : 141037747,
"rocksdbTotalBytesWrittenByPut" : 740000012,
"rocksdbTotalCompactionLatencyMs" : 21949695000,
"rocksdbWriterStallLatencyMs" : 0,
"rocksdbZipFileBytesUncompressed" : 7038
}
} ],
"sources" : [ {
} ],
"sink" : {
}
}
Goal # 3: Improve resource allocation and scalabilityStreaming Autoscaling with Delta Live Tables (DLT)At Data + AI Summit last year, we announced Delta Live Tables, which is a framework that allows you to declaratively build and orchestrate data pipelines, and largely abstracts the need to configure clusters and node types. We’re taking this a step further and introducing an intelligent autoscaling solution for streaming pipelines that improves upon the existing Databricks Optimized Autoscaling. These benefits include:The new algorithm takes advantage of the new back pressure metrics to adjust cluster sizes to better handle scenarios in which there are fluctuations in streaming workloads, which ultimately leads to better cluster utilization.While the existing autoscaling solution retires nodes only if they are idle, the new DLT Autoscaler will proactively shut down selected nodes when utilization is low, while simultaneously guaranteeing that there will be no failed tasks due to the shutdown.Better Cluster Utilization:Proactive Graceful Worker Shutdown:As of writing, this feature is currently in Private Preview. Please reach out to your account team for more information.Trigger.AvailableNowIn Structured Streaming, triggers allow a user to define the timing of a streaming query’s data processing. These trigger types can be micro-batch (default), fixed interval micro-batch (Trigger.ProcessingTime(“”), one-time micro-batch (Trigger.Once), and continuous (Trigger.Continuous). Databricks Runtime 10.1 introduces a new type of trigger; Trigger.AvailableNow that is similar to Trigger.Once but provides better scalability. Like Trigger Once, all available data will be processed before the query is stopped, but in multiple batches instead of one. This is supported for Delta Lake and Auto Loader streaming sources.Example:
spark.readStream
.format("delta")
.option("maxFilesPerTrigger", "1")
.load(inputDir)
.writeStream
.trigger(Trigger.AvailableNow)
.option("checkpointLocation", checkpointDir)
.start()
SummaryAs we head into 2022, we will continue to accelerate innovation in Structured Streaming, further improving performance, decreasing latency and implementing new and exciting features. Stay tuned for more information throughout the year! Try Databricks for freeGet StartedRelated postsNative Support of Session Window in Spark Structured StreamingOctober 12, 2021 by Jungtaek Lim, Yuanjian Li and Shixiong Zhu in Engineering Blog
Apache Spark™ Structured Streaming allowed users to do aggregations on windows over event-time. Before Apache Spark 3.2™, Spark supported tumbling windows and sliding win...
What’s New in Apache Spark™ 3.1 Release for Structured StreamingApril 27, 2021 by Yuanjian Li, Shixiong Zhu and Bo Zhang in Engineering Blog
Along with providing the ability for streaming processing based on Spark Core and SQL API, Structured Streaming is one of the most important...
Infrastructure Design for Real-time Machine Learning InferenceSeptember 1, 2021 by Yu Chen in Company Blog
This is a guest authored post by Yu Chen, Senior Software Engineer, Headspace. Headspace’s core products are iOS, Android and web-based apps that f...
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데이터 레이크하우스가 최신 데이터 스택에 어떻게 부합하는지 이해하려면 참석하십시오.
지금 등록하세요솔루션산업별 솔루션금융 서비스의료 서비스 및 생명 공학제조커뮤니케이션, 미디어 및 엔터테인먼트공공 부문리테일모든 산업 보기사용 사례별 솔루션솔루션 액셀러레이터프로페셔널 서비스디지털 네이티브 비즈니스데이터 플랫폼 마이그레이션5월 9일 | 오전 8시(태평양 표준시)
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직접 참석하거나 키노트 라이브스트림을 시청하세요.지금 등록하기고객파트너클라우드 파트너AWSAzureGoogle CloudPartner Connect기술 및 데이터 파트너기술 파트너 프로그램데이터 파트너 프로그램Built on Databricks Partner Program컨설팅 & SI 파트너C&SI 파트너 프로그램파트너 솔루션클릭 몇 번만으로 검증된 파트너 솔루션과 연결됩니다.자세히회사Databricks 채용Databricks 팀이사회회사 블로그보도 자료Databricks 벤처수상 실적문의처Gartner가 Databricks를 2년 연속 리더로 선정한 이유 알아보기보고서 받기Databricks 이용해 보기데모 보기문의처로그인JUNE 26-29REGISTER NOWDemo Hub간단한 온디맨드 동영상을 통해 실무자 관점에서 Databricks를 검토해보세요. 아래 각각의 데모에는 노트북, 동영상, ebook 등 직접 활용해 볼 수 있는 자료들이 포함되어 있습니다.무료로 시작하기제품 데모Databricks 플랫폼이 데모에서는 Databricks 레이크하우스 플랫폼이 무엇인지 간략한 개요로 안내해 드립니다. 예를 들어 Apache Spark™, Delta Lake, MLflow 및 Koalas와 같은 오픈 소스 프로젝트가 Databricks 에코시스템과 어떤 면에서 적합한지 논의합니다.자세히Databricks SQLDatabricks 워크플로Unity Catalog데이터 사이언스와 머신 러닝Delta SharingDelta LakeDelta Live 테이블Delta Lake 데이터 통합 (Auto Loader와 COPY INTO)파트너 데모Azure Databricks 클라우드 통합Azure Databricks 레이크하우스 플랫폼은 데이터 레이크와 데이터 웨어하우스의 가장 좋은 점만 모아
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채용 확인하기WorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark 및 Spark 로고는 Apache Software Foundation의 상표입니다.개인 정보 보호 고지|이용약관|귀하의 개인 정보 선택|귀하의 캘리포니아 프라이버시 권리 |
https://www.databricks.com/explore/data-science-machine-learning/intro-mlflow-blog?itm_data=DSproduct-pf-dsml |
MLflow - An open source machine learning platform
|
https://www.databricks.com/glossary/data-lakehouse | What is a Data Lakehouse?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWData LakehouseAll>Data LakehouseWhat is a Data Lakehouse?A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.Data Lakehouse: Simplicity, Flexibility, and Low CostData lakehouses are enabled by a new, open system design: implementing similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. Merging them together into a single system means that data teams can move faster as they are able to use data without needing to access multiple systems. Data lakehouses also ensure that teams have the most complete and up-to-date data available for data science, machine learning, and business analytics projects. Key Technology Enabling the Data LakehouseThere are a few key technology advancements that have enabled the data lakehouse:metadata layers for data lakesnew query engine designs providing high-performance SQL execution on data lakesoptimized access for data science and machine learning tools.Metadata layers, like the open source Delta Lake, sit on top of open file formats (e.g. Parquet files) and track which files are part of different table versions to offer rich management features like ACID-compliant transactions. The metadata layers enable other features common in data lakehouses, like support for streaming I/O (eliminating the need for message buses like Kafka), time travel to old table versions, schema enforcement and evolution, as well as data validation. Performance is key for data lakehouses to become the predominant data architecture used by businesses today as it's one of the key reasons that data warehouses exist in the two-tier architecture. While data lakes using low-cost object stores have been slow to access in the past, new query engine designs enable high-performance SQL analysis. These optimizations include caching hot data in RAM/SSDs (possibly transcoded into more efficient formats), data layout optimizations to cluster co-accessed data, auxiliary data structures like statistics and indexes, and vectorized execution on modern CPUs. Combining these technologies together enables data lakehouses to achieve performance on large datasets that rivals popular data warehouses, based on TPC-DS benchmarks. The open data formats used by data lakehouses (like Parquet), make it very easy for data scientists and machine learning engineers to access the data in the lakehouse. They can use tools popular in the DS/ML ecosystem like pandas, TensorFlow, PyTorch and others that can already access sources like Parquet and ORC. Spark DataFrames even provide declarative interfaces for these open formats which enable further I/O optimization. The other features of a data lakehouse, like audit history and time travel, also help with improving reproducibility in machine learning. To learn more about the technology advances underpinning the move to the data lakehouse, see the CIDR paper Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics and another academic paper Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores.History of Data ArchitecturesBackground on Data WarehousesData warehouses have a long history in decision support and business intelligence applications, though were not suited or were expensive for handling unstructured data, semi-structured data, and data with high variety, velocity, and volume.Emergence of Data LakesData lakes then emerged to handle raw data in a variety of formats on cheap storage for data science and machine learning, though lacked critical features from the world of data warehouses: they do not support transactions, they do not enforce data quality, and their lack of consistency/isolation makes it almost impossible to mix appends and reads, and batch and streaming jobs.Common Two-Tier Data ArchitectureData teams consequently stitch these systems together to enable BI and ML across the data in both these systems, resulting in duplicate data, extra infrastructure cost, security challenges, and significant operational costs. In a two-tier data architecture, data is ETLd from the operational databases into a data lake. This lake stores the data from the entire enterprise in low-cost object storage and is stored in a format compatible with common machine learning tools but is often not organized and maintained well. Next, a small segment of the critical business data is ETLd once again to be loaded into the data warehouse for business intelligence and data analytics. Due to multiple ETL steps, this two-tier architecture requires regular maintenance and often results in data staleness, a significant concern of data analysts and data scientists alike according to recent surveys from Kaggle and Fivetran. Learn more about the common issues with the two-tier architecture.Additional ResourcesWhat is a Lakehouse? - BlogLakehouse Architecture: From Vision to RealityIntroduction to Lakehouse and SQL AnalyticsLakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced AnalyticsDelta Lake: The Foundation of Your LakehouseThe Databricks Lakehouse PlatformData Brew Vidcast: Season 1 on Data LakehousesThe Rise of the Lakehouse ParadigmBuilding the Data Lakehouse by Bill InmonThe Data Lakehouse Platform for DummiesBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/solutions/accelerators/market-risk | Solution Accelerator - How to build: A modern risk management solution in financial services | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorBuild a Modern Risk Management Solution in Financial ServicesAdopt a more agile approach to risk management by unifying data and AI in the Lakehouse
This solution has two parts. First, it shows how Delta Lake and MLflow can be used for value-at-risk calculations — showing how banks can modernize their risk management practices by back-testing, aggregating and scaling simulations by using a unified approach to data analytics with the Lakehouse. Secondly, the solution uses alternative data to move toward a more holistic, agile and forward-looking approach to risk management and investments.
Read the full write-up part 1
Read the full write-up part 2
Download notebookBenefits and business valueGain a holistic viewUse a more complete view of risk and investment with real-time and alternative data that can be analyzed on demand
Detect emerging threatsProactively identify emerging threats to protect capital and optimize exposuresAchieve speed at scaleScan through large volumes of data quickly and thoroughly to respond in time and reduce risk
Reference ArchitectureResourcesWorkshopLearn moreBlogLearn moreeBookLearn moreDeliver AI innovation faster with Solution Accelerators for popular industry use cases. See our full library of solutionsReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/br | Página inicial da Databricks - DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT
Adeus, Data Warehouse. Olá, Lakehouse.
Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna.
Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT
Descubra a Lakehouse para Manufatura
Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWA melhor solução de data warehouse é o lakehouseCombine todos os seus dados, análises e IA em uma única plataformaComece gratuitamenteSaiba maisReduza custos e acelere a inovação na Lakehouse PlatformSaiba maisUnificadaUma plataforma para seus dados, consistentemente governada e disponível para todas as suas análises e IAAbertaConstruída em padrões abertos e integrada a todas as nuvens para funcionar perfeitamente em sua pilha de dados modernaEscalávelEscale de forma eficiente com cada carga de trabalho, de simples pipelines de dados até grandes LLMsOrganizações orientadas a dados escolhem o Lakehouse
Ver todos os clientes
O Lakehouse unifica suas equipes de dadosGerenciamento e engenharia de dadosSimplifique a ingestão e o gerenciamento de dadosCom ETL automatizado e confiável, compartilhamento de dados aberto e seguro e desempenho extremamente rápido, o Delta Lake transforma seu data lake no destino para todos os seus dados estruturados, semiestruturados e não estruturados.Saiba mais Assista à demoData warehousingObtenha novos insights a partir dos dados mais completosCom acesso imediato aos dados mais recentes e abrangentes e o poder do Databricks SQL — que é até 12 vezes melhor em relação ao preço/desempenho do que data warehouses em nuvem tradicionais — analistas e cientistas de dados agora podem obter novos insights rapidamente.Saiba mais Assista à demoData science e machine learningAcelerar o ML em todo o ciclo de vidaO lakehouse é a base do Databricks Machine Learning — uma solução nativa e colaborativa de dados para todo o ciclo de vida do aprendizado de máquina, da preparação à produção. Combinado com pipelines de dados de alta qualidade e desempenho, o lakehouse acelera o machine learning e a produtividade da equipe.Saiba mais Assista à demoGovernança e compartilhamento de dadosUnifique a governança e compartilhamento para dados, análises e IACom a Databricks, você tem acesso a um modelo comum de segurança e governança para todos os seus dados, análises e ativos de IA no lakehouse, em qualquer da nuvem. Descubra e compartilhe dados entre plataformas, nuvens ou regiões sem replicação ou vínculo com um fornecedor. Você pode até distribuir produtos de dados em um mercado aberto.Saiba mais Assista à demoO data warehouse é história. Descubra por que o lakehouse é a arquitetura moderna para dados e IA.Descubra o lakehouseCatálogo de sessões já disponívelJunte-se a nós em São Francisco para explorar o ecossistema do lakehouse e os avanços em tecnologias de código abertoExplorar sessõesVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivo.Obtenha o relatório600 CIOs. 14 setores. 18 países.Este novo estudo descobriu que a estratégia de dados é fundamental para o sucesso da IA. Veja mais perspectivas do CIO.Obtenha o relatórioO Livro Completo da Engenharia de DadosFique por dentro das tendências mais recentes em engenharia de dados baixando sua cópia do Livro Completo da Engenharia de Dados.Obter o e-BookTudo pronto para começar?Experimente o Databricks gratuitamenteProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers
at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/dataaisummit/speaker/jonathan-hollander/# | Jonathan Hollander - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJonathan HollanderVP, Enterprise Data Technology Platforms at TD BankBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/mike-del-balso | Mike Del Balso - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMike Del BalsoCo-founder + CEO at TectonBack to speakersMike Del Balso is the co-founder of Tecton, where he is building next-generation data infrastructure for Real-Time ML. Before Tecton, Mike was the PM lead for the Uber's Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/fr/try-databricks?itm_data=Homepage-HeroCTA-Trial | Essayer gratuitement Databricks | DatabricksEssayer gratuitement DatabricksExpérimentez pleinement la plateforme Databricks gratuitement pendant 14 jours, sur AWS, Microsoft Azure ou Google Cloud, au choix.Simplification de l'ingestion des données et automatisation de l’ETLImportez des données depuis des centaines de sources. Utilisez une approche déclarative simple pour créer des pipelines de données.Collaborez dans votre langage préféréCodez en Python, R, Scala et SQL. Bénéficiez du RBAC, d’intégrations avec Git et d’outils comme la rédaction collaborative et la gestion automatique des versions.Un rapport performance / prix jusqu'à 12 fois supérieur à celui des data warehousesDécouvrez pourquoi plus de 7 000 clients dans le monde s’appuient sur Databricks pour toutes leurs charges de travail de la BI à l’IA.Créez votre compte Databricks1/2PrénomNomE-mail professionnelEntrepriseIntitulé de posteNuméro de téléphone (facultatif)Veuillez sélectionnerPaysContinuerAvis de confidentialité (mis à jour)Conditions d'utilisationVos choix de confidentialitéVos droits de confidentialité en Californie |
https://www.databricks.com/dataaisummit/speaker/ian-galloway | Ian Galloway - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingIan GallowaySenior Director, Applications at Collins AerospaceBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/rajesh-iyer | Rajesh Iyer - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRajesh IyerVice President Financial Services Insights & Data at CapgeminiBack to speakersRajesh is the head of AI COE for Financial Services globally and drives growth in the Machine Learning and Artificial Intelligence Practice, as part of the Insights & Data Global Service Line at Capgemini. He has 27 years of Financial Services Data Sciences and AI/ML experience across multiple domains, such as Risk, Distribution, Operations and Marketing, but working mostly with very large financial services institutions across the Banking and Capital Markets and Insurance verticals.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/kr/partnerconnect | Partner Connect | DatabricksSkip to main content플랫폼Databricks 레이크하우스 플랫폼Delta Lake데이터 거버넌스데이터 엔지니어링데이터 스트리밍데이터 웨어하우징데이터 공유머신 러닝데이터 사이언스가격Marketplace오픈 소스 기술보안 및 신뢰 센터웨비나 5월 18일 / 오전 8시(태평양 표준시)
안녕, 데이터 웨어하우스. 안녕하세요, 레이크하우스입니다.
데이터 레이크하우스가 최신 데이터 스택에 어떻게 부합하는지 이해하려면 참석하십시오.
지금 등록하세요솔루션산업별 솔루션금융 서비스의료 서비스 및 생명 공학제조커뮤니케이션, 미디어 및 엔터테인먼트공공 부문리테일모든 산업 보기사용 사례별 솔루션솔루션 액셀러레이터프로페셔널 서비스디지털 네이티브 비즈니스데이터 플랫폼 마이그레이션5월 9일 | 오전 8시(태평양 표준시)
제조업을 위한 레이크하우스 살펴보기
코닝이 수동 검사를 최소화하고 운송 비용을 절감하며 고객 만족도를 높이는 중요한 결정을 내리는 방법을 들어보십시오.지금 등록하세요학습관련 문서교육 및 인증데모리소스온라인 커뮤니티University Alliance이벤트Data + AI Summit블로그LabsBeacons2023년 6월 26일~29일
직접 참석하거나 키노트 라이브스트림을 시청하세요.지금 등록하기고객파트너클라우드 파트너AWSAzureGoogle CloudPartner Connect기술 및 데이터 파트너기술 파트너 프로그램데이터 파트너 프로그램Built on Databricks Partner Program컨설팅 & SI 파트너C&SI 파트너 프로그램파트너 솔루션클릭 몇 번만으로 검증된 파트너 솔루션과 연결됩니다.자세히회사Databricks 채용Databricks 팀이사회회사 블로그보도 자료Databricks 벤처수상 실적문의처Gartner가 Databricks를 2년 연속 리더로 선정한 이유 알아보기보고서 받기Databricks 이용해 보기데모 보기문의처로그인JUNE 26-29REGISTER NOWPartner Connect레이크하우스로 데이터, 분석 및 AI 솔루션을 간편하게 탐색 및 통합데모 보기Partner Connect를 사용하면 Databricks 플랫폼에서 직접 데이터, 분석 및 AI 도구를 손쉽게 탐색하고, 현재 사용하고 있는 도구에 빠르게 통합할 수 있습니다. Partner Connect에서는 클릭 몇 번만으로 도구 통합을 간소화하고 레이크하우스의 기능을 신속히 확장할 수 있습니다.데이터와 AI 도구를 <br />레이크하우스에 연결원하는 데이터 및 AI 도구를 레이크하우스에 간편하게 연결하고 모든 분석 사용 사례 지원새로운 사용 사례를 위한 <br />검증된 데이터 및 AI 솔루션 탐색검증된 파트너 솔루션으로 이루어진 원스톱 포털을 통해 다음 데이터 애플리케이션 구축 기간 단축클릭 몇 번으로 <br />사전 구축된 통합 설정Partner Connect는 클러스터, 토큰, 연결 파일 등의 리소스를 자동으로 구성하여 파트너 솔루션에 연결함으로써 통합 작업 간소화파트너로 시작하기Databricks 파트너는 고객에게 더욱 빠른 분석 인사이트를 제공할 역량을 갖추고 있습니다. Databricks의 개발 및 파트너 리소스를 활용하여 클라우드 기반 오픈 플랫폼과 함께 성장하세요. 파트너 되기“오랜 파트너십을 바탕으로 구축된 Partner Connect는 우리 회사와 고객 간의 통합 환경을 설계하는 데 유용합니다. Partner Connect를 통해 현재 Fivetran을 사용하거나 Partner Connect에서 우리 회사를 발견한 수천 곳의 Databricks 고객사에 보다 쉽게 간소화된 경험을 제공하고 있으며, 고객사에서는 데이터의 인사이트와 더욱 다양한 분석 사용 사례를 검색하고, 수백 개의 데이터 소스를 레이크하우스로 간편하게 연결해 레이크하우스를 통한 가치 창출 기간을 단축합니다.”— George Fraser, Fivetran CEO데모FivetranSaaS 앱(예: Salesforce, Google Analytics)을 비롯한 180개 이상의 앱에서 얻은 데이터를 레이크하우스로 연결dbtdbt Cloud 및 Databricks를 사용하여 데이터 변환 구축 시작하기Power BIDatabricks 레이크하우스의 성능과 기술을 모든 사용자에게 제공합니다.tableauTableau Desktop과 Databricks SQL을 연결하여 모든 사용자에게 데이터 레이크하우스를 제공함으로써 현대적 분석을 지원합니다.Rivery데이터 수집, 변환 그리고 Delta Lake로의 전송까지 데이터 여정 간소화Labelbox레이크하우스에서 AI 및 분석을 위한 비구조적 데이터를 간편하게 준비합니다.Prophecy시각적 드래그앤드롭 인터페이스를 사용하여 Spark 및 Delta 파이프라인 구축 및 배포아르시온분산된 CDC 기반 복제 플랫폼에서 데이터 소스를 레이크하우스로 연결무료 시험판 사용해 보기리소스블로그2023년 2월 – 파트너 커넥트의 새로운 파트너 통합 발표2022년 9월 – 파트너 커넥트에 새로운 파트너 통합 도입2022년 6월 – 파트너 커넥트의 새로운 파트너 통합 발표Partner Connect를 사용하여 Databricks에서 비즈니스 구축관련 문서Databricks Partner Connect 가이드더 자세히 알아볼 준비가 되셨나요?Databricks의 개발 및 파트너 리소스를 활용하여 클라우드 기반 오픈 플랫폼과 함께 성장하세요.파트너 되기문의제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티솔루션산업 기준프로페셔널 서비스솔루션산업 기준프로페셔널 서비스회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처Databricks
채용 확인하기WorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/solutions/accelerators/overall-equipment-effectiveness | Overall Equipment Effectiveness and KPI Monitoring | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorOverall Equipment EffectivenessPre-built code, sample data and step-by-step instructions ready to go in a Databricks notebook
Get startedAchieve performant and scalable end-to-end equipment monitoringFor operational teams within manufacturing, it’s critical to monitor and measure equipment performance. The advancements in Industry 4.0 and smart manufacturing have allowed manufacturers to collect vast volumes of sensor and equipment data. Making sense of this data to measure productivity provides a crucial competitive advantage.Overall Equipment Effectiveness (OEE) has become the standard for measuring manufacturing equipment productivity.The computation of OEE has traditionally been a manual exercise, and has been difficult to compute at the latency and scale required with legacy systems. In this Solution Accelerator, we demonstrate how the computation of OEE may be achieved in a multi-factory environment and in near real-time on Databricks.Get started with our Solution Accelerator for OEE to realize performant and scalable end-to-end equipment monitoring to:Incrementally ingest and process data from sensor/IoT devices in a variety of formatsCompute and surface KPIs and metrics to drive valuable insightsOptimize plant operations with data-driven decisionsDownload notebookResourcesBlogLearn moreeBookDownload nowWebinarLearn moreDeliver AI innovation faster with Solution Accelerators for popular industry use cases. See our full library of solutionsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/dataaisummit/speaker/christian-hamilton/# | Christian Hamilton - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingChristian HamiltonDirector, Data Science Technology at 84.51Back to speakersChristian Hamilton is a Director of Data Science Technology at 84.51°. He has spent 22 years in Kroger companies, holding diverse titles in Data Science, Retail Operations, and Finance. His work in emerging technology includes developing the first recommender sciences for Kroger’s digital channels & implementing spark streaming. He’s currently focused on democratizing data across the enterprise, establishing single sources of truth, empowering collaboration, and championing observability and governance.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/nat-friedman | Nat Friedman - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingNat FriedmanCreator of Copilot; Former CEO at GithubBack to speakersNat Friedman has founded two startups, led GitHub as CEO from 2018 to 2022, and now invests in infrastructure, AI, and developer companies.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/blog/category/data-and-ai/industry-insights | The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWLoading...ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/de/try-databricks?itm_data=Homepage-BottomCTA-Trial | Databricks kostenlos testen | DatabricksDatabricks kostenlos testenTesten Sie die komplette Databricks-Plattform 14 Tage lang kostenlos in AWS, Microsoft Azure oder Google Cloud. Die Wahl des Cloud-Anbieters liegt bei Ihnen.Datenaufnahme vereinfachen und ETL automatisierenNehmen Sie Daten aus Hunderten von Quellen auf. Verwenden Sie einen einfachen deklarativen Ansatz zur Erstellung von Datenpipelines.In Ihrer bevorzugten Sprache zusammenarbeitenCode in Python, R, Scala und SQL mit Co-Authoring, automatischer Versionierung, Git-Integrationen und RBAC.12x besseres Preis/Leistungsverhältnis als Cloud Data WarehousesErfahren Sie, warum mehr als 7.000 Kunden weltweit auf Databricks für all ihre Workloads von BI bis KI vertrauen.Erstellen Sie Ihr Databricks-Konto1/2VornameNachnameBerufliche E-Mail-AdresseUnternehmenStellenbezeichnungRufnummer (Optional)Bitte auswählenLandWeiterDatenschutzhinweis (aktualisiert)Terms of UseIhre DatenschutzwahlenIhre kalifornischen Datenschutzrechte |
https://www.databricks.com/dataaisummit/speaker/manbir-paul | Manbir Paul - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingManbir PaulVP of Engineering, Data Insights and MarTech at SephoraBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/glossary/hadoop | Apache Hadoop: What is it and how can you use it?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWWhat Is Hadoop?All>What Is Hadoop?Try Databricks for freeGet StartedApache Hadoop is an open source, Java-based software platform that manages data processing and storage for big data applications. The platform works by distributing Hadoop big data and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel. Some key benefits of Hadoop are scalability, resilience and flexibility. The Hadoop Distributed File System (HDFS) provides reliability and resiliency by replicating any node of the cluster to the other nodes of the cluster to protect against hardware or software failures. Hadoop flexibility allows the storage of any data format including structured and unstructured data.Migrating From Hadoop to Data Lakehouse for Dummies
Get faster insights at a lower cost when you migrate to the lakehouse.Download NowHowever, Hadoop architectures present a list of challenges, especially as time goes on. Hadoop can be overly complex and require significant resources and expertise to set up, maintain and upgrade. It is also time-consuming and inefficient due to the frequent reads and writes used to perform computations. The long-term viability of Hadoop continues to degrade as major Hadoop providers begin to shift away from the platform and because the accelerated need to digitize has encouraged many companies to reevaluate their relationship with Hadoop. The best solution to modernize your data platform is to migrate from Hadoop to the Databricks Lakehouse Platform. Read more about the challenges with Hadoop, and the shift toward modern data platforms, in our blog post.What is Hadoop programming?In the Hadoop framework, code is mostly written in Java but some native code is based in C. Additionally, command-line utilities are typically written as shell scripts. For Hadoop MapReduce, Java is most commonly used but through a module like Hadoop streaming, users can use the programming language of their choice to implement the map and reduce functions.What is a Hadoop database?Hadoop isn’t a solution for data storage or relational databases. Instead, its purpose as an open-source framework is to process large amounts of data simultaneously in real-time.Data is stored in the HDFS, however, this is considered unstructured and does not qualify as a relational database. In fact, with Hadoop, data can be stored in an unstructured, semi-structured, or structured form. This allows for greater flexibility for companies to process big data in ways that meet their business needs and beyond.What type of database is Hadoop?Technically, Hadoop is not in itself a type of database such as SQL or RDBMS. Instead, the Hadoop framework gives users a processing solution to a wide range of database types.Hadoop is a software ecosystem that allows businesses to handle huge amounts of data in short amounts of time. This is accomplished by facilitating the use of parallel computer processing on a massive scale. Various databases such as Apache HBase can be dispersed amongst data node clusters contained on hundreds or thousands of commodity servers.When was Hadoop invented?Apache Hadoop was born out of a need to process ever increasingly large volumes of big data and deliver web results faster as search engines like Yahoo and Google were getting off the ground.Inspired by Google’s MapReduce, a programming model that divides an application into small fractions to run on different nodes, Doug Cutting and Mike Cafarella started Hadoop in 2002 while working on the Apache Nutch project. According to a New York Times article, Doug named Hadoop after his son's toy elephant.A few years later, Hadoop was spun off from Nutch. Nutch focused on the web crawler element, and Hadoop became the distributed computing and processing portion. Two years after Cutting joined Yahoo, Yahoo released Hadoop as an open source project in 2008. The Apache Software Foundation (ASF) made Hadoop available to the public in November 2012 as Apache Hadoop.What’s the impact of Hadoop?Hadoop was a major development in the big data space. In fact, it’s credited with being the foundation for the modern cloud data lake. Hadoop democratized computing power and made it possible for companies to analyze and query big data sets in a scalable manner using free, open source software and inexpensive, off-the-shelf hardware.This was a significant development because it offered a viable alternative to the proprietary data warehouse (DW) solutions and closed data formats that had - until then - ruled the day.With the introduction of Hadoop, organizations quickly had access to the ability to store and process huge amounts of data, increased computing power, fault tolerance, flexibility in data management, lower costs compared to DWs, and greater scalability. Ultimately, Hadoop paved the way for future developments in big data analytics, like the introduction of Apache Spark.What is Hadoop used for?When it comes to Hadoop, the possible use cases are almost endless.RetailLarge organizations have more customer data available on hand than ever before. But often, it’s difficult to make connections between large amounts of seemingly unrelated data. When British retailer M&S deployed the Hadoop-powered Cloudera Enterprise, they were more than impressed with the results.Cloudera uses Hadoop-based support and services for the managing and processing of data. Shortly after implementing the cloud-based platform, M&S found they were able to successfully leverage their data for much improved predictive analytics.This led them to more efficient warehouse use and prevented stock-outs during “unexpected” peaks in demand and gaining a huge advantage over the competition.FinanceHadoop is perhaps more suited to the finance sector than any other. Early on, the software framework was quickly pegged for primary use in handling the advanced algorithms involved with risk modeling. It’s exactly the type of risk management that could help avoid the credit swap disaster that led to the 2008 recession.Banks have also realized this same logic also applies to managing risk for customer portfolios. Today, it’s common for financial institutions to implement Hadoop to better manage the financial security and performance of their client’s assets. JPMorgan Chase is just one of many industry giants that use Hadoop to manage exponentially increasing amounts of customer data from across the globe.HealthcareWhether nationalized or privatized, healthcare providers of any size deal with huge volumes of data and customer information. Hadoop frameworks allow for doctors, nurses and carers to have easy access to the information they need when they need it and it also makes it easy to aggregate data that provides actionable insights. This can apply to matters of public health, better diagnostics, improved treatments and more.Academic and research institutions can also leverage a Hadoop framework to boost their efforts. Take for instance, the field of genetic disease which includes cancer. We have the human genome mapped out and there are nearly three billion base pairs in total. In theory, everything to cure an army of diseases is now right in front of our faces.But to identify complex relationships, systems like Hadoop will be necessary to process such a large amount of information.Security and law enforcementHadoop can help improve the effectiveness of national and local security, too. When it comes to solving related crimes spread across multiple regions, a Hadoop framework can streamline the process for law enforcement by connecting two seemingly isolated events. By cutting down on the time to make case connections, agencies will be able to put out alerts to other agencies and the public as quickly as possible.In 2013, The National Security Agency (NSA) concluded that the open-source Hadoop software was superior to the expensive alternatives they’d been implementing. They now use the framework to aid in the detection of terrorism, cybercrime and other threats.How does Hadoop work?Hadoop is a framework that allows for the distribution of giant data sets across a cluster of commodity hardware. Hadoop processing is performed in parallel on multiple servers simultaneously.Clients submit data and programs to Hadoop. In simple terms, HDFS (a core component of Hadoop) handles the Metadata and distributed file system. Next, Hadoop MapReduce processes and converts the input/output data. Lastly, YARN divides the tasks across the cluster.With Hadoop, clients can expect much more efficient use of commodity resources with high availability and a built-in point of failure detection. Additionally, clients can expect quick response times when performing queries with connected business systems.In all, Hadoop provides a relatively easy solution for organizations looking to make the most out of big data.What language is Hadoop written in?The Hadoop framework itself is mostly built from Java. Other programming languages include some native code in C and shell scripts for command lines. However, Hadoop programs can be written in many other languages including Python or C++. This allows programmers the flexibility to work with the tools they’re most familiar with.How to use HadoopAs we’ve touched upon, Hadoop creates an easy solution for organizations that need to manage big data. But that doesn’t mean it's always straightforward to use. As we can learn from the use cases above, how you choose to implement the Hadoop framework is pretty flexible.How your business analysts, data scientists, and developers. decide to use Hadoop will all depend on your organization and its goals.Hadoop is not for every company but most organizations should re-evaluate their relationship with Hadoop. If your business handles large amounts of data as part of its core processes, Hadoop provides a flexible, scalable and affordable solution to fit your needs. From there, it’s mostly up to the imagination and technical abilities of you and your team.Hadoop query exampleHere are a few examples of how to query Hadoop:Apache HiveApache Hive was the early go-to solution for how to query SQL with Hadoop. This module emulates the behavior, syntax and interface of MySQL for programming simplicity. It’s a great option if you already heavily use Java applications as it comes with a built-in Java API and JDBC drivers. Hive offers a quick and straightforward solution for developers but it’s also quite limited as the software’s rather slow and suffers from read-only capabilities.IBM BigSQLThis offering from IBM is a high-performance massively parallel processing (MPP) SQL engine for Hadoop. Its query solution catered to enterprises that need ease in a stable and secure environment. In addition to accessing HDFS data, it can also pull from RDBMS, NoSQL databases, WebHDFS and other sources of data.What is the Hadoop ecosystem?The term Hadoop is a general name that may refer to any of the following:The overall Hadoop ecosystem, which encompasses both the core modules and related sub-modules.The core Hadoop modules, including Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common (discussed below). These are the basic building blocks of a typical Hadoop deployment.Hadoop-related sub-modules, including: Apache Hive, Apache Impala, Apache Pig, and Apache Zookeeper, and Apache Flume among others. These related pieces of software can be used to customize, improve upon, or extend the functionality of core Hadoop.What are the core Hadoop modules?HDFS - Hadoop Distributed File System. HDFS is a Java-based system that allows large data sets to be stored across nodes in a cluster in a fault-tolerant manner.YARN - Yet Another Resource Negotiator. YARN is used for cluster resource management, planning tasks, and scheduling jobs that are running on Hadoop.MapReduce - MapReduce is both a programming model and big data processing engine used for the parallel processing of large data sets. Originally, MapReduce was the only execution engine available in Hadoop. But, later on Hadoop added support for others, including Apache Tez and Apache Spark.Hadoop Common - Hadoop Common provides a set of services across libraries and utilities to support the other Hadoop modules.What are the Hadoop ecosystem components?Several core components make up the Hadoop ecosystem.HDFSThe Hadoop Distributed File System is where all data storage begins and ends. This component manages large data sets across various structured and unstructured data nodes. Simultaneously, it maintains the Metadata in the form of log files. There are two secondary components of HDFS: the NameNode and the DataNode.NameNodeThe master Daemon in Hadoop HDFS is NameNode. This component maintains the filesystem namespace and regulates client access to said files. It’s also known as the Master node and stores Metadata like the number of blocks and their locations. It consists mainly of files and directories and performs file system executions such as naming, closing and opening files.DataNodeThe second component is the slave Daemon and named the DataNode. This HDFS component stores the actual data or blocks as it performs client-requested read and write functions. This means DataNode also is responsible for replica creation, deletion and replication as instructed by the Master NameNode.The DataNode consists of two system files, one for data and one for recording block metadata. When an application is started up, handshaking takes place between the Master and Slave daemons to verify namespace and software version. Any mismatches will automatically take down the DataNode.MapReduceHadoop MapReduce is the core processing component of the Hadoop ecosystem. This software provides an easy framework for application writing when it comes to handling massive amounts of structured and unstructured data. This is mainly achieved by its facilitation of parallel processing of data across various nodes on commodity hardware.MapReduce handles job scheduling from the client. User-requested tasks are divided into independent tasks and processes. Next, these MapReduce jobs are differentiated into subtasks across the clusters and nodes throughout the commodity servers.This is accomplished by two phases; the Map phase and the Reduce phase. During the Map phase, the data set is converted into another set of data broken down into key/value pairs. Next, the Reduce phase converts the output according to the programmer via the InputFormat class.Programmers specify two main functions in MapReduce. The Map function is the business logic for processing data. The Reduce function produces a summary and aggregate of the intermediate data output of the map function, producing the final output.YARNIn simple terms, Hadoop YARN is a newer and much-improved version of MapReduce. However, that is not a completely accurate picture. This is because YARN is also used for scheduling and processing and the executions of job sequences. But YARN is the resource management layer of Hadoop where each job runs on the data as a separate Java application.Acting as the framework’s operating system, YARN allows things like batch processing and f data handled on a single platform. Much above the capabilities of MapReduce, YARN allows programmers to build interactive and real-time streaming applications.YARN allows for programmers to run as many applications as needed on the same cluster. It provides a secure and stable foundation for the operational management and sharing of system resources for maximum efficiency and flexibility.What are some examples of popular Hadoop-related software?Other popular packages that are not strictly a part of the core Hadoop modules but that are frequently used in conjunction with them include:Apache Hive is data warehouse software that runs on Hadoop and enables users to work with data in HDFS using a SQL-like query language called HiveQL.Apache Impala is the open source, native analytic database for Apache Hadoop.Apache Pig is a tool that is generally used with Hadoop as an abstraction over MapReduce to analyze large sets of data represented as data flows. Pig enables operations like join, filter, sort, and load.Apache Zookeeper is a centralized service for enabling highly reliable distributed processing.Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.Apache Oozie is a workflow scheduler system to manage Apache Hadoop jobs. Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions.Interest piqued? Read more about the Hadoop ecosystem.How to use Hadoop for analyticsDepending on data sources and organizational needs, there are three main ways to use the Hadoop framework for analytics.Deploy in your corporate data center(s)This is often a time-effective and financially sound option for those businesses with the necessary existing resources. Otherwise, setting up the technical equipment and IT staff required may overextend monetary and team resources. This option does give businesses greater control over the security and privacy of data.Go with the cloudBusinesses that desire a much more rapid implementation, lower upfront costs and lower maintenance requirements will want to leverage a cloud-based service. With a cloud provider, data and analytics are run on commodity hardware that exists in the cloud. These services streamline the processing of big data at an affordable price but come with certain drawbacks.Firstly, anything that’s on the public internet is fair game for hackers and the like. Secondly, service outages to the internet and network providers can grind your business systems to a halt. For existing framework users, they may involve something like needing to migrate from Hadoop to the Lakehow Architecture.On-premise providersThose opting for better uptime, privacy and security will find all three things with an on-premise Hadoop provider. These vendors offer the best of both worlds. They can streamline the process by providing all equipment, software and service. But since the infrastructure is on-premises, you gain all the benefits that large corporations get from having data centers.What are the benefits of Hadoop?Scalability - Unlike traditional systems that limit data storage, Hadoop is scalable as it operates in a distributed environment. This allowed data architects to build early data lakes on Hadoop. Learn more about the history and evolution of data lakes.Resilience - The Hadoop Distributed File System (HDFS) is fundamentally resilient. Data stored on any node of a Hadoop cluster is also replicated on other nodes of the cluster to prepare for the possibility of hardware or software failures. This intentionally redundant design ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.Flexibility - Differing from relational database management systems, when working with Hadoop, you can store data in any format, including semi-structured or unstructured formats. Hadoop enables businesses to easily access new data sources and tap into different types of data.What are the challenges with Hadoop architectures?Complexity - Hadoop is a low-level, Java-based framework that can be overly complex and difficult for end-users to work with. Hadoop architectures can also require significant expertise and resources to set up, maintain, and upgrade.Performance - Hadoop uses frequent reads and writes to disk to perform computations, which is time-consuming and inefficient compared to frameworks that aim to store and process data in memory as much as possible, like Apache Spark.Long-term viability - In 2019, the world saw a massive unraveling within the Hadoop sphere. Google, whose seminal 2004 paper on MapReduce underpinned the creation of Apache Hadoop, stopped using MapReduce altogether, as tweeted by Google SVP of Technical Infrastructure, Urs Hölzle. There were also some very high-profile mergers and acquisitions in the world of Hadoop. Furthermore, in 2020, a leading Hadoop provider shifted its product set away from being Hadoop-centric, as Hadoop is now thought of as “more of a philosophy than a technology.” Lastly, 2021 has been a year of interesting changes. In April 2021, the Apache Software Foundation announced the retirement of ten projects from the Hadoop ecosystem. Then in June 2021, Cloudera agrees to private. The impact of this decision on Hadoop users is still to be seen. This growing collection of concerns paired with the accelerated need to digitize has encouraged many companies to re-evaluate their relationship with Hadoop.Which companies use Hadoop?Hadoop adoption is becoming the standard for successful multinational companies and enterprises. The following is a list of companies that utilize Hadoop today:Adobe - the software and service providers use Apache Hadoop and HBase for data storage and other services.eBay - uses the framework for search engine optimization and research.A9 - a subsidiary of Amazon that is responsible for technologies related to search engines and search-related advertising.LinkedIn - as one of the most popular social and professional networking sites, the company uses many Apache modules including Hadoop, Hive, Kafka, Avro, and DataFu.Spotify - the Swedish music streaming giant used the Hadoop framework for analytics and reporting as well content generation and listening recommendations.Facebook - the social media giant maintains the largest Hadoop cluster in the world, with a dataset that grows a reported half of a PB per day.InMobi - the mobile marketing platform utilizes HDFS and Apache Pig/MRUnit tasks involving analytics, data science and machine learning.How much does Hadoop cost?The Hadoop framework itself is an open-source Java-based application. This means, unlike other big data alternatives, it’s free of charge. Of course, the cost of the required commodity software depends on what scale.When it comes to services that implement Hadoop frameworks you will have several pricing options:Per Node- most commonPer TBFreemium product with or without subscription-only tech supportAll-in-one package deal including all hardware and softwareCloud-based service with its own broken down pricing options- can essentially pay for what you need or pay as you goRead more about challenges with Hadoop, and the shift toward modern data platforms, in our blog post.Additional ResourcesStep-by-Step Migration: Hadoop to DatabricksMigration hubHidden Value of Hadoop Migration whitepaperIt’s Time to Re-evaluate Your Relationship with Hadoop (Blog)Delta Lake and ETLMaking Apache Spark™ Better with Delta LakeBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/dataaisummit/speaker/luk-verhelst | Luk Verhelst - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingLuk VerhelstData Architect at Volvo Group (consultant)Back to speakersOccupation:
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https://www.databricks.com/dataaisummit/speaker/blaise-sandwidi | Blaise Sandwidi - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingBlaise SandwidiLead Data Scientist, As. ESG Officer, PhD at International Finance Corporation (IFC)–World Bank GroupBack to speakersBlaise Sandwidi is a Lead Data Scientist with IFC’s ESG Global Advisory team. Blaise oversees the development of data science to support ESG risk modeling and data science for development. His past work experience includes positions with private sector institutions focused on building machine learning platforms to enable better investment decisions. Blaise holds a Ph.D. and a master’s degree in finance from the University of Paris-Est, France.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
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Improving Health Outcomes With Data and AI
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https://www.databricks.com/dataaisummit/speaker/holly-smith | Holly Smith - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingHolly SmithSenior Resident Solutions Architect at DatabricksBack to speakersHolly Smith is a renowned speaker and multi award winning Data & AI expert who has over a decade of experience working with Data & AI teams in a variety of capacities from individual contributors all the way up to leadership. She has spent the last four years at Databricks working with multi national companies as they embark on their journey to the cutting edge of data.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/fr/product/aws?itm_data=menu-item-awsProduct | Databricks sur la plateforme de données AWS - DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT
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DémarrerPlanifier une démoDatabricks sur AWS vous permet de stocker et de gérer toutes vos données sur une plateforme lakehouse simple et ouverte qui combine le meilleur des data warehouses et des data lakes pour unifier toutes vos charges de travail d'analytique et d'IA.Data engineering fiableSQL Analytics sur toutes vos données →Data Science collaborativeMachine learning en productionPourquoi Databricks sur AWS ?Simple
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https://www.databricks.com/dataaisummit/speaker/lindsey-woodland/# | Lindsey Woodland - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingLindsey WoodlandExecutive Vice President, Client Data Science at 605Back to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/tathagata-das/# | Tathagata Das - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingTathagata Das DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/company/partners/consulting-and-si/partner-solutions/capgemini-migrate-legacy-cards-and-core-banking-portfolios | Migrate Legacy Cards and Core Banking Portfolios by Capgemini and Databricks | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWBrickbuilder SolutionMigrate Legacy Cards and Core Banking Portfolios by CapgeminiMigration solution developed by Capgemini and powered by the Databricks Lakehouse Platform
Get startedReduce migration efforts by up to 50%The ability to migrate monolithic mainframe systems and integrate them into modern tech stacks on cloud is critical for retail banks in today’s competitive market. Organizations require real-time processing, high-quality data, elastic autoscaling, and support for a multiprogramming environment to succeed with complex cards and banking portfolio conversions. This is where Capgemini’s solution for migrating legacy cards and core banking portfolios on the Databricks Lakehouse Platform can offer a distinct advantage — by enabling rapid conversion from external source systems and providing a fully configurable and industrialized conversion capability. Leveraging public cloud services, this solution provides a cost-efficient conversion platform with predictable time-to-market capabilities, allowing you to:Rapidly complete ingestion and ease development of ETL jobs in order to meet conversion SLAs Reduce time to market for handling different file structures and char encoding by following a low-code/no-code framework designCompletely reconcile and validate the loads / EBCDIC-to-ASCII conversion at record speedGet startedDeliver AI innovation faster with solution accelerators for popular industry use cases. See our full library of solutions ➞ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/de/product/aws?itm_data=menu-item-awsProduct | Databricks auf der AWS-Datenplattform – DatabricksSkip to main contentPlattformDie Lakehouse-Plattform von DatabricksDelta LakeData GovernanceData EngineeringDatenstreamingData-WarehousingGemeinsame DatennutzungMachine LearningData SciencePreiseMarketplaceOpen source techSecurity & Trust CenterWEBINAR 18. Mai / 8 Uhr PT
Auf Wiedersehen, Data Warehouse. Hallo, Lakehouse.
Nehmen Sie teil, um zu verstehen, wie ein Data Lakehouse in Ihren modernen Datenstapel passt.
Melden Sie sich jetzt anLösungenLösungen nach BrancheFinanzdienstleistungenGesundheitswesen und BiowissenschaftenFertigungKommunikation, Medien und UnterhaltungÖffentlicher SektorEinzelhandelAlle Branchen anzeigenLösungen nach AnwendungsfallSolution AcceleratorsProfessionelle ServicesDigital-Native-UnternehmenMigration der Datenplattform9. Mai | 8 Uhr PT
Entdecken Sie das Lakehouse für die Fertigung
Erfahren Sie, wie Corning wichtige Entscheidungen trifft, die manuelle Inspektionen minimieren, die Versandkosten senken und die Kundenzufriedenheit erhöhen.Registrieren Sie sich noch heuteLernenDokumentationWEITERBILDUNG & ZERTIFIZIERUNGDemosRessourcenOnline-CommunityUniversity AllianceVeranstaltungenData + AI SummitBlogLabsBaken26.–29. Juni 2023
Nehmen Sie persönlich teil oder schalten Sie für den Livestream der Keynote einJetzt registrierenKundenPartnerCloud-PartnerAWSAzureGoogle CloudPartner ConnectTechnologie- und DatenpartnerTechnologiepartnerprogrammDatenpartner-ProgrammBuilt on Databricks Partner ProgramConsulting- und SI-PartnerC&SI-PartnerprogrammLösungen von PartnernVernetzen Sie sich mit validierten Partnerlösungen mit nur wenigen Klicks.Mehr InformationenUnternehmenKarriere bei DatabricksUnser TeamVorstandUnternehmensblogPresseAktuelle Unternehmungen von DatabricksAuszeichnungen und AnerkennungenKontaktErfahren Sie, warum Gartner Databricks zum zweiten Mal in Folge als Leader benannt hatBericht abrufenDatabricks testenDemos ansehenKontaktLoginJUNE 26-29REGISTER NOWDatabricks auf AWSDie einfache einheitliche Datenplattform, die nahtlos in AWS integriert ist
Erste SchrittePlanen Sie eine DemoMit Databricks auf AWS können Sie alle Ihre Daten auf einer einfachen, offenen Lakehouse-Plattform speichern und verwalten, die das Beste aus Data Warehouses und Data Lakes kombiniert, um alle Ihre Analytics- und KI-Workloads zusammenzuführen.Zuverlässiges Data EngineeringSQL Analytics
für alle Ihre DatenKollaborative Data ScienceMachine Learning in der ProduktionWarum Databricks auf AWS?Einfach
Databricks ermöglicht eine einheitliche Datenarchitektur auf S3 für SQL-Analytics, Data Science und Machine Learning.12x besseres Preis/Leistungsverhältnis
Erzielen Sie durch SQL-optimierte Compute-Cluster eine Data-Warehouse-Leistung, die der Wirtschaftlichkeit des Data Lake entspricht.Bewährt
Tausende Kunden haben Databricks in AWS implementiert, um eine bahnbrechende Analytics-Plattform bereitzustellen, die alle Analytics- und KI-Anwendungsfälle abdeckt.Dollar Shave Club: Personalisierung von Kundenerlebnissen mit Databricks
E-Book herunterladenHotels.com: Optimierung des Kundenerlebnisses durch Machine Learning
Fallstudie herunterladenHP: Von der Datenaufbereitung bis zum Deep Learning – wie HP Analytics mit Databricks vereinheitlicht
On-Demand-Webinar ansehenAusgewählte IntegrationenAWS GravitonAWS GravitonDatabricks-Cluster unterstützen AWS Graviton-Instances. Diese Instances verwenden von AWS entwickelte Graviton-Prozessoren, die auf die Arm64-Befehlssatzarchitektur aufsetzen. Nach Angaben von AWS weisen Instance-Typen mit diesen Prozessoren das beste Preis-Leistungs-Verhältnis aller Instance-Typen bei Amazon EC2 auf. Mehr erfahrenAWS-SicherheitAmazon RedshiftAWS GlueEinführung auf UnternehmensebeneAnwendungsfälleEngines für personalisierte Empfehlungen
Verarbeiten Sie alle Ihre Daten in Echtzeit, um die relevantesten Produkt- und Serviceempfehlungen zu geben.Genomische Sequenzierung
Modernisieren Sie Ihre Technologie und verbessern Sie das Erlebnis für Patienten und Ärzte mit der schnellsten DNASeq-Pipeline in großem Umfang.Betrugserkennung und
-prävention
Nutzen Sie vollständige historische Daten zusammen mit Echtzeit-Datenströmen, um schnell anomale und verdächtige Finanztransaktionen zu identifizieren.RessourcenWhitepaperWie Sie aus Ihrem Data Lake durch Analyse Erkenntnisse gewinnenZusammenführen von Big Data und KI in der FinanzdienstleistungsbrancheDer verborgene Mehrwert der Hadoop-MigrationWebinareDaten- und Analytics-Plattformen mit Databricks und AWS souverän modernisierenWarum datenorientierte Startups im Lakehouse entwickelnNutzung der Vorteile von Echtzeit-Analytics für eine schnelle Entscheidungsfindung bei QubyLoyaltyOne vereinfacht und skaliert Data Analytics-Pipelines mit Delta LakeDas Potenzial in Ihrem Data Lake erschließenAufbau eines Data Lakehouse bei DoorDash und GrammarlyBranchenNutzung von KI/ML zur Gewinnung von Erkenntnissen aus der Praxis aus klinischen Labordaten auf Populationsbasis bei PrognosWie Machine Learning die Data Analytics bei den Behörden verändertMöchten Sie loslegen?DATABRICKS KOSTENLOS TESTENProduktPlatform OverviewPreiseOpen Source TechDatabricks testenDemoProduktPlatform OverviewPreiseOpen Source TechDatabricks testenDemoLearn & SupportDokumentationGlossaryWEITERBILDUNG & ZERTIFIZIERUNGHelp CenterLegalOnline-CommunityLearn & SupportDokumentationGlossaryWEITERBILDUNG & ZERTIFIZIERUNGHelp CenterLegalOnline-CommunityLösungenBy IndustriesProfessionelle ServicesLösungenBy IndustriesProfessionelle ServicesUnternehmenÜber unsKarriere bei DatabricksDiversität und InklusionUnternehmensblogKontaktUnternehmenÜber unsKarriere bei DatabricksDiversität und InklusionUnternehmensblogKontaktWeitere Informationen unter
„Karriere bei DatabricksWeltweitEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/jp/product/data-science | データサイエンス | DatabricksSkip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ＆トラストセンターウェビナー 5 月 18 日午前 8 時 PT
さようなら、データウェアハウス。こんにちは、レイクハウス。
データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。
今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)
製造業のためのレイクハウスを発見する
コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日～29日
直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOWデータサイエンスデータサイエンスの大規模な連携無料トライアルデモをリクエストDatabricksのデータ サイエンスの詳細Databricks NotebooksIDE IntegrationsRepos機械学習オープンなレイクハウス基盤に構築されたコラボレーション型の統合データサイエンス環境により、データの準備、モデリング、知見の共有まで、エンドツーエンドのシームレスなデータサイエンスワークフローを実現。クリーンで信頼性の高いデータへの迅速なアクセス、事前構成されたコンピューティングリソース、IDE 統合、多言語対応の機能など、データサイエンスチームに最大限の柔軟性を提供します。クリーンで信頼性の高いデータへの迅速なアクセス、事前構成されたクラスタ、多言語対応の機能など、データサイエンスチームに最大限の柔軟性を提供します。データサイエンスワークフロー全体におけるコラボレーションDatabricks の Notebook では、Python、R、Scala、SQL などの言語を使用し、インタラクティブな視覚化によるデータ探索が可能で、新たな知見を発見できます。また、共同編集、コメント作成、自動バージョニング、Git の統合、ロールベースのアクセス制御により、高い信頼性でのセキュアなコード共有が可能です。インフラ管理からの解放ノート PC のデータ許容量やコンピューティング利用枠の制限など、インフラに関する懸念は不要になり、データサイエンスに注力できます。Databricks のプラットフォームでは、ローカル環境からクラウドへの移行、Notebook の自動管理クラスタへの接続が容易で、分析のワークロードを柔軟にスケーリングできます。任意のローカル IDE でスケーラブルなコンピューティングIDE（統合開発環境）の選択肢はさまざまです。Databricks では、任意の IDE の接続が可能です。使い慣れた環境で、無制限のデータストレージとコンピューティングを利用できます。さらに、Databricks で直接使用できる RStudio や JupyterLab が、シームレスなエクスペリエンスを提供します。データサイエンスのためのデータ供給Delta Lake は、バッチ、ストリーミング、構造化、非構造化のあらゆるデータを単一システムに集約し、クリーニング、カタログ化します。これにより、組織全体が一元化されたデータストアを使用してデータを探索できるようになります。データ品質の自動チェック機能により、分析の要件に適合する高品質なデータを供給します。データの追加や変換に際しても、バージョニング機能により、コンプライアンス要件に対応します。データ探索のためのローコード、ビジュアルツールDatabricks Notebook 内の視覚化ツールをネイティブに使用して、データの準備、変換、分析を行い、さまざまな専門レベルのユーザーがデータを扱うことができます。データの変換と視覚化が完了したら、バックグラウンドで実行されるコードを生成できます。定型コードを作成する時間を節約できるため、価値の高い作業に時間を費やすことができます。新たな気づきの発見と共有分析をダイナミックダッシュボードに素早く反映し、分析結果を容易に共有、エクスポートできます。ダッシュボードは常に最新の状態で、インタラクティブなクエリの実行も可能です。ロールベースのアクセス制御で、セル、視覚化、Notebook を共有し、HTML や IPython ノートブックなどの複数のフォーマットでエクスポートできます。データブリックスソリューションへの移行Hadoop やエンタープライズ DWH などのレガシーシステムに関連するデータサイロ、パフォーマンス低下、高いコストにうんざりしていませんか？Databricks レイクハウスに移行することで、あらゆるデータ、分析、AI のユースケースに対応する最新のプラットフォームが実現します。データブリックスソリューションへの移行関連リソース
関連リソース一覧
データサイエンスや機械学習に関する eBook やビデオを探すには、リソースライブラリをご覧ください。
詳しく見るeBook とブログデータサイエンスのビッグブック大規模な共同のデータサイエンス技術モダンクラウドデータプラットフォームMLflow：オープンソースの機械学習プラットフォーム新しい Delta Sharing ソリューションの詳細ガートナー MQ DBMS & DSML 2 部門のリーダー移行ガイド：Hadoop から Databricks への移行ブログHadoop からレイクハウスへの移行：成功のための 5 つのステップオンラインイベントHadoop から Databricks への移行ガイド無料お試し・その他ご相談を承りますDatabricks 無料トライアル製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ＆インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ＆インクルージョンDatabricks ブログご相談・お問い合わせ 採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.プライバシー通知|利用規約|プライバシー設定|カリフォルニア州のプライバシー権利 |
https://www.databricks.com/de/blog | The Databricks BlogSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWLoading...ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/glossary/what-is-sparkr | SparkRPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSparkRAll>SparkRTry Databricks for freeGet StartedSparkR is a tool for running R on Spark. It follows the same principles as all of Spark’s other language bindings. To use SparkR, we simply import it into our environment and run our code. It’s all very similar to the Python API except that it follows R’s syntax instead of Python. For the most part, almost everything available in Python is available in SparkR.Additional ResourcesSparkR Overview DocumentationSparkR: Interactive R Programs at ScaleIntroducing Apache Spark 3.0: Now available in Databricks Runtime 7.0Back to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/company/careers/open-positions?department=security | Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/it/events | Eventi di Databricks | DatabricksSkip to main contentPiattaformaThe Databricks Lakehouse PlatformDelta LakeGovernance dei datiIngegneria dei datiStreaming di datiData warehouseCondivisione dei datiMachine LearningData SciencePrezziMarketplaceTecnologia open-sourceSecurity and Trust CenterWEBINAR 18 maggio / 8 AM PT
Addio, Data Warehouse. Ciao, Lakehouse.
Partecipa per capire come una data lakehouse si inserisce nel tuo stack di dati moderno.
Registrati oraSoluzioniSoluzioni per settoreServizi finanziariSanità e bioscienzeIndustria manifatturieraComunicazioni, media e intrattenimentoSettore pubblicoretailVedi tutti i settoriSoluzioni per tipo di applicazioneAcceleratoriServizi professionaliAziende native digitaliMigrazione della piattaforma di dati9 maggio | 8am PT
Scopri la Lakehouse for Manufacturing
Scopri come Corning sta prendendo decisioni critiche che riducono al minimo le ispezioni manuali, riducono i costi di spedizione e aumentano la soddisfazione dei clienti.Registrati oggi stessoFormazioneDocumentazioneFormazione e certificazioneDemoRisorseCommunity onlineUniversity AllianceEventiConvegno Dati + AIBlogLabsBeacons
26–29 giugno 2023
Partecipa di persona o sintonizzati per il live streaming del keynoteRegistratiClientiPartnerPartner cloudAWSAzureGoogle CloudPartner ConnectPartner per tecnologie e gestione dei datiProgramma Partner TecnologiciProgramma Data PartnerBuilt on Databricks Partner ProgramPartner di consulenza e SIProgramma partner consulenti e integratori (C&SI)Soluzioni dei partnerConnettiti con soluzioni validate dei nostri partner in pochi clic.RegistratiChi siamoLavorare in DatabricksIl nostro teamConsiglio direttivoBlog aziendaleSala stampaDatabricks VenturesPremi e riconoscimentiContattiScopri perché Gartner ha nominato Databricks fra le aziende leader per il secondo anno consecutivoRichiedi il reportProva DatabricksGuarda le demoContattiAccediJUNE 26-29REGISTER NOWEventi di DatabricksScopri i prossimi meetup, webinar, convegni e altri appuntamenti di DatabricksData + AI Summit 202326-29 giugnoScegli come vivere la tua esperienza: partecipa in prima persona o segui gli interventi e le sessioni di tuo interesse da remotoRegistratiLoading...Browse All Upcoming EventsProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineSoluzioniPer settoreServizi professionaliSoluzioniPer settoreServizi professionaliChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiPosizioni aperte
in DatabricksMondoEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Informativa sulla privacy|Condizioni d'uso|Le vostre scelte sulla privacy|I vostri diritti di privacy in California |
https://www.databricks.com/customers/hsbc | Customer Story: HSBC | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCUSTOMER STORYReinventing mobile banking with ML6Seconds to perform complex analytics compared to 6 hours1Delta Lake has replaced 14 databases4.5xImprovement in engagement on the app INDUSTRY: Financial services SOLUTION: Anomaly detection,customer segmentation,fraud detection,recommendation engines,transaction enrichment PLATFORM USE CASE: Delta Lake,data science,machine learning,ETL CLOUD: Azure“We’ve seen major improvements in the speed we have data available for analysis. We have a number of jobs that used to take 6 hours and now take only 6 seconds.”
– Alessio Basso, Chief Architect, HSBCAs one of the largest international banks, HSBC is ushering in a new way to manage digital payments across mobile devices. They developed PayMe, a social app that facilitates cashless transactions between consumers and their networks instantly and securely. With over 39 million customers, HSBC struggled to overcome scalability limitations that blocked them from making data-driven decisions. With Databricks, they are able to scale data analytics and machine learning to feed customer-centric use cases including personalization, recommendations, network science, and fraud detection.Data science and engineering struggled to leverage dataHSBC understands the massive opportunity for them to better serve their 39+ million customers through data and analytics. Seeing an opportunity to reinvent mobile payments, they developed the PayMe, a social payments app. Since its launch in their home market of Hong Kong, they have become the #1 app in the region amassing 1.8+ million users.In an effort to provide their fast growing customer base the best possible mobile payments experience, they looked to data and machine learning to enable various desired use cases such as detecting fraudulent activity, customer 360 to inform marketing decisions, personalization, and more. However, building models that could deliver on these use cases in a secure, fast and scalable manner was easier said than done.Slow data pipelines resulted in old data: Legacy systems hampered their ability to process and analyze data at scale. They were required to manually export and sample data, which was time consuming. This resulted in the data being weeks old upon delivery to the data science team which blocked their ability to be predictive.Manual data exporting and masking: Legacy processes required a manual approval form to be filled out for every data request which was error-prone. Furthermore, the manual masking process was time consuming and did not adhere to strict data quality and protection rules.Inefficient data science: Data scientists worked in silos on their own machines and custom environments, limiting their ability to explore raw data and train models at scale. As a result, collaboration was poor and iteration on models were very slow.Data analysts struggled to leverage data: Needing access to subsets of structured data for business intelligence and reporting.Faster and more secure analytics and ML at scaleThrough the use of NLP and machine learning, HSBC is able to quickly understand the intent behind each transaction within their PayMe app. This wide range of information is then used to inform various use cases from recommendations to customers to reducing anomalous activity.With Azure Databricks, they are able to unify data analytics across data engineering, data science, and analysts.Improved operational efficiency: features such as auto-scaling clusters and support for Delta Lake has improved operations from data ingest to managing the entire machine learning lifecycle.Real time data masking with delta lake: With Databricks and Delta Lake, HSBC was able to securely provide anonymized production data in real-time to data science and data analyst teams.Performant and scalable data pipelines with Delta Lake: This has enabled them to perform real-time data processing for downstream analytics and machine learning.Collaboration across data science and engineering: Enables faster data discovery, iterative feature engineering, and rapid model development and training.Richer insights leads to the #1 appDatabricks provides HSBC with a unified data analytics platform that centralizes all aspects of their analytics process from data engineering to the productionization of ML models that deliver richer business insights.Faster data pipelines: Automating processes and increased data processing from 6 hours to 6 seconds for complex analytics.Descriptive to predictive: Ability to train models against their entire dataset, has empowered them to deploy predictive models to feed various use cases.From 14 databases to 1 Delta Lake: Moved from 14 read replica databases to a single unified data store with Delta Lake.PayMe is #1 app in Hong Kong: 60% market share of the Hong Kong market making PayMe the #1 app.Improved consumer engagement: Ability to leverage network science to understand customer connections has resulted in a 4.5x improvement in engagement levels with the PayMe app.Related ContentArticleWIRED Brand Lab | When it comes to security, data is the best defenseSessionTechnical Talk at Spark + AI Summit EU 2019Ready to get started?Try Databricks for freeLearn more about our productTalk to an expertProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/company/careers/open-positions?department=administration | Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/dataaisummit/speaker/naveen-zutshi/# | Naveen Zutshi - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingNaveen ZutshiChief Information Officer at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/fr/product/google-cloud?itm_data=menu-item-gcpProduct | Databricks Google Cloud Platform (GCP) | DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT
Au revoir, entrepôt de données. Bonjour, Lakehouse.
Assistez pour comprendre comment un data lakehouse s’intègre dans votre pile de données moderne.
Inscrivez-vous maintenantSolutionsSolutions par secteurServices financiersSanté et sciences du vivantProduction industrielleCommunications, médias et divertissementSecteur publicVente au détailDécouvrez tous les secteurs d'activitéSolutions par cas d'utilisationSolution AcceleratorsServices professionnelsEntreprises digital-nativesMigration des plateformes de données9 mai | 8h PT
Découvrez le Lakehouse pour la fabrication
Découvrez comment Corning prend des décisions critiques qui minimisent les inspections manuelles, réduisent les coûts d’expédition et augmentent la satisfaction des clients.Inscrivez-vous dès aujourd’huiApprendreDocumentationFORMATION ET CERTIFICATIONDémosRessourcesCommunauté en ligneUniversity AllianceÉvénementsSommet Data + IABlogLabosBeacons26-29 juin 2023
Assistez en personne ou connectez-vous pour le livestream du keynoteS'inscrireClientsPartenairesPartenaires cloudAWSAzureGoogle CloudContact partenairesPartenaires technologiques et de donnéesProgramme partenaires technologiquesProgramme Partenaire de donnéesBuilt on Databricks Partner ProgramPartenaires consulting et ISProgramme Partenaire C&SISolutions partenairesConnectez-vous en quelques clics à des solutions partenaires validées.En savoir plusEntrepriseOffres d'emploi chez DatabricksNotre équipeConseil d'administrationBlog de l'entreprisePresseDatabricks VenturesPrix et distinctionsNous contacterDécouvrez pourquoi Gartner a désigné Databricks comme leader pour la deuxième année consécutiveObtenir le rapportEssayer DatabricksRegarder les démosNous contacterLoginJUNE 26-29REGISTER NOWDatabricks sur Google CloudLa plateforme lakehouse ouverte rejoint le cloud ouvert pour regrouper le data engineering, la data science et l'analytiqueDémarrerDatabricks on Google Cloud est un service développé conjointement qui vous permet de stocker toutes vos données sur une plateforme Lakehouse simple et ouverte combinant le meilleur des data warehouses et des data lakes pour unifier toutes vos charges de travail d'analytique et d'IA. L'intégration étroite avec Google Cloud Storage, BigQuery et Google Cloud AI Platform permet à Databricks de fonctionner en toute transparence sur les services de data et d'IA sur Google Cloud.
Data engineering fiableSQL Analytics sur toutes vos données →Data Science collaborativeMachine learning en production
Pourquoi Databricks sur Google Cloud ?OuvertConstruit sur des normes, des API et une infrastructure ouvertes et librement accessibles afin que vous puissiez accéder, traiter et analyser les données selon vos critères.OptimiséDéployez Databricks sur Google Kubernetes Engine, le premier moteur d'exécution de Databricks basé sur Kubernetes pour obtenir des résultats plus rapidement.IntégréAccédez en un clic à Databricks depuis la Google Cloud Console, avec sécurité, facturation et gestion intégrées.En savoir plus sur ces clients
« Databricks sur Google Cloud simplifie le processus de conduite de plusieurs cas d'usage sur une plateforme de calcul évolutive, réduisant ainsi les cycles de planification nécessaires pour fournir une solution à chaque question business ou problématique que nous utilisons. »—Harish Kumar, Global Data Science Director chez ReckittIntégration simplifiée avec Google CloudGoogle Cloud StorageGoogle Cloud StorageFacilitez un accès fluide en lecture / écriture pour les données dans Google Cloud Storage (GCS) et exploitez le format ouvert Delta Lake pour ajouter de grandes capacités de fiabilité et de performance au sein de Databricks.Google Kubernetes EngineBigQueryIdentité du grand nuageGoogle Cloud AI PlatformGoogle Cloud BillingLookerÉcosystème des partenairesRessourcesÉvènements virtuelsAtelier virtuel : le Data Lake ouvert →ActualitésDatabricks s'associe à Google Cloud pour proposer sa plateforme à des entreprises internationalesBlogs et rapportsAnnonce du lancement de Databricks sur Google CloudPrésentation de Databricks sur Google Cloud – Désormais dévoilée en avant-première au publicFiche technique Databricks sur Google CloudDatabricks sur Google Cloud disponible partout dès maintenantData engineering, data science et analytique avec Databricks sur Google CloudPrêt à vous lancer ?ESSAYER GRATUITEMENT DATABRICKSProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneSolutionsBy IndustriesServices professionnelsSolutionsBy IndustriesServices professionnelsEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterD écouvrez les offres d'emploi
chez Databrickspays/régionsEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Avis de confidentialité|Conditions d'utilisation|Vos choix de confidentialité|Vos droits de confidentialité en Californie |
https://www.databricks.com/fr/try-databricks?itm_data=NavBar-TryDatabricks-Trial | Essayer gratuitement Databricks | DatabricksEssayer gratuitement DatabricksExpérimentez pleinement la plateforme Databricks gratuitement pendant 14 jours, sur AWS, Microsoft Azure ou Google Cloud, au choix.Simplification de l'ingestion des données et automatisation de l’ETLImportez des données depuis des centaines de sources. Utilisez une approche déclarative simple pour créer des pipelines de données.Collaborez dans votre langage préféréCodez en Python, R, Scala et SQL. Bénéficiez du RBAC, d’intégrations avec Git et d’outils comme la rédaction collaborative et la gestion automatique des versions.Un rapport performance / prix jusqu'à 12 fois supérieur à celui des data warehousesDécouvrez pourquoi plus de 7 000 clients dans le monde s’appuient sur Databricks pour toutes leurs charges de travail de la BI à l’IA.Créez votre compte Databricks1/2PrénomNomE-mail professionnelEntrepriseIntitulé de posteNuméro de téléphone (facultatif)Veuillez sélectionnerPaysContinuerAvis de confidentialité (mis à jour)Conditions d'utilisationVos choix de confidentialitéVos droits de confidentialité en Californie |
https://www.databricks.com/learn/certification | Databricks Certification and Badging | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCommunity loginAcademy loginDatabricks Certification and BadgingThe new standard for lakehouse training and certificationsValidate your data and AI skills in the Databricks Lakehouse Platform by getting Databricks certified. Whether you are new to business intelligence or looking to confirm your skills as a machine learning or data engineering professional, Databricks can help you achieve your goals.Role Progression and CertificationsData AnalystData analysts transform data into insights by creating queries, data visualizations and dashboards using Databricks SQL and its capabilities.AssociateThe Databricks Certified Data Analyst Associate certification exam assesses an individual’s ability to use the Databricks SQL service to complete introductory data analysis tasks.
Learn moreData EngineerData engineers design, develop, test and maintain batch and streaming data pipelines using the Databricks Lakehouse Platform and its capabilities.AssociateThe Databricks Certified Data Engineer Associate certification exam assesses an individual’s ability to use the Databricks Lakehouse Platform to complete introductory data engineering tasks.
Learn moreProfessionalThe Databricks Certified Data Engineer Professional certification exam assesses an individual’s ability to use Databricks to perform advanced data engineering tasks.
Learn moreML Data ScientistMachine learning practitioners develop, deploy, test and maintain machine learning models and pipelines using Databricks Machine Learning and its capabilities.AssociateThe Databricks Certified Machine Learning Associate certification exam assesses an individual’s ability to use Databricks to perform basic machine learning tasks.
Learn moreProfessionalThe Databricks Certified Machine Learning Professional certification exam assesses an individual’s ability to use Databricks Machine Learning and its capabilities to perform advanced machine learning in production tasks.
Learn moreNext StepsSelect the certification that aligns to your roleRegister for the exam or sign up for the class to prepare for the examTake the exam and celebrate your success by posting on social mediaSpecialty BadgesAs you progress through your Lakehouse learning paths, you can earn specialty badges. Specialty badges represent an achievement in a focus area, such as a specific professional services offering or deployment on one of Databricks’ cloud vendors.Apache Spark Developer AssociateThe Databricks Certified Associate Developer for Apache Spark certification exam assesses the understanding of the Spark DataFrame API and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session.
Learn morePlatform AdministratorThis accreditation is the final assessment in the Databricks Platform Administrator specialty learning pathway.
Learn moreHadoop Migration ArchitectThe Databricks Certified Hadoop Migration Architect certification exam assesses an individual’s ability to architect migrations from Hadoop to the Databricks Lakehouse Platform.
Learn moreNext StepsSelect the specialty badge you are interested inLearn how the programs work and how to earn the specialty badgeResourcesAccess your earned Databricks credentialsCertification FAQProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/dataaisummit/speaker/himanshu-raja | Himanshu Raja - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingHimanshu Raja DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/yaniv-kunda | Yaniv Kunda - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingYaniv KundaSenior Software Architect at AkamaiBack to speakersYaniv Kunda is a Senior Software Architect at Akamai. With more than 25 years of experience in software engineering and with a particular interest in the infrastructural aspects of the systems he worked on, Yaniv has been focusing on Big Data for the past 4 years. He holds a BA in Computer Sciences from the Interdisciplinary Center Herzliya.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/br/discover/beacons | Beacons Hub Page | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks Beacons ProgramThe Databricks Beacons program is our way to thank and recognize the community members, data scientists, data engineers, developers and open source enthusiasts who go above and beyond to uplift the data and AI community.Whether they are speaking at conferences, leading workshops, teaching, mentoring, blogging, writing books, creating tutorials, offering support in forums or organizing meetups, they inspire others and encourage knowledge sharing – all while helping to solve tough data problems.Meet the Databricks BeaconsBeacons share their passion and technical expertise with audiences around the world. They are contributors to a variety of open source projects including Apache Spark™, Delta Lake, MLflow and others. Don’t hesitate to reach out to them on social to see what they’re working on.ISRAELAdi PolakAdi is a Senior Software Engineer and Developer Advocate in the Azure Engineering organization at Microsoft.FRANCEBartosz KoniecznyBartosz is a Data Engineering Consultant and an instructor.
UNITED STATESR. Tyler CroyTyler, the Director of Platform Engineering at Scribd, has been an open source developer for over 14 years.CHINAKent YaoKent is an Apache Spark™ committer and a staff software engineer at NetEase.IRELANDKyle HamiltonKyle is the Chief Innovation and Data Officer at iQ4, and a lecturer at the University of California, Berkeley.POLANDJacek LaskowskiJacek is an IT freelancer who specializes in Apache Spark™, Delta Lake and Apache Kafka.UNITED STATESScott HainesScott is a Distinguished Software Engineer at Nike where he helps drive Apache Spark™ adoption.UNITED KINGDOMSimon WhiteleySimon is the Director of Engineering at Advancing Analytics, is a Microsoft Data Platform MVP and Data + AI Summit speaker.UNITED STATESGeeta ChauhanGeeta leads AI/PyTorch Partnership Engineering at Facebook AI and focuses on strategic initiatives.SWITZERLANDLorenz WalthertLorenz Walthert is a data scientist, MLflow contributor, climate activist and a GSoC participant.CANADAYitao LiYitao is a software engineer at SafeGraph and the current maintainer of sparklyr, an R interface for Apache Spark™.POLANDMaciej SzymkiewiczMaciej is an Apache Spark™ committer. He is available for mentoring and consulting.JAPANTakeshi YamamuroTakeshi is a software engineer, Apache Spark™ committer and PMC member at NTT, Inc., who mainly works on Spark SQL.Membership CriteriaBeacons are first and foremost practitioners in the data and AI community whose technology focus includes MLflow, Delta Lake, Apache Spark™, Databricks and related ecosystem technologies. Beacons actively build others up throughout the year by teaching, blogging, speaking, mentoring, organizing meetups, creating content, answering questions on forums and more.Program BenefitsPeer networking and sharing through a private Slack channelAccess to Databricks and OSS subject matter expertsRecognition on the Databricks website and social channelsCustom swagIn the future, sponsored travel and lodging to attend select
Databricks eventsSponsorship and swag for meetupsNominate a peerWe’d love to hear from you! Tell us who made continued outstanding contributions to the data and AI community. Candidates must be nominated by someone in the community, and everyone — including customers, partners, Databricks employees or even a current Beacon — is welcome to submit a nomination. Applications will be reviewed on a rolling basis, and membership is valid for one year.NominateProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/dataaisummit/speaker/shasidhar-eranti/# | Shasidhar Eranti - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingShasidhar ErantiSpecialist Solutions Architect at DatabricksBack to speakersShasidhar is part of Specialist Solutions Architects team at Databricks. He is an expert in designing and building batch and streaming applications at scale using Apache Spark. At Databricks he works directly with customers to build. deploy and manage end-to-end spark pipelines in production, also help guide towards Spark best practices.
Shashidhar started his Spark journey back in 2014 in Bangalore, later he worked as an independent consultant for couple of years and joined Databricks in 2018.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/jp/glossary | 用語集アーカイブ｜Databricksサイト名Skip to main contentプラットフォームデータブリックスのレイクハウスプラットフォームDelta Lakeデータガバナンスデータエンジニアリングデータストリーミングデータウェアハウスデータ共有機械学習データサイエンス料金Marketplaceオープンソーステクノロジーセキュリティ＆トラストセンターウェビナー 5 月 18 日午前 8 時 PT
さようなら、データウェアハウス。こんにちは、レイクハウス。
データレイクハウスが最新のデータスタックにどのように適合するかを理解するために出席してください。
今すぐ登録ソリューション業種別のソリューション金融サービス医療・ライフサイエンス製造通信、メディア・エンターテイメント公共機関小売・消費財全ての業界を見るユースケース別ソリューションソリューションアクセラレータプロフェッショナルサービスデジタルネイティブビジネスデータプラットフォームの移行5月9日 |午前8時(太平洋標準時)
製造業のためのレイクハウスを発見する
コーニングが、手作業による検査を最小限に抑え、輸送コストを削減し、顧客満足度を高める重要な意思決定をどのように行っているかをご覧ください。今すぐ登録学習ドキュメントトレーニング・認定デモ関連リソースオンラインコミュニティ大学との連携イベントDATA+AI サミットブログラボBeacons2023年6月26日～29日
直接参加するか、基調講演のライブストリームに参加してくださいご登録導入事例パートナークラウドパートナーAWSAzureGoogle CloudPartner Connect技術・データパートナー技術パートナープログラムデータパートナープログラムBuilt on Databricks Partner ProgramSI コンサルティングパートナーC&SI パートナーパートナーソリューションDatabricks 認定のパートナーソリューションをご利用いただけます。詳しく見る会社情報採用情報経営陣取締役会Databricks ブログニュースルームDatabricks Ventures受賞歴と業界評価ご相談・お問い合わせDatabricks は、ガートナーのマジック・クアドラントで 2 年連続でリーダーに位置付けられています。レポートをダウンロードDatabricks 無料トライアルデモを見るご相談・お問い合わせログインJUNE 26-29REGISTER NOWGlossaryA-Zsearch ゲノミクス ゲノミクスとは、生物のゲノムのシーケンシングと分析に関する遺伝学の一分野です。その主な役割は、DNA のシーケンス全体、または DNA を構成する原子の組成、および DNA 原子間の化学結合を決定することです。ゲノミクスの分野は、全体構造としてのゲノムに重点を置いており、生物の完全な遺伝物質の研究として定義することができます。DNA は 1869 年に初めて単離されましたが、ゲノミクスは、科学者が単純な生物の DNA シーケンスを決定した 1970 年代に始まったばかりです。ゲノミクスの分野で最{...} デジタルツイン デジタルツインとは
IBM によると、デジタルツインの従来の定義は、「物理オブジェクトを正確に反映するように設計された仮想モデル」です。デジタルツインは、離散的または連続的な製造プロセスにおいて、さまざまな IoT センサー（OT：運用技術データ）やエンタープライズデータ（IT：情報技術）を用いてシステムやプロセスの状態データを収集し、仮想モデルを形成します。このモデルは、シミュレーションの実行、性能の問題の調査、知見の抽出に使用できます。
デジタルツインの概念は、特に新しいものでは{...} データセット Datasetとは、Java および Scala 用のタイプセーフなSparkの構造化APIです。Python および R は動的型付け言語であるため、この API の使用はできませんが、Scala や Java で大規模なアプリケーションを作成するためには強力なツールです。DataFrame は、Row 型のオブジェクトの分散型コレクションであり、さまざまなタイプの表形式データを保持できます。Dataset API を使用すると、データフレーム内のレコードに Java クラスを割り当て、Jav{...} データボルト データボルトとは
Data Vault（データボルト）とは、データモデリングのデザインパターンで、エンタープライズ規模の分析向けのデータウェアハウスを構築する際に使用されます。データボルトには、ハブ、リンク、サテライトの 3 種類のエンティティがあります。
ハブは、ビジネスの中核となるコンセプトを、リンクは、ハブ間のリレーションシップを表します。サテライトは、ハブに属する情報やハブ間のリレーションシップに関するデータを格納します。
データボルトは、レイクハウスのパラダイムを採用{...} データマート データマートとは
データマートは、テーブルのセットを含むキュレートされたデータベースです。単一のデータチームやコミュニティ、マーケティングやエンジニアリング部門といった基幹業務の特定のニーズに対応できるよう設計されています。データマートは通常、データウェアハウスよりも小規模で、特定の目的に特化しています。一般的には、大規模なエンタープライズのデータウェアハウスのサブセットとして扱われ、分析や BI（ビジネスインテリジェンス）、レポーティングに使用されます。データマートは、中央データウェアハ{...} データ共有 データ共有とは
データ共有とは、同じデータを複数のユーザーで利用できるようにすることです。増加し続けるデータは、あらゆる企業にとって重要な戦略的資産です。組織内外におけるデータ共有は、新たなビジネスチャンスを生み出すカギとなる技術です。外部データを利用するだけでなく、データを共有することで、パートナーとのコラボレーション、新たなパートナーシップの確立、データのマネタイズによる新たな収益源の確保が可能になります。
従来のデータ共有ソリューション
SFTP（SSH File Tra{...} メダリオンアーキテクチャ メダリオンアーキテクチャとは
メダリオンアーキテクチャとは、レイクハウスのデータを論理的に整理するために用いられるデータ設計を意味します。データがアーキテクチャの 3 つのレイヤー（ブロンズ → シルバー → ゴールドのテーブル）を流れる際に、データの構造と品質を増分的かつ漸次的に向上させることを目的としてい ます。メダリオンアーキテクチャは、「マルチホップ」アーキテクチャとも呼ばれます。
レイクハウスアーキテクチャのメリット
シンプルなデータモ{...} 金融サービスのパーソナライズ 金融サービスのパーソナライズとは
金融商品やサービスのコモディティ化が進み、メディアや小売業界がパーソナライズされた体験を好むようになったことで、消費者の目は肥えてきています。消費者から求められるものが日々変化していく中で、銀行がこれからも必要とされ続けるためには、パーソナライズされた知見やレコメンド、財務目標の設定、レポート機能といった従来の銀行業務を超えた魅力的な銀行体験を提供する必要があり、これらは全て地理空間や自然言語処理（NLP）などの高度な分析機能によって実現されます。金融サー{...}ACID トランザクション トランザクション とは
データベースやデータストレージシステムにおけるトランザクションとは、1 つの作業単位として扱われるあらゆる操作のことです。トランザクションは、完全に実行される、もしくは全く実行されないかのいずれかで、ストレージシステムを一貫した状態に保ちます。トランザクションの典型的な例として、銀行の預金口座から現金を引き出す処理が挙げられます。この場合、預金口座から現金を引き出したか、もしくは全く引き出さなかったか、どちらかの処理が発生し、中間の状態はありません。
ACID {...}AdaGrad 機械学習や深層学習における最適化のための最も一般的なアルゴリズムの 1 つに、勾配降下法があります。勾配降下法は機械学習モデルのトレーニングに使用されます。
勾配降下法の種類
現在、機械学習や深層学習のアルゴリズムに使用されている勾配降下法は、主に3種類あります。
バッチ勾配降下法
3 種類の勾配降下法の中で、バッチ勾配降下法は一番容易な手法です。トレーニングデータセットの各データの誤差を計算しますが、トレーニングデータの計算が全て終了するまでモデル{...}Apache Hive Apache Hive とは
Apache Hive は、Apache Hadoop 分散ファイルシステム (HDFS) から抽出された大規模なデータセットの読み取り、書き込み、および管理を行うために設計されたオープンソースのデータウェアハウスソフトウェアで、より規模の大きい Hadoop エコシステムの側面も持ち合わせています。
Apache Hiveの豊富なドキュメントと継続的なアップデートにより、Apache Hiveはアクセスしやすい方法{...}Apache Kudu Apache Kudu とは
Apache Kudu とは、Apache Hadoop 向けに開発された無料のオープンソースの列指向ストレージシステムです。構造化データ用エンジンで、各行への低遅延でランダムなミリ秒スケールのアクセスに加えて、優れたアクセスパターン分析もサポートします。広く普及している Hadoop 分散ファイルシステム（HDFS）と NoSQL データベースの HBase 間をつなぐために作成されたビッグデータエンジンです。
Hadoo{...}Apache Kylin Apache Kylin とは
Apache Kylin とは、ビッグデータの対話型分析のための分散型オープンソースのオンライン分析処理（OLAP）エンジンです。Apache Kylin は Hadoop や Spark でSQL インターフェイスと多次元分析（OLAP）を提供するよう設計されています。さらに、ODBC ドライバ、JDBC ドライバ、REST API を介して BI ツールと容易に統合します。2014年に eBay が構築した Apache Kylin {...}Apache Spark Apache Spark とは
Apache Spark は、ビッグデータのワークロードに使用するオープンソースの分析エンジンです。リアルタイム分析とデータ処理のワークロードに加えて、両方のバッチ処理が可能です。Apache Spark は 2009 年にカリフォルニア大学バークレー校の研究プロジェクトとして開発されました。それまで研究者は、Hadoop システムでのジョブ処理を高速化する方法を模索していました。Apache Spark は Hadoop MapRedu{...}Catalystオプティマイザ Catalyst オプティマイザとは、Spark SQL で主要な役割を果たす最適化機能です。Scala のパターンマッチングや準クォートなどの高度なプログラミング言語の機能を斬新な方法で利用し、拡張可能なクエリオプティマイザを構築します。Catalyst は Scala の関数型プログラミング構造に基づいており、次の 2 つの主要な目的を想定して設計されています。
Spark SQLへの新しい最適化技術と機能の追加を容易にする
外部の開発者でもオプティマイザの拡張を実行できるよ{...}Convolutional Layer：畳み込み層 深層学習において、畳み込みニューラルネットワーク（CNN または ConvNet）はディープニューラルネットワークの１つの手法です。画像内のパターン認識に通常使用されますが、空間データ分析、コンピュータビジョン、自然言語処理、信号処理などさまざまな用途に対する導入事例もあります。畳み込みネットワークのアーキテクチャは人間の脳内のニューロン結合パ ターンに類似し、視覚野の組織構造に着想を得ました。人工ニューラルネットワーク関連のこのタイプは、ネットワークの最も重要な操作の一つである「畳み込み」から名{...}Databricks Runtime Databricks ランタイムは、データブリックスが管理するマシンのクラスタ上で実行されるソフトウェアアーティファクトのセットです。Spark はもちろん、ビッグデータ分析の操作性やパフォーマンス、セキュリティなどを大幅に向上させるコンポーネントや更新プログラムも数多く追加されています。Databricks ランタイムが他のランタイムよりも優れている点は次のとおりです。
優れたパフォーマンス：Databricks I/Oモジュール（DBIO）は、垂直統合スタックを活用してクラウドで{...}DataFrames DataFrame とは
DataFrame の概念は、多くの言語やフレームワークで共通しています。DataFrame は、柔軟かつ 直感的にデータの保存や操作ができるため、最新のデータ分析で最も一般的に使用されるデータ構造の 1 つです。
DataFrame にはスキーマと呼ばれる青写真が含まれており、各列の名前とデータタイプが定義されています。Spark DataFrame には、文字列型や整数型などの汎用的なデータタイプと、構造型などの Spark 固有のデータタイプを{...}DNA シーケンス DNA シーケンスとは
DNA シーケンスとは、DNA（デオキシリボ核酸）のヌクレオチドの正確な配列を決定するプロセスです。塩基としても知られる4つの化学構成要素（アデニン、グアニン、シトシン、チミン）の順序のDNAシーケンシングは、DNA分子内で発生します。DNA シーケンシングの最初の手法は、1970年代半ばにフレッド・サンガー（Fred Sanger）、ウォルター・ギルバート（Walter Gilbert）、アラン・マクサム（Allan Maxam）によって開発されました。配列決定さ{...}ETL：抽出・変換・ロード
Delta Live Tables
Delta Live Tables（DLT）は、データパイプラインの構築と管理を容易にし、信頼性を向上させて Delta Lake に高品質データをもたらします。
Databricks ETL の
詳細
ETL とは
組織におけるデータ、データソースの増加、データタイプの多様化に伴い、分析、データサイエンス、機械学習に取り組み、データを活用してビジネスの気づきを引き出{...}Feature Engineering Feature engineering for machine learning
Feature engineering, also called data preprocessing, is the process of converting raw data into features that can be used to develop machine learning models. This topic describes the principal concepts of f{...}Hadoop Hadoop とは
「Hadoop」とは何を意味するのでしょうか。「Hadoop」とは何の略なのでしょうか？Hadoop は、High Availability Distributed Object Oriented Platform の略です。そして、これこそが Hadoop テ クノロジーが開発者に提供するものです。オブジェクト指向タスクの並列分散による高可用性を実現します。
Apache Hadoop とは、オープンソースの Java ベースのソフトウェアプラットフォームで、ビッ{...}Hadoop エコシステム Hadoop エコシステムとは
Apache Hadoop エコシステムとは、Apache Hadoop ソフトウェアライブラリのさまざまなコンポーネントを指します。オープンソースプロジェクトだけでなく、補足ツールの全てが含まれます。Hadoop エコシステムの最もよく知られているツールには、HDFS、Hive、Pig、YARN、MapReduce、Spark、HBase Oozie、Sqoop、Zookeeper、などがあります。開発者が頻繁に使用する主要な Hadoop エコ{...}Hadoop クラスタ Hadoop クラスタとは
Apache Hadoop とは、オープンソースの Java ベースのソフトウェアフレームワークで、並列データ処理エンジンです。アルゴリズム（MapReduce アルゴリズムなど）を使用してビッグデータ分析処理タスクを並列実行できる小さなタスクに分割し、Hadoop クラスタ全体に分散させることができます。Hadoop クラスタとは、ビッグデータセットに対してこのような並列計算を実行するためにネットワーク化された、ノードと呼ばれるコ{...}Hadoop 分散ファイルシステム（HDFS） HDFS
HDFS （ Hadoop 分散ファイルシステム）は、 Hadoop アプリケーションで使用される主要なストレージシステムです。このオープンソースのフレームワークは、ノード間のデータ転送を高速に行うことで動作します。ビッグデータを取り扱い、保存する必要のある企業でよく利用されています。HDFS は、ビッグデータを管理し、ビッグデータ解析をサポートする手段として、多くの Hadoop システムにおいて重要なコンポーネントとなっています。
HDFS を利用している企業は世界中に{...}Hive 日付関数 ハイブ日付関数とは
Hiveでは、データの処理や照会を行う際に役立つ多くの組み込み関数を提供しています。これらの関数が提供する機能には、文字列操作、日付操作、型変換、条件演算子、数学関数などがあります。
HIVE の組み込み関数の種類
日付関数
日付に日数を加算したり、他の類似の演算を追加するなど、日付データ型に対する操作を実行するために主に使用されます。
数学関数
主に数学的計算を実行するために使用されます。
条件関数
条件をテストするために使用{...}Jupyter Notebook Jupyter Notebook とは
Jupyter Notebook は、オープンソースで提供された Web アプリケーションであり、プログラムや数式、その他のマルチメディアリソースを含むドキュメントを作成・共有する目的で、主にデータサイエンティストに利用されています。
Jupyter Notebook の用途
Jupyter Notebook は、探索的データ解析（EDA）、データクレンジングとデータ変換、データ可視化、統計モデリング、機械学習、深層学習{...}Keras モデル Keras モデルとは？
Keras とは、Theano と Tensorflow 上に構築された深層学習のためのハイレベルのライブラリです。Keras は、Python で記述され、深層学習モデルの範囲を作成するためのクリーンで便利な方法を提供します。Keras は、ニューラルネットワークの開発とテストに関して最も使用されている高レベルのニューラルネットワーク API の 1 つです。現在では、ニューラルネットワークのレイヤーの作成や複雑なアーキテクチャの設定が、Ke{...}MapReduce MapReduce とは
MapReduce は、Apache Hadoop エコシステムの一部であり、Java ベースの分散実行フレームワークです。開発者が実装する Map 処理と Reduce 処理の 2 つの処理ステップを公開することで、分散プログラミングの複雑さを解消します。Map 処理では、データは並列処理するタスク間で分割されます。データの各チャンクには、変換ロジックを適用できます。Map 処理が完了すると Reduce 処理が行われ、Map 処理で分割されたデータの集約を実行{...}MLOps MLOps とは
MLOps は、Machine Learning Operations（機械学習オペレーション）の略語です。機械学習エンジニアリングの中核となる MLOps は、機械学習モデルを本番環境に移行し、維持・監視のプロセスを効率化することに重点を置いています。MLOps は、多くの場合、データサイエンティスト、DevOps エンジニア、IT 部門で構成されるチーム間のコラボレーションを担います。
MLOps の活用法
MLOps は、機械学習や AI ソリュ{...}pandas DataFrame
データサイエンスに関していうと、 pandas DataFrame を使いこなすことで、ビジネスのあり方そのものを変革できるといっても過言ではありません。ただし、そのためには適切なデータ構造が必要です。これらを上手く活用することで、データの操作や分析を最大限効率的にできるようになります。
この目的のために使える最も便利なデータストラクチャの1つが pandas DataFrame です。
pandas とは、プログラミング言語 Python でデータ分析を行うためのオープンソ{...}Parquet Parquet とは
Apache Parquet は、効率的なデータの保存と検索のために設計された、オープンソースの列指向データファイル形式です。複雑なデータを一括処理するための効率的なデータ圧縮と符号化方式を提供し、パフォーマンスを向上させます。Apache Parquet は、バッチとインタラクティブの両方のワークロードで共通の交換形式となるように設計されており、Hadoop で利用可能な他の列指向ストレージファイル形式である RCFile や ORC に似ています。
{...}PyCharm PyCharm とは、コンピュータプログラミングで使用される統合開発環境（IDE）です。プログラミング言語 Python 用に作成されています。PyCharm をデータブリックスで使用する場合、デフォルトでは PyCharm は Python の仮想環境を作成しますが、Conda 環境の作成や既存環境の使用設定が可能です。
{...}PySpark PySpark とは
Apache Spark は、プログラミング言語 Scala で記述されています。PySpark とは、Spark を実行するための Python API です。Apache Spark とPython のコラボレーションをサポートするためにリリースされました。PySpark は、Apache Spark とプログラミング言語 Python での Resilient Distributed Dataset（RDD）とのインターフェイスもサポートしており、これは Py4{...}Spark API Sparkには、DataFrame、Dataset、RDDの3つのAPIがあります。
レジリエントな分散データセット(RDD)とは
レジリエントな分散データセット(RDD)は、分散コンピューティングを用いたレコードコレクションです。フォールトトレラントで不変な性質を有しています。RDDは、低レベルAPIとの並列操作が可能で、遅延機能によりSparkの操作を迅速化します。また、RDDは2つの操作をサポートしています。
トランスフォーメーション —別の RDD を返す遅延操作{...}Spark Elasticsearch Spark Elasticsearch とは
Spark Elasticsearch とは、ドキュメント指向および半構造化データを格納、取得、管理する NoSQL 分散データベースです。GitHub オープンソースである Elasticsearch は、Apache Lucene をベースに構築され、Apache ライセンスの条件下でリリースされた RESTful な検索エンジンでもあります。
Elasticsearch は Java ベースであるため、さまざまな形式のドキュメントフ{...}Spark SQL 多くのデータサイエンティスト、アナリスト、一般的な BI ユーザーは、データの解析に対話型の SQL クエリに活用しています。Spark SQL とは、構造化データ処理のためのSparkモジュールです。DataFrames と呼ばれるプログラミングの抽象化が可能で、分散型 SQL クエリエンジンとしても機能します。これにより、既存のデプロイやデータで未修正の Hadoop Hive クエリを最大 100 倍の速さで実行できるようになりました。また、他の Spark エコシステ{...}Sparklyr Sparklyrとは
Sparklyr とは、R 言語と Apache Spark 間のインターフェースを提供するオープンソースのパッケージです。Spark では、分散データを低レイテンシで扱えるため、Spark の機能を最新のR環境で活用することができるようになりました。Sparklyr は、インタラクティブな環境にある大規模なデータセットと連動するための有効なツールです。これにより、Spark でデータを分析するために、R の使い慣れたツールを使用することが可能となり、R と Spar{...}SparkR SparkR とは、R 言語を Spark 上で動作させるためのツールです。Spark の他の言語バインディングと同じ原理に基づいています。SparkR を使用するには、環境にインポートしてコードを実行するだけです。Python ではなくR 言語の構文に従っていることを除けば、Python API と非常に類似しています。ほとんどの場合、Python で利用可能なものは、SparkR でも利用できます。
{...}Sparkアプリケーション Spark アプリケーションとは、ドライバプロセスと一連のエグゼキュータプロセスで構成されるアプリケーションプログラムです。ドライバプロセスは、main() 関数を実行し、クラスタのノード上で動作します。また、3 つの役割があり、Spark アプリケーションに関する情報管理、ユーザーのプログラムや入力への応答、およびエグゼキュータ（瞬間的に定義）全体におけるタスクの分析、分散、スケジューリングを行います。ドライバプロセスは必要不可欠です。Sparkアプリケーションの中心であり、アプリケーションの{...}Sparkストリーミング Apache Spark ストリーミングは、Apache Spark の前世代ストリーミングエンジンです。Spark ストリーミングの今後の更新はなく、レガシープロジェクトとなります。Apache Spark には、「構造化ストリーミング」と呼ばれる新しくて使いやすいストリーミングエンジンがあります。ストリーミングアプリケーションとパイプラインには、Spark 構造化ストリーミングをご使用ください。構造化ストリーミングの詳細はこちらでご覧いただけます。
Sparkストリーミングとは
{...}Sparkチューニング Sparkパフォーマンスチューニングとは
Sparkパフォーマンスチューニングとは、システムが使用するメモリやコア、インスタンスなどを記録するための設定を調整する処理のことです。この処理により、Sparkは優れた性能を発揮し、リソースのボトルネックの防止も可能になります。
データのシリアライズとは
メモリ使用量を削減するために、Spark RDDをシリアル化して格納する必要があります。また、データのシリアライズは、ネットワークのパフォーマンスにも影響します。Sparkの性能を向{...}Sparse Tensor Python には、多次元配列を操作する NumPy と呼ばれるビルトインライブラリがあります。PyTensor ライブラリを開発するには、NumPy を使用することが第一の要件となります。Sptensor は、Sparse Tensor を表すクラスです。Sparse Tensor とは、エントリの大部分がゼロであるデータセットです。例としては、大規模な対角行列（多くがゼロ要素）が挙げられます。Tensor オブジェクトの値全体を保存するのではなく、非ゼロ値とそれに対応する座標を保存します。S{...}Streaming Analytics How Does Stream Analytics Work?
Streaming analytics, also known as event stream processing, is the analysis of huge pools of current and “in-motion” data through the use of continuous queries, called event streams. These streams are triggered by a{...}Supply Chain Management What is supply chain management?
Supply chain management is the process of planning, implementing and controlling operations of the supply chain with the goal of efficiently and effectively producing and delivering products and services to the end{...}TensorFlow Google は、2015年11月に機械学習のためのフレームワークをオープンソースで公開し、TensorFlow と名付けました。CPU、GPU、GPU クラスタでの深層学習、ニューラルネットワーク、一般的な数値計算をサポートしています。TensorFlow の最大の利点はそのコミュニティにあり、多くの開発者、データサイエンティスト、データエンジニアがオープンソースの開発に貢献しています。TensorFlow の現在のバージョンは、リリースノートとともに GitHub&nbs{...}Tensorflow Estimator API Tensorflow Estimator API とは
Estimator は、完全なモデルを表しますが、ユーザーの多くに複雑な印象を与える傾向があります。Estimator API とは、モデルを訓練して、その精度を評価し、推論を作成するためのメソッドを提供する高レベル API です。下の図のように、TensorFlow は複数の API 層からなるプログラミングスタックを提供します。Estimator には、事前構築さ れた Estimator と、独自でカスタマイズする Es{...}Tungsten Tungsten プロジェクトとは
Tungsten は、Apache Spark の実行エンジンを変更する包括プロジェクトのコードネームです。Spark アプリケーション向けのメモリと CPU の効率を大幅に向上させることに重点を置き、性能を最新のハードウェアの限界に近づけます。
Tungsten プロジェクトに含まれるイニシアティブ
メモリ管理とバイナリ処理：アプリケーションのセマンティックスを活用してメモリを明示的に管理し、JVM オブジェクトモデルとガベージコレク{...}アノマリー検知 アノマリー検知とは、定常状態とは統計的に異なる不審なイベントや観測値を特定する手法です。異常検知とも呼ばれます。このような「異常」な挙動は、多くの場合に、クレジットカードの不正使用、マシンの故障、サイバー攻撃といった問題の存在を意味します。例えば、膨大な数のトランザクションの監視が必要な金融業界では、アノマリー検知がエラーの発生場所の特定や原因の分析を支援し、問題への迅速な対応を可能にします。また、検知した異常値をもとにしたアラートの発行にも活用され、担当者の行動を促します。そこから得られる情報{...}オルタナティブデータ オルタナティブ（代替）データとは
オルタナティブデータ（代替データとも呼ばれる）とは、従来のソースではなく、他のユーザーによって使用されていない代替データソースから収集されたデータ情報です。オルタナティブデータを分析に活用することで、業界標準のデータソースでは得ることができない洞察を取得することが可能です。ただし、正確には何をオルタナティブデータとみなすかは業界によって異なり、自社や競合他社で既に使用されている従来のデータソースに依存されています。
標準的なオルタナティブデータタイプ{...}オーケストレーション オーケストレーションとは
オーケストレーションとは、複数のコンピュータシステム、アプリケーション、サービスを調整および管理し、大規模なワークフローやプロセスを実行するために複数タスクをつなぎ合わせることです。これらのプロセスは、自動化された複数タスクで構成され、複数のシステムをまたぐこともあります。
オーケストレーションは、頻繁に繰り返されるプロセスの実行を効率化および最適化し、データチームが複雑なタスクやワークフローを容易に管理できるようにします。プロセスはいつでも繰り返しが可能で{...}オープンバンキング オープンバンキングとは
オープンバンキングとは、消費者の事前同意のもとに、消費者の金融データへのアクセスをセキュアに共有する方法です²。規制や技術革新、競合の勢いに後押しされ、オープンバンキングは、銀行以外の第三者や消費者などが顧客データをさらに活用できるよう、顧客データの民主化を呼びかけています。この技術革新は、銀行業界を他業界との高い連携性を持つプラットフォーム提供者へと進化させると同時に、銀行にエコシステムを拡大し、新規市場への参入機会を与えています。オープンバンキングを利用して、現{...}サービスとしての Apache Spark サービスとしての Apache Spark（Apache Spark as Spark-as-a-Service）とは
Apache Spark は、大規模なデータの高速リアルタイム処理を実現するオープンソースのクラスタコンピューティングフレームワークです。Spark は、カリフォルニア大学バークレー校の AMPLab で 2009 年に研究が開始されて以来、目覚ましい発展を遂げてきました。Apache Spark は現在、50 を超える組織から 200 名以上が参加する、ビッグデータの最{...}スタースキーマ スタースキーマとは
スタースキーマとは、データベース内のデータを整理することで理解・分析しやすくなった多次元データモデルで、データウェアハウスやデータベース、データマート、その他のツールに適用できます。スタースキーマの設計は、大規模なデータセットへのクエリを実行するために最適化されています。
1990 年代にラ ルフ・キンボールによって発表されたスタースキーマは、反復的なビジネス定義の重複を減らすことによってデータの保存や履歴の管理、データの更新を効率的に行い、データウェアハウスでのデ{...}スノーフレークスキーマ スノーフレークスキーマとは
スノーフレークスキーマは、スタースキーマを拡張した多次元データモデルで、ディメンションテーブルがサブディメンションテーブルに細分化されたものです。スノーフレークスキーマは、データウェアハウスやデータマート、リレーショナルデータベースの多次元分析を使用した BI（ビジネスインテリジェンス）やレポーティングによく使用されています。
スノーフレークスキーマでは、エンジニアがそれぞれのディメンションテーブルを論理的なサブディメンションに細分化します。このため、デー{...}データウェアハウス データウェアハウスとは？
データウェアハウス（DWH）は、複数のソースから得られた最新データや履歴データをビジネスに適した形で蓄積し、知見の取得やレポート作成を容易にするデータ管理システムです。主に、ビジネスインテリジェンス（BI）、レポート作成、データ分析に使用されます。
データウェアハウスでは、POS システム、インベントリ管理システム、マーケティングや販売データベースなどの業務システムに蓄積されたデータを、迅速かつ容易に分析可能です。データは、オペレーショナルデータストアを中継{...}データガバナンス データガバナンスとは
データガバナンスとは、データがビジネス戦略に沿った価値をもたらすよう、組織内のデータを統制することを意味します。単なるツールやプロセスにとどまらず、人、プロセス、技術、データを包括するフレームワークを用いてデータを統制し、ビジネスの目標達成を支援するものです。
ビジネスにおけるデータガバナンスのメリット
データの量と複雑さの増大に伴い、コアビジネスの強化につながるデータガバナンスに注目しています。データガバナンスはビジネスに次のようなメリットをもたらします{...}データレイクハウス データレイクハウスとは？
データレイクハウスとは、データレイクの柔軟性、経済性、スケーラビリティとデータウェアハウスのデータ管理や ACID トランザクションの機能を取り入れたオープンで新たなデータ管理アーキテクチャで、あらゆるデータにおけるビジネスインテリジェンス（BI）と機械学習（ML）を可能にします。
シンプル、柔軟で低コストなデータレイクハウス
データレイクハウスは新たなオープンシステムデザインによって構築されており、データウェアハウスと類似のデータ構造とデータ管理機能{...}データ分析プラットフォーム データ分析プラットフォームとは
データ分析プラットフォームとは、膨大で複雑な動的データの分析に必要なサービスとテクノロジーのエコシステムです。企業が所有する各種ソースからのデータの取得、結合、連動、検索、視覚化を可能にします。包括的なデータ分析プラットフォームには、予測分析、データ視覚化、 ロケーションインテリジェンス、自然言語、コンテンツ分析など、さまざまな機能を搭載した複数のツールが組み込まれています。その主な目的は、あらゆる種類のデータを実用的な洞察に変換し、真のビジネス成果につなげる{...}トランスフォーメーション トランスフォーメーションとは：
Sparkでは、コアとなるデータ構造は不変であり、一度作成したデータ構造は変更できないため、実際に使用する際に、最初はこの概念に疑問を抱くかもしれません。SparkでDataFrameを変更するためには、Sparkに対し、既存のDataFrameをどのように修正したいかを指示する必要があります。この指示をトランスフォーメーションと呼びます。トランスフォーメーションとは、Sparkを使用してビジネスロジックをどのように記述するかの中心となるものです。トランスフ{...}ニューラルネットワーク ニューラル ネットワークとは
ニューラルネットワークとは、層状構造が人間の脳内にあるニューロンのネットワーク構造に類似した数理モデルです。ニューロンと呼ばれる相互に結合する処理要素を特徴としており、出力機能を生成します。ニューラルネットワークは、入力層と出力層で構成されており、その多くには隠れ層があります。この隠れ層は、入力を出力層で使用できるものに変換するユニットで構成されています。
ニューラルネットワークアーキテクチャのタイプ
人工ニューラルネットワークとしても知られるニュー{...}ハッシュバケット コンピューティングにおけるハッシュテーブル [ハッシュマップ] とは、キー [一意の文字列または整数] に基づいてオブジェクトに事実上直接アクセスできるデータ構造です。ハッシュテーブルは、バケットやスロットの配列にインデックス計算を行うために、ハッシュ関数を使用し、そこから目的の値をみつけます。使用されるキーの主な特徴は次のとおりです。
社会保障番号、電話番号、口座番号などのキーを使用します。
キーは一意である必要があります。
各キーは、値に関連付け（マッピング）されます。
{...}バイオインフォマティクス バイオインフォマティクスは、膨大な生物学のデータのコレクションから知識を抽出するために計算を使用する研究分野です。
バイオインフォマティクスは、生物学のデータの保存、取得、整理、分析を行うバイオテクノロジーにITを活用することを指します。膨大なデータ量がゲノム配列決定プロジェクトや他の研究から生成されており、このデータ急増により、実に生物学における課題のほとんどは、膨大な計算の必要性に迫られています。バイオインフォマティクスという用語は、1970年にポーリーン・ホフヴェイ（Paul{...}ビッグデータ分析 データ分析とビッグデータ分析の違い
Hadoop が開発される以前は、最新のストレージと計算システムの基盤となる技術には限りがあり、企業での分析はスモールデータに制限されていました。しかし、このような比較的簡易な形式でも、特に新しいデータソースの統合においては、分析が困難なケースが生じていまし た。従来のデータ分析は構造化データのテーブルで構成されたリレーショナルデータベース（SQL データベースなど）の使用に依存しています。データを分析用のデータベースに取り込む前に、未加工データ{...}ベイジアンニューラルネットワーク ベイジアンニューラルネットワークとは
ベイジアンニューラルネットワーク（BNN）とは、過学習の制御を目的として、事後確率推定により標準ネットワークを拡張することを指します。広い視点からみると、ベイジアン手法は統計的方法論を使用して、モデルパラメータ（ニューラルネットワークの重みとバイアス）を含む、あらゆるものがそれに付随する確率分布を持つようにすることです。プログラミング言語において、特定の値を取得できる変数は、その特定の変数にアクセスする度に同じ結果になります。まず、一連の入力特徴量の加{...}ホスト型のSpark ホスト型の Spark とは
Apache Spark とは、2009年に UC バークレーで、高速性、使いやすさ、高度な分析を中心として構築されたビッグデータ用の高速で汎用的なクラスタコンピューティングシステムです。Apache Spark は、Scala、Java、Python、R の高レベル API と、データ分析用の一般的な計算グラフをサポートする最適化されたエンジンを提供します。さらに、SQL とデータフレーム用の Spark SQL、機械学習用の MLlib、グラフ処理用の G{...}マテリアライズドビュー データブリックスの Delta パイプラインとマテリアライズドビュー
概要
Delta パイプラインは、データパイプラインのライフサイクルを管理する API と UI を提供します。オープンソースのフレームワークがデータエンジニアリングチームによる ETL の開発をシンプルにし、データの信頼性を向上させ、運用の拡張を支援します。データ変換のコーディングやジョブのスケジューリングを行う代わりに、宣言型パイプラインを構築することで、データの最終状態を容易に定義できます。さまざまなタスク間{...}マネージドSpark マネージドSparkとは
マネージド Spark は、バッチ処理、クエリ、ストリーミング、機械学習などのオープンソースのデータツールを利用できるマネージドサービスです。ユーザーは、このような自動化を使用することで、オンデマンドでクラスタの迅速な作成や管理を容易し、タスクが完了したときにクラスタをオフにすることができます。ワークロード、パフォーマンス要件、または既存のリソースに基づいてクラスタのサイズを設定することも可能です。さらに、ほんの数秒で動的にスケールアップおよびスケールダウンできる{...}モデルリスク管理 モデルリスク管理とは、モデルの誤りまたは誤用に基づく意思決定によって生じる潜在的な悪影響がもたらすリスクを管理することです。モデルリスク管理は、モデルリスク、すなわちモデルの誤りや誤用の可能性を特定、測定、軽減する技術や手法を取り入れることを目的にしています。金融サービスにおけるモデルリスクとは、精度が低いモデルを使用して意思決定を行うことで生じる損失リスクを意味します。多くの場合は 金融証券の評価に使用され、消費者信用スコアの付与、クレジットカードの不正取引のリアルタイムな確率予測、マネーロンダ{...}ラムダアーキテクチャ ラムダアーキテクチャとは
ラムダアーキテクチャとは、膨大なデータ「ビッグデータ」を処理するアプローチです。ハイブリッドアプローチを使用してバッチ処理やストリーム処理メソッドへのアクセスを提供し、任意の関数を計算する問題を解決するために使用されます。ラムダアーキテクチャは3つのレイヤーから構成されています。
バッチレイヤー
新しいデータは、データシステムへのフィードとして継続的に提供されます。データはバッチレイヤーとスピードレイヤーに同時に供給されます。全てのデータを一度に調べ、{...}リアルタイムなリテール（小売業） 小売業におけるリアルタイムデータ
小売業におけるリアルタイムデータとは、データへのリアルタイムなアクセスを意味します。バッチ式のアクセス、分析、コンピューティングからリアルタイムアクセスに移行することで、データは常時稼働の状態となり、正確でタイムリーな意思決定とビジネスインテリジェンス（BI）の推進が可能になります。需要予測、パーソナライゼーション、店頭在庫の可用性、到着時間予測、オーダーピッキングとコンソリデーションといったリアルタイムのユースケースは、サプライチェーンのアジリティ{...}リテール向けレイクハウス リテール向けレイクハウス
リテール向けレイクハウスは、Databricks 初の業界特化型レイクハウスです。ソリューションアクセラレータ、データ共有のケイパビリティ、パートナーエコシステムを通じて、小売業者の迅速な業務遂行を支援します。
リテール向けレイクハウスは、テクノロジー、パートナー、ツール、業界イニシアチブの集大成であり、データと AI における強力なコラボレーションを推進します。リテール向けレイクハウスの主要な構成要素は、次の 4 つです。
データと AI {...}予測分析 予測分析とは
予測分析とは、新しいデータと過去のデータを活用してパターンを見つけ出し、将来の結果や傾向を予測する高度な分析手法です。
予測分析の仕組み
予測分析では、統計分析技術、分析クエリ、データマイニング、予測モデリング、自動機械学習アルゴリズムなどの多くの技術をデータセットに使用して、特定の事象が発生する可能性を数値化し、what-if シナリオやリスク評価などを含む予測モデルを作成します。予測分析により、組織はデータに含まれるパターンを見つけて利用することで、リスクと機{...}予測型メンテナンス 予測型メンテナンスとは
予測型メンテナンスとは、一言でいうと、予め定められたスケジュールだけでなく、設備の実際の状態や状況に基づき、いつ頃、具体的にどのようなメンテナンスを行うべきかを判断し、設備の稼働時間と生産性を最大化するためのものです。故障を予測や予防し、適切な定期メンテナンスを実施することで、コストのかかる機器のダウンタイムを削減できます 。
IoT とセンサーデータが機器からストリーミングされることで、予測型メンテナンスは、製造業者が効率的に機械が停止するタイミングを予測する{...}人工ニューラルネットワーク 人工ニューラルネットワークとは
人工ニューラルネットワーク（ANN）とは、人間の脳内にある神経細胞（ニューロン）の動作を模したコンピューティングシステムです。
人工ニューラルネットワークの仕組み
人工ニューラルネットワーク（ANN）は、階層で構成される重み付き有向グラフにするとわかりやすく、これらの階層は人間の脳の生体ニューロンを模した多数のノードを特徴とし、相互に接続され、活性化関数を含みます。第1層は、外部から未処理の入力信号を受信します。人間の視覚処理における視神経に類似{...}構造化ストリーミング 構造化ストリーミングとは、ストリーミン グデータを処理するための高レベル API です。Spark 2.2 で実運用が可能になりました。構造化ストリーミングでは、Spark の構造化 API を使用してバッチモードで実行するのと同じ操作が、ストリーミング形式で実行可能です。これにより、レイテンシの短縮、インクリメンタル処理が可能になります。構造化ストリーミングの最大のメリットは、事実上コードを変更することなく、ストリーミングシステムから迅速に価値を引き出すことができることです。また、バッチジョブを{...}機械学習パイプライン 通常、機械学習アルゴリズムを実行する際には、前処理、特徴抽出、モデル適合、検証など一連のステージのタスクが含まれます。例えば、テキスト文書を分類する場合、テキストのセグメンテーションやクリーニング、特徴量の抽出、交差検証での分類モデルのトレーニングなどがあります。各ステージに利用できるライブラリは多数ありますが、特に大規模なデータセットを使用する場合、それぞれのライブラリを全体につなげる作業は容易ではありません。また、ほとんどの機械学習ライブラリは、分散計算用には設計されていないか、パイプライン{...}機械学習モデル 機械学習モデルとは
機械学習モデルとは、未知のデータセットからパターンを発見したり、判断を導き出すプログラムのことです。例えば、自然言語処理では、機械学習モデルにより、これまで聞き取れなかった文章や単語の組み合わせの背後にある意図を解析し、正しく認識できます。また、画像認識では、機械学習モデルを学習させることで、車や犬などのオブジェクトを認識できます。機械学習モデルは、大規模なデータセットを用いて「トレーニング」することで、上述のようなタスクの実行が可能になります。トレーニングでは、機械学{...}機械学習ライブラリ（MLlib） Apache Spark の機械学習ライブラリ（MLlib）とは、シンプルでスケーラビリティが高く、他のツールと容易に統合できるように設計された、機械学習を実装するためのツールです。Sparkのスケーラビリティ、言語の互換性、高速性により、データサイエンティストは、分散デー タを取り巻く複雑さ（インフラストラクチャ、構成など）の解決ではなく、データの問題とモデルに集中できます。Spark 上に構築されたMLlibは、分類、回帰、クラスタリング、協調フィルタリング、次元削減、基になる最適化プリミティ{...}深層学習 深層学習とは
深層学習とは、人間の脳の構造と機能にインスパイアされたアルゴリズムを用いて膨大なデータを扱う機械学習のサブセットです。そのため、深層学習モデルはディープニューラルネットワークと呼ばれます。深層学習は、データ表現の学習に基づく機械学習手法の1つで、従来のタスク固有のアルゴリズムとは異なります。
深層学習の仕組み
深層学習では、コンピュータモデルが、画像、言語、または音声から直接分類タスクを実行できるように学習します。タスクを繰り返し実行して、結果改善のための微調整を{...}統合 AI フレームワーク 総合人工知能（ UAI ）は、開発者カンファレンス「F8 」で Facebook によって発表されました。UAI は Facebook 主導で開発された、PyTorch と Caffe の 2 つの深層学習フレームワークを統合したもので、PyTorch は大規模なコンピューティングリソースへのアクセスを想定したリサーチに焦点を当て、Caffeは、Android や Raspberry Pi デバイスのモデル展開に焦点を当てています。スコープが狭い Facebook の統合 AI とは異なり、統合{...}統合データウェアハウス 統合データウェアハウスとは
統合データウェアハウス（エンタープライズデータウェアハウスとも呼ばれる）は、業務に関わるあらゆるデータを保持し、エンタープライズ全体でのアクセスが可能な統合データベースです。今日、多くの企業においてデータはサイロ化されています。データの品質、統合、ガバナンスの保守や、メタやマスターデータ、B2B データ交換、データベース、アーキテクチャの管理など、同じ組織内の異なるそれぞれの部門で、多様なデータをさまざまなツールで管理しています。大企業におけるデータウェアハウス{...}統合データ分析 統合データ分析とは、データ処理を AI 技術と統合する新しいカテゴリのソリューションです。企業組織にとっての AI の実現可能性を格段に高め、AI への取り組みを加速化させます。統合データ分析により、企業におけるサイロ化されたデータストレージシステム間でのデータパイプラインの構築や、モデル構築のラベル付きデータセットの準備が容易になるため、組織は既存のデータに AI を実行したり、大規模なデータセットに対して AI を繰り返し実行したりできるようになります。 また、統合データ分析では、幅広い {...}統合データ分析プラットフォーム データブリックスの統合データ分析プラットフォーム は、データサイエンスをエンジニアリングとビジネスに統合し、組織のイノベーションを加速させます。データブリックスを統合データ分析プラットフォームとして使用することで、大規模なデータを制限なく迅速に準備し、クリーンアップすることができます。また、このプラットフォームでは、 あらゆる人工知能アプリケーションに対して ML モデルを継続的にトレーニングしてデプロイすることも可能です。統合データ分析プラットフォームを使用するメリットのトップ 3 は{...}耐障害性分散データセット（RDD) 耐障害性分散データセット（RDD）とは、Spark のリリース以降、Spark の主要なユーザー向け API として利用されてきました。RDD は、クラスタ内の複数のノードに配置されたデータ要素の不変の集合体であり、変換その他の操作のための基礎的な API と並行して使用することが可能です。
RDDの使用が適した5つのケース
データセットに対し、低レベルの変換やアクション、管理を実行する場合
所有データがメディアストリームやテキストストリームなどの非構造化データである場合
{...}自動化バイアス 自動化バイアスとは
自動化バイアスとは、自動化支援システムや意思決定支援システムに過度に依存することを意味します。自動化された意思決定支援システムの利用可能性は高まっており、集中治療室や航空機のコックピットなど重大な影響を及ぼす意思決定が必要な状況下での利用も一般的になりつつあります。認知的努力を最小限に抑え、「自動化バイアス」に依存しがちなのは、人間の傾向性ですが、大規模なデータからの学習をベースとするAIや自動化機能にも同様の傾向性が当てはまる懸念があります。このタイプのコンピューテー{...}複合イベント処理 複合イベント処理（CEP）とは
複合イベント処理（CEP）とは、イベント処理、ストリーム処理、あるいはイベントストリーム処理とも呼ばれ、データベースにデータを格納する前か、場合によっては格納せずに、データを照会する技術を使用した処理です。複合イベント処理は、多くの異なる情報を集約するのに有用で、イベント間の因果関係をリアルタイムで特定、分析する整理ツールです。CEPでは連続的に収集されるイベントをパターンと照合し、イベント内容についての洞察の提供、効果的なアクションの積極的な実行を可能にし{...}設備総合効率（ OEE ) 設備総合効率（ OEE ）とは
設備総合効率（ OEE ） は、 製造 におけるオペレーションが、予定されている期間中に、その潜在的な能力（設備、時間、材料）に対して、どれだけ利用されているかを示す指標です。製造時間のうち、実際の稼働時間の割合を特定することができます。OEE は、離散的または連続的なプロセスの総合的な性能を示すダッシュボードです。OEE は 100% で最大となり、良い部品だけが（ 100% 品質）、最高速度で（ 100%{...}連続実行アプリケーション 連続実行アプリケーションとは、データにリアルタイムで反応するエンドツーエンドのアプリケーションです。特に開発者は、このアプリケーションを活用することで、単一のプログラミングインターフェイスを使用して、クエリの提供やバッチジョブとの対話など、現在別々のシステムで処理されている連続実行アプリケーションの側面をサポートすることができます。連続実行アプリケーションが処理できるユースケースは次のとおりです。
リアルタイムで提供されるデータの更新：開発者は、更新と提供（SparkのJDBCサーバ{...}需要予測 需要予測とは
需要予測とは、消費者の需要（将来収益）を予測するプロセスです。具体的には、定量的および定性的データを使用して、消費者が購入する商品の品揃えを予測します。
小売業者は、消費者が求めるタイミングで商品を提供できないことで、1 兆円規模の収益機会を逃しています。低精度の需要予測は、需要に即していない商品の陳列につながり、さらには、店頭の在庫切れなどの大きな問題を引き起こします。
リテール向けレイクハウスによる需要予測
リテール向けレイクハウスは、デー{...}高密度テンソル 高密度テンソルとは、全ての値が示される連続したメモリのブロックに値を格納する幾 何概念です。テンソルまたは多次元配列は、多様な多次元データ分析アプリケーションで使用されます。さまざまなオープンソースのサードパーティツールボックスによって拡張された MATLAB suite など、テンソル計算を実行できるソフトウェア製品は数多くあります。MATLAB 単体でも、さまざまな要素ごとおよびバイナリの密なテンソル演算をサポートしています。各ニューロンが前の層の全てニューロンからの入力を受け取り、高密度で接{...}製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ製品プラットフォーム料金オープンソーステクノロジーDatabricks 無料トライアルデモ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティ学習・サポートドキュメント用語集トレーニング・認定ヘルプセンター法務オンラインコミュニティソリューション業種別プロフェッショナルサービスソリューション業種別プロフェッショナルサービス会社情報会社概要採用情報ダイバーシティ＆インクルージョンDatabricks ブログご相談・お問い合わせ会社情報会社概要採用情報ダイバーシティ＆インクルージョンDatabricks ブログご相談・お問い合わせ 採用情報言語地域English (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/dataaisummit/why-attend | Why Attend - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingWhy AttendExplore everything Data + AI Summit has to offer — from the latest innovations and technologies to thought-provoking panel discussions to networking opportunities where you can connect with other data professionals in your industry.Register nowSession informationWith 10 amazing tracks and over 20 technologies to learn about, get the right experience for your level.
Data Lakehouse ArchitectureThe decisions you make about your core data architecture will affect the reliability, performance and utility of your data analytics, data science and machine learning. Increasingly, many organizations are adopting the data lakehouse to improve their data platform. Has the ability to more easily combine structured, unstructured and real-time data workloads created new opportunities for data teams? Has ACID reliability on data lakes simplified how data is used? In this track, get answers to these questions and find out how to adopt a data lakehouse, migrate from data lakes and data warehouses, and integrate lakehouses with other data platforms.
Technologies/topic ideas: lakehouse architecture, Delta Lake, Photon, platform security and privacy, serverless, cost management, data warehouse, data lake, Apache Iceberg, Data Mesh
Data GovernanceData governance, security and compliance are critical — they help guarantee that all data, BI and ML assets are maintained and managed securely across the enterprise and you’re compliant with regulatory frameworks. In this track, learn best practices, frameworks, processes, roles, policies and standards for data governance of structured and unstructured data across clouds.
Technologies/topic ideas: data governance, multicloud, Unity Catalog, security, compliance, privacy
Data SharingWhen enterprises can easily exchange data with their customers, partners, suppliers and internal lines of business, they can better collaborate, innovate and unlock value from that data. In this track, discover best practices for sharing data across data platforms and clouds, ways to avoid replication and lock-in, and how to distribute data products through marketplaces.
Technologies/topic ideas: sharing and collaboration, Delta Sharing, data cleanliness, data cleanrooms, data marketplaces
Data Engineering If you want to optimize data processing and reduce costs, this session is for you. You’ll learn how to use a combination of systems and processes to ingest, orchestrate and transform raw data for analytics and machine learning. Dive into best practices for data architectures, software engineering, ETL, data management, data quality, DataOps and orchestration.
Technologies/topic ideas: data pipelines, orchestration, CDC, medallion architecture, Delta Live Tables, dbt, Databricks Workflows, ETL/ELT, DataOps, Parquet, Apache Spark internals
Data Streaming Today’s organization needs to respond in real time as events unfold. In this track, learn how data streaming can help you drive faster decision-making, make more accurate predictions and improve customer experiences. Explore best practices for implementing real-time data pipelines with your favorite tools and languages, find out how to reduce complexity for real-time data workflows and eliminate silos to support all your real-time use cases.
Technologies/topic ideas: Apache Spark Structured Streaming, real-time ingestion, real-time ETL, real-time ML, real-time analytics, real-time applications, Delta Live Tables
DSML: Production ML/MLOpsOvercome the challenges of putting ML projects into production and operationalizing them at scale. In this track, you’ll find out how to scale ML in production and apply MLOps best practices across the end-to-end machine learning lifecycle.
Technologies/topic areas: MLOps, Feature Stores, organizational ML, MLflow, Model Serving and more
DSML: ML Use Cases/TechnologiesMachine learning continues to disrupt industries and accelerate business outcomes across use cases and industries. In this track, discover how to apply ML to solve business challenges, explore technologies and best practices, and learn how to integrate data science with the rest of your organization.
Technologies/topic areas: PyTorch, TensorFlow, Keras, XGBoost, fast.ai, scikit-learn, Python and R ecosystems, deep learning, notebooks, LLMs and more
Data Warehouses, BI and Visualization Data analytics is a vital component of business decision-making. In this track, find out how to build analytics pipelines and integrations and learn about tooling and infrastructure for SQL analytics, BI and visualization.
Technologies/topic ideas: ANSI SQL, Redash, Databricks SQL, Tableau, Power BI, visualization techniques, Spark SQL and DataFrames, data integration, data warehouse and analytics
ResearchIn this track — dedicated to academic and advanced industrial research — explore large-scale data analytics and machine learning systems, the hardware that powers them (GPUs, I/O storage devices, etc.) and how these systems are applied to use cases like genomics, astronomy, image scanning, disease detection, vaccine research and more.
Data StrategyChoosing a data lakehouse platform is only the first step in implementing your data strategy. Success requires a thoughtful approach to people and processes. In this track, you’ll find out how to align goals, identify the right use cases, organize and enable teams, mitigate risk and operate at scale so you can be even more successful with data, analytics and AI.
Technologies/topic ideas: data management, data governance, strategy, data teams, data mesh, data democratization
Content tailored to your roleData + AI Summit has something for everyone — explore, learn and connect with us and the data, analytics and AI community.
Data EngineerIncrease efficiency, control costs and reduce data risks — all in a day’s work for today’s data engineer. Broaden your knowledge and expand your skills by learning from industry experts and your peers how to deliver high-quality, reliable data for every use case from BI to AI.
Explore sessionsData ScientistSuddenly, AI is everywhere and that’s putting pressure on data scientists and ML engineers to quickly deliver results. Learn how lakehouse and tools like MLflow can accelerate productionization of models, increase productivity, reduce risk and increase ROI on new models.
Explore sessionsData AnalystData analytics has moved beyond the warehouse. Join the global community of data analysts at Data + AI Summit and discover ways to support your stakeholders in making informed and timely decisions, achieve line of business KPIs and reduce rework risks.
Explore sessionsIT Decision MakerData and AI are now mission critical for every business. Learn how the lakehouse architecture unifies data, analytics and AI on a single platform for better performance, lower TCO and faster innovation.
Explore sessionsIndustry ExperiencesJoin and learn along with your industry peers in these specialized sessions focused on the unique challenges, use cases, and the data, analytics and AI requirements of your sector.
Financial ServicesLearn how to minimize risk, deliver superior customer experience and accelerate innovation on lakehouse.
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Government and Public SectorLearn how lakehouse is helping to unlock the full potential of data to deliver mission objectives and better serve citizens.
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Healthcare and Life SciencesLearn ways to accelerate research and improve patient outcomes on lakehouse.
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ManufacturingLearn how the lakehouse helps optimize supply chains, boost product innovation, increase operational efficiencies, predict fulfillment needs and reduce overall costs.
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Communications, Media & EntertainmentLearn ways to accelerate audience and advertiser outcomes — 360° view of audience, lower churn, increased ARPU — with lakehouse.
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Retail and Consumer GoodsLearn how lakehouse is helping to harness the full power of data so retailers, suppliers and partners can collaborate across the value chain.
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Don’t miss this year’s event!Register NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/legal/legal-subscription | Databricks Legal Communication Subscription | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWLegalTermsDatabricks Master Cloud Services AgreementAdvisory ServicesTraining ServicesUS Public Sector ServicesExternal User TermsWebsite Terms of UseCommunity Edition Terms of ServiceAcceptable Use PolicyPrivacyPrivacy NoticeCookie NoticeApplicant Privacy NoticeDatabricks SubprocessorsPrivacy FAQsDatabricks Data Processing AddendumAmendment to Data Processing AddendumSecurityDatabricks SecuritySecurity AddendumLegal Compliance and EthicsLegal Compliance & EthicsCode of ConductThird Party Code of ConductModern Slavery StatementFrance Pay Equity ReportSubscribe to UpdatesDatabricks Legal Communication SubscriptionSign up here to receive notifications when Databricks updates either the Service Specific Terms or the Subprocessors ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
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https://www.databricks.com/glossary/keras-model | What is the Keras Model?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWKeras ModelAll>Keras ModelTry Databricks for freeGet StartedWhat is a Keras Model?Keras is a high-level library for deep learning, built on top of Theano and Tensorflow. It is written in Python and provides a clean and convenient way to create a range of deep learning models. Keras has become one of the most used high-level neural networks APIs when it comes to developing and testing neural networks. Creating layers for neural networks as well as setting up complex architectures are now a breeze due to the Keras high-level API. A Keras model is made up of a sequence or a standalone graph. There are several fully configurable modules that can be combined to create new models. Some of these configurable modules that you can plug together are neural layers, cost functions, optimizers, initialization schemes, dropout, loss, activation functions, and regularization schemes. One of the main advantages that come with modularity is that you can easily add new features as separate modules. As a result, Keras is very flexible and well-suited for innovative research. There are two ways you can develop a Keras model: sequential and functional. Sequential API ModeThe Sequential API model is the simplest model and it comprises a linear pile of layers that allows you to configure models layer-by-layer for most problems. The sequential model is very simple to use, however, it is limited in its topology. The limitation comes from the fact that you are not able to configure models with shared layers or have multiple inputs or outputs.Functional APIAlternatively, the Functional API is ideal for creating complex models, that require extended flexibility. It allows you to define models that feature layers connect to more than just the previous and next layers. Models are defined by creating instances of layers and connecting them directly to each other in pairs, Actually, with this model you can connect layers to any other layer. With this model creating complex networks such as siamese networks, residual networks, multi-input/multi-output models, directed acyclic graphs (DAGs), and models with shared layers becomes possible. Additional ResourcesKeras DocumentationHow to Use MLflow to Experiment a Keras Network Model: Binary Classification for Movie ReviewsHow to Use MLflow To Reproduce Results and Retrain Saved Keras ML ModelsBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/dataaisummit/speaker/ankit-mathur/# | Ankit Mathur - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingAnkit MathurTech Lead, Model Serving GPUs at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/brendan-barsness | Brendan Barsness - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingBrendan BarsnessData and Analytics Architect at DeloitteBack to speakersBrendan Barsness is an advanced analytics consultant helping organizations apply data and AI solutions. He develops technical architectures and drives the deployment and adoption of cloud resources and services to meet the scalable demand for enterprise analytics and data management. Brendan is a Databricks Solutions Architect Champion at Deloitte and currently supports the State Department’s Center for Analytics.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/kr/resources | Resources - DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWResourcesLoading...ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/dataaisummit/speaker/donghwa-kim | Donghwa Kim - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingDonghwa KimSr. Director of IT Architecture at Ontada, a McKesson CompanyBack to speakersDonghwa Kim is the Sr. Director of Architecture at Ontada, a McKesson company. He is responsible for delivering the next generation Data and Analytics platform using Databricks Lakehouse. Prior to joining Ontada, he worked as the enterprise architect for migrating a large scale on-prem data warehouse into Databricks at Veterans Affairs (VA) and at Centers for Medicare and Medicaid Services (CMS). He has over 20 years of IT experiences within healthcare and finance industries. Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/meena-ram/# | Meena Ram - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMeena RamSr Director, Enterprise Data and Records Management Office at CIBCBack to speakersMeena Ram knows the Value of Data and has successfully bridged the gaps between business and technology stakeholders over the past 15 years.
Meena Ram is the Head of Enterprise Data Management at Canadian Imperial Bank of Commerce (CIBC).
Overseeing CIBC’s Data practice across Canada and US Region. Before leading CIBC’s Enterprise Data Management Office, Meena played a key role in setting the vision and strategy of Enterprise Data hubs to support analytics, working on complex data structures and delivering multi-million dollar migrations in Capital Markets. Prior to CIBC, Meena was a Data and Analytics Consultant for Deloitte where she working on various high profile institutions both in the US and Canada providing advisory and development solutions. Meena was a Business Intelligence and ETL developer for CGI’s Wealth Management servicing 4 major clients. In the UK, Meena was managing multiple Data Warehouses for Citibank’s credit card platforms. Meena holds a Bachelor in Software Engineering and a Masters in Artificial Intelligence from Manchester University. Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/fr/company/partners | Partenaires | DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT
Au revoir, entrepôt de données. Bonjour, Lakehouse.
Assistez pour comprendre comment un data lakehouse s’intègre dans votre pile de données moderne.
Inscrivez-vous maintenantSolutionsSolutions par secteurServices financiersSanté et sciences du vivantProduction industrielleCommunications, médias et divertissementSecteur publicVente au détailDécouvrez tous les secteurs d'activitéSolutions par cas d'utilisationSolution AcceleratorsServices professionnelsEntreprises digital-nativesMigration des plateformes de données9 mai | 8h PT
Découvrez le Lakehouse pour la fabrication
Découvrez comment Corning prend des décisions critiques qui minimisent les inspections manuelles, réduisent les coûts d’expédition et augmentent la satisfaction des clients.Inscrivez-vous dès aujourd’huiApprendreDocumentationFORMATION ET CERTIFICATIONDémosRessourcesCommunauté en ligneUniversity AllianceÉvénementsSommet Data + IABlogLabosBeacons26-29 juin 2023
Assistez en personne ou connectez-vous pour le livestream du keynoteS'inscrireClientsPartenairesPartenaires cloudAWSAzureGoogle CloudContact partenairesPartenaires technologiques et de donnéesProgramme partenaires technologiquesProgramme Partenaire de donnéesBuilt on Databricks Partner ProgramPartenaires consulting et ISProgramme Partenaire C&SISolutions partenairesConnectez-vous en quelques clics à des solutions partenaires validées.En savoir plusEntrepriseOffres d'emploi chez DatabricksNotre équipeConseil d'administrationBlog de l'entreprisePresseDatabricks VenturesPrix et distinctionsNous contacterDécouvrez pourquoi Gartner a désigné Databricks comme leader pour la deuxième année consécutiveObtenir le rapportEssayer DatabricksRegarder les démosNous contacterLoginJUNE 26-29REGISTER NOWPartenaires DatabricksDatabricks compte plus de 1200+ partenaires dans le monde qui fournissent des solutions et des services de données, d'analytique et d'IA à nos clients communs en utilisant la plateforme Lakehouse de Databricks. Ces partenaires vous permettent d'exploiter Databricks pour unifier toutes vos charges de travail de données et d'IA afin d'obtenir des insights plus pertinents.« Databricks gère la quantité de données tandis que Tableau accélère la visualisation. Ces solutions forment un tandem parfait au cœur de notre plateforme, offrant à nos clients la performance dont ils ont besoin pour fournir des capacités de véhicule autonome de pointe. »— Patrick McAuliffe, Lead Engineer, IncitePartenaires cloudDatabricks fonctionne sur AWS, Microsoft Azure, Google Cloud et Alibaba Cloud, avec une intégration profonde aux services d'infrastructure, de données et d'IA de chaque fournisseur.Partenaires technologiquesLes partenaires technologiques intègrent leurs solutions à Databricks pour offrir des fonctionnalités complémentaires en matière d'ETL, d'ingestion de données, de BI, de ML et de gouvernance.Partenaires consultingLes partenaires consultants sont des experts qui occupent une position unique pour vous aider à élaborer, mettre en œuvre et faire évoluer des initiatives en matière de données, d'analytique et d'IA avec DatabricksDevenir partenaireNos partenaires travaillent avec Databricks pour mettre au point et fournir des solutions innovantes à destination des entreprises. Rejoignez le programme de partenariat Databricks et accédez à des outils, des formations, des accélérateurs de solutions et des programmes de mise sur le marché.En savoir plusProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneSolutionsBy IndustriesServices professionnelsSolutionsBy IndustriesServices professionnelsEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterD écouvrez les offres d'emploi
chez Databrickspays/régionsEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Avis de confidentialité|Conditions d'utilisation|Vos choix de confidentialité|Vos droits de confidentialité en Californie |
https://www.databricks.com/company/board-of-directors | Databricks Board of Directors | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWLeadershipWith a long-term vision, our leadership team leverages decades of experience to chart a new course for data and AIMeet our teamExecutive teamFoundersBoard of directorsIon StoicaCo–founder and Executive ChairBen HorowitzCo–founder of Andreessen HorowitzElena DonioBoard MemberAli GhodsiCo–founder and Chief Executive OfficerPete SonsiniGeneral Partner at New Enterprise AssociatesJonathan ChadwickBoard MemberMatei ZahariaCo–founder and Chief TechnologistScott ShenkerProfessor of Computer Science at UC BerkeleyProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/company/careers/open-positions?location=munich%2C%20germany | Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
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https://www.databricks.com/br/try-databricks?itm_data=Homepage-HeroCTA-Trial | Experimente o Databricks | DatabricksExperimente o Databricks gratuitamente Experimente a plataforma Databricks completa gratuitamente por 14 dias em sua nuvem AWS, Microsoft Azure ou Google Cloud.Simplique a ingestão de dados e automatize processos de ETLImporte dados de centenas de fontes. Utilize uma abordagem asseverativa e simples para construir pipelines de dados.Colabore em sua linguagem preferidaCode em Python, R, Scala e SQL com colaboração, versionamento automático, integrações com Git e controles de acesso (RBAC) Preço/Performance até 12x superior a data warehouses em nuvemDescubra porque mais de 7000 clientes de todo mundo confiam na Databricks para todos os seus workloads de Dados, de BI a AI.Crie sua conta Databricks1/2Primeiro NomeSegundo NomeEmailEmpresaTítuloTelefone (Opcional)SelecionarPaísContinuarAviso de privacidade (atualizado)Termos de UsoSuas opções de privacidadeSeus direitos de privacidade na Califórnia |
https://www.databricks.com/dataaisummit/speaker/geoffrey-freeman/# | Geoffrey Freeman - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingGeoffrey FreemanSolutions Architect at T-MobileBack to speakersGeoffrey Freeman has spent most of his career working in massive scale online data delivery services. He is currently a solution architect for T-Mobile's procurement division.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/jitendra-malik | Jitendra Malik - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJitendra MalikComputer Vision Pioneer, Former Head of Facebook AI Research at University of California at BerkeleyBack to speakersJitendra Malik was born in Mathura, India in 1960. He received the B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 1980 and the PhD degree in Computer Science from Stanford University in 1985. In January 1986, he joined the university of California at Berkeley, where he is currently the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences. He is also on the faculty of the department of Bioengineering, and the Cognitive Science and Vision Science groups. During 2002-2004 he served as the Chair of the Computer Science Division, and as the Department Chair of EECS during 2004-2006 as well as 2016-2017. In 2018 and 2019, he served as Research Director and Site Lead of Facebook AI Research in Menlo Park.
Prof. Malik's research group has worked on many different topics in computer vision, computational modeling of human vision, computer graphics and the analysis of biological images. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, shape contexts and R-CNN. He has mentored more than 70 PhD students and postdoctoral fellows.
He received the gold medal for the best graduating student in Electrical Engineering from IIT Kanpur in 1980 and a Presidential Young Investigator Award in 1989. At UC Berkeley, he was selected for the Diane S. McEntyre Award for Excellence in Teaching in 2000 and a Miller Research Professorship in 2001. He received the Distinguished Alumnus Award from IIT Kanpur in 2008. His publications have received numerous best paper awards, including five test of time awards - the Longuet-Higgins Prize for papers published at CVPR (twice) and the Helmholtz Prize for papers published at ICCV (three times). He received the 2013 IEEE PAMI-TC Distinguished Researcher in Computer Vision Award, the 2014 K.S. Fu Prize from the International Association of Pattern Recognition, the 2016 ACM-AAAI Allen Newell Award, the 2018 IJCAI Award for Research Excellence in AI, and the 2019 IEEE Computer Society Computer Pioneer Award. He is a fellow of the IEEE and the ACM. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/customers?itm_data=hp-promo-customerlogos-seeallcustomers | Databricks Customer Stories | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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Read moreExplore all customersThe Data Team EffectData teams are the united force that are solving the world’s toughest problems.Be the next success storyContact usResourcesCustomer StoryLearn how Databricks enables Condé Nast to deliver personalized content to its customers.Learn moreWebinarLearn how Apple and Disney+ unified analytics and AI for successWatch nowPodcastHear about the role of data and AI in healthcare equity from the CDAO at HumanaWatch nowReady to get started?Try for freeContact usProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/company/partners/consulting-and-si/partner-solutions/accenture-cloud-data-migration | Cloud Data Migration by Accenture | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
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Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWBrickbuilder SolutionCloud Data Migration by AccentureMigration solution developed by Accenture and powered by the Databricks Lakehouse PlatformGet startedLess guesswork, more valueAccenture’s Cloud Data Migration helps you navigate any complexity, from building landing zones in the Cloud Continuum to regulating data sovereignty. Accenture works with you to determine the right cloud strategy, operating model, roadmap and additional ecosystem partners. They then help you accelerate migration and modernization to the cloud so that it is secure, cost-effective and agile. Accenture’s comprehensive cloud migration framework brings industrialized capabilities together with exclusive preconfigured, industry-specific tools, methods and automation across all cloud models and proven delivery methods. With Accenture’s Cloud Data Migration Service, you will benefit from:Increased innovation, agility and flexibilityEasing of rising resource demands and better consumption managementReduction in cost, immediate business results and cloud scalabilityGet startedDeliver AI innovation faster with solution accelerators for popular industry use cases. See our full library of solutions ➞ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/it/p/whitepaper/mit-cio-vision-2025?itm_data=home-promocard2-mit-cio-vision-2025 | Visione dei CIO al 2025: Colmare il divario fra BI e AI - DatabricksReportVisione dei CIO al 2025Colmare il divario fra BI e AIIndagine globale fra i CIO sull'adozione dell'AI orientata al valore aziendaleScopri come le principali imprese e organizzazioni stanno risolvendo le sfide più complesse di gestione dei dati per sfruttare al meglio l'AI. Il nuovo studio di MIT Technology Review raccoglie le opinioni di 600 CIO in 18 Paesi e 14 settori industriali.Come operano i CIO per dare priorità all'adozione dell'AI? Come investono nel miglioramento della loro strategia di gestione dei dati?Trova le risposte a queste domande nelle interviste approfondite con alti dirigenti (C-level) di Procter & Gamble, Johnson & Johnson, Cummins, Walgreens, S&P Global, Marks & Spencer e altre aziende.I CIO hanno evidenziato alcuni trend principali:secondo il 72%, i dati sono la sfida più grande per l'AI, mentre il 68% ritiene cruciale unificare la piattaforma di gestione dei dati per analisi e AIil 94% dichiara di utilizzare già l'AI in alcune linee di business e oltre la metà prevede che l'AI si diffonda su larga scala entro il 2025il 72% ritiene che il multicloud sia fondamentale e molti sostengono l'utilizzo di standard aperti per mantenere una flessibilità strategicaRichiedi il reportProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineSoluzioniPer settoreServizi professionaliSoluzioniPer settoreServizi professionaliChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiPosizioni aperte
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https://www.databricks.com/dataaisummit/speaker/manbir-paul/# | Manbir Paul - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingManbir PaulVP of Engineering, Data Insights and MarTech at SephoraBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/resources/ebook/data-analytics-and-ai-governance | Data, Analytics and AI Governance | DatabrickseBookData, Analytics and AI GovernanceHow to build an effective governance strategy for your data lakehouseData, analytics and AI governance is perhaps the most important yet challenging aspect of any data and AI democratization effort. For your data analytics and AI needs, you’ve probably deployed two different systems — data warehouses for business intelligence and data lakes for AI. And now you’ve created data silos with data movement across two systems, each with a different governance model.But data isn’t limited to files or tables. You also have assets like dashboards, ML models, and notebooks, each with their own permission models, making it difficult to manage access permissions for all these assets consistently.The problem gets bigger when your data assets exist across multiple clouds with different access management solutions. What you need is a unified approach to simplify governance for all your data on any cloud.Read this eBook to find out:Why your organization needs a modern approach to data governance that covers the full breadth of data use cases — from BI to MLWhat the key components are for a successful data governance frameworkData governance best practices for a data lakehouseHow you can unify governance for your data, analytics and AI use cases with the Databricks Lakehouse PlatformGet the eBookProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/dataaisummit/speaker/faraz-yasrobi | Faraz Yasrobi - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFaraz YasrobiSoftware Engineer at GrammarlyBack to speakersFaraz Yasrobi s a software engineer with over 7 years of experience in the data domain. Specializing in data infrastructure, engineering, storage, governance, and security. Faraz is currently a tech lead in Grammarly’s data platform team and has been leading the data democratization project over the past two years.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/weston-hutchins/# | Weston Hutchins - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingWeston HutchinsProduct Manager at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/blog/2019/12/18/make-your-data-lake-ccpa-compliant.html | CCPA Compliance and Data Lakes: Guide to Protecting Data PrivacySkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCategoriesAll blog postsCompanyCultureCustomersEventsNewsPlatformAnnouncementsPartnersProductSolutionsSecurity and TrustEngineeringData Science and MLOpen SourceSolutions AcceleratorsData EngineeringTutorialsData StreamingData WarehousingData StrategyBest PracticesData LeaderInsightsIndustriesFinancial ServicesHealth and Life SciencesMedia and EntertainmentRetailManufacturingPublic SectorMake Your Data Lake CCPA Compliant with a Unified Approach to Data and Analyticsby Raela Wang, Justin Olsson, Jeffrey Hirschey and Michael OrtegaDecember 18, 2019 in ProductShare this postWith more digital data being captured every day, there has been a rise of various regulatory standards such as the General Data Protection Regulation (GDPR) and recently the California Consumer Privacy Act (CCPA). These privacy laws and standards are aimed at protecting consumers from businesses that improperly collect, use, or share their personal information, and is changing the way businesses have to manage and protect the consumer data they collect and store.Similar to the GDPR, the CCPA empowers individuals to request:what personal information is being captured,how personal information is being used, andto have that personal information deleted.Additionally, the CCPA encompasses information about ‘households’. This has potential to significantly expand the scope of personal information subject to these requests. Failure to comply in a timely manner can result in statutory fines and statutory damages (where a consumer need not even prove damages) that can rise quickly. The challenge for companies doing business in California or otherwise subject to the CCPA, then, is to ensure they can quickly find, secure, and delete that personal information.Many companies wrongfully think that the data privacy processes and controls put in place for GDPR compliance will guarantee complete compliance with the CCPA–and while the things you may have done to prepare for the GDPR are helpful and a great start–they are unlikely to be sufficient. Companies need to focus on understanding their need for compliance and must determine which processes and controls can effectively prevent the misuse and unauthorized sale of consumer data.Are you prepared for CCPA?CCPA requires businesses to potentially delete all personal information about a consumer upon request. Many organizations today are using or plan to use a data lake for storing the vast majority of their data in order to have a comprehensive view of their customers and business and power downstream data science, machine learning, and business analytics. The lack of structure of a data lake makes it challenging to locate and remove individual records to remain compliant with these regulatory requirements.This is critical when responding to a consumer’s deletion request, and if a business receives more than just a few consumer rights requests in a short period of time, the resources spent to comply with the requests could be significant. Businesses that fail to comply with CCPA requirements by January 1, 2020 could be subject to lawsuits and civil penalties. The CCPA also contains a “lookback” period applying it to actions and personal information since January 1, 2019, making it vital to get these solutions in place quickly.Taking your data security beyond the data lakeWhen it comes to adhering to CCPA requirements, your data lake should enable you to respond to consumer rights requests within prescribed timelines without handicapping your business. Unfortunately, most data lakes lack the data management and data manipulation capabilities to quickly locate and remove records, which makes this challenging.Fortunately, Databricks offers a solution. The Databricks Unified Data Analytics Platform simplifies data access and engineering, while fostering a collaborative environment that supports analytics and machine learning. As part of the platform, Databricks offers a Unified Data Service that ensures reliability and scalability for your data pipelines, data lakes, and data analytics workflows.One of the main components of the Databricks Unified Data Service is Delta Lake, an open-source storage layer that brings enhanced data reliability, performance, and lifecycle management to your data lake. With improved data management, organizations can start to think “beyond the data lake” and leverage more advanced analytics techniques and technologies to extend their data for downstream business needs including data privacy protection and CCPA compliance.Start building a CCPA-friendly data lake with Delta LakeDelta Lake provides your data lake with a structured data management system including transactional capabilities. This enables you to easily and quickly search, modify, and clean your data using standard DML statements (e.g. DELETE, UPDATE, MERGE INTO).To get started, ingest your raw data with the Spark APIs that you’re familiar with and write them out as Delta Lake tables. Doing this also adds metadata to your files. If your data is already in Parquet format, you also have the option to convert the parquet files in place to a Delta Lake table without rewriting any of the data. Delta uses an open file format (parquet) so there are no worries of being locked in as you can quickly and easily convert your data back into another format if you need to.Once ingested, you can easily search and modify individual records within your Delta Lake tables. The final step is to make Delta Lake your single source of truth by erasing any underlying raw data. This removes any lingering records from your raw data sets. We suggest setting up a retention policy with AWS or Azure of thirty days or less to automatically remove raw data so that no further action is needed to delete the raw consumer data to meet CCPA response timelines.How do I delete data in my data lake using Delta Lake?You can find and delete any personal information related to a consumer by running two commands:DELETE FROM data WHERE email = '[email protected]';VACUUM data;The first command identifies records that contain the string “[email protected]” stored in the column email, and deletes the data containing these records by rewriting the respective underlying files with the consumer’s unique personal data removed and marking the old files as deleted.The second command cleans up the Delta table, removing any stale records that have been logically deleted and are outside of the default retention period. With the default retention period of 7 days, this means that files marked for deletion will linger around until you run the VACUUM command at least 7 days later. You could easily set up a scheduled job with the Databricks Job Scheduler to run the VACUUM command for you in an automated fashion. You might also be familiar with Delta Lake’s time travel capabilities, which allows you to keep historical versions of your Delta Lake table in case you need to query an earlier version of the table. Note that when you run VACUUM, you will lose the ability to time travel back to a version older than the default 7-day data retention period.After running these commands, you can now safely state that you have removed the necessary consumer data and records from your data lake.How else does Databricks help me with CCPA consumer rights requests?Once a user's personal information has been removed from the data lake, it is also important to remove this personal information from the tools used by your data teams. Often times these tools reside locally on the data scientist’s or engineer’s laptop. A better more secure solution is to use Databricks with its hosted Data Science Workspace where data teams can prep, explore and model data collaboratively in a shared notebook environment. This improves team productivity while creating a secured, centralized environment for the entire analytics workflow.To help you meet CCPA compliance requirements, Databricks provides you with privacy protection tools to permanently remove personal information, either on a per-command or per-notebook level.After you delete a notebook, it is moved to trash. If you don’t take further action, it will be permanently deleted within 30 days - allowing you to be confident it has been deleted within the prescribed timelines for both CCPA and GDPR.If for any reason you need to do this more quickly, we also offer the ability to permanently delete individual items in the trash:deleting all items in a particular user’s trash:or purging all deleted items in a workspace on command, which includes deleted notebook cells, notebook comments or MLFlow experiments:You also have the option to purge Databricks notebook revision history, which is useful to ensure that old query results are permanently deleted:Getting Started with CCPA Compliance for Data and AnalyticsWith the Databricks Unified Data Analytics Platform and Delta Lake, you can bring enhanced data security, reliability, performance, and lifecycle management to your data lake while delivering on all your analytics needs. Organizations can now quickly find and remove individual records from a data lake to meet CCPA access requests and compliance requirements without hindering their business.Learn more about Delta Lake and the Databricks Unified Data Analytics Platform. Sign-up for your free Databricks trial now.Try Databricks for freeGet StartedSee all Product postsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/br/solutions/accelerators | Aceleradores de soluções da Databricks – Entregando valor de dados e IA com mais rapidez – DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT
Adeus, Data Warehouse. Olá, Lakehouse.
Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna.
Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT
Descubra a Lakehouse para Manufatura
Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWSoluções para cada setorForneça os dados e os resultados de IA que mais importam com mais rapidezComece sua avaliação gratuitaAceleradores de soluções da DatabricksEconomize horas de processo de descoberta, design, desenvolvimento e testes com os aceleradores de soluções da Databricks. Nossos guias dedicados — notebooks totalmente funcionais e práticas recomendadas — aceleram os resultados para seus casos de uso mais comuns e impactantes. Vá da ideia à prova de conceito (PoC) em apenas duas semanas.
Comece a usar os aceleradores de soluções com sua avaliação gratuita da Databricks ou conta existente.
Comece sua avaliação gratuita hoje mesmoExplorar aceleradoressearchHide filtersIndustrySortDatabricksRemoção automatizada de PHIfeaturednewDatabricksPrevisões de demanda refinadasfeatured🔥DatabricksDisponibilidade nas prateleirasfeaturedDatabricksDetecção de toxicidade em jogosfeaturedDatabricksAjuste de riscos do MedicareSplunkAnálise cibernética (Conector Splunk)DatabricksAnálise de desempenho ESG🔥DatabricksAnálise de imagem de patologia digitalDatabricksAnálise de sobrevivência e valor do tempo de vidaDatabricksAnálises de ponto de venda em tempo realnewDatabricksAnálises geoespaciais para identificar fraudesDatabricksAtribuição multi-touchDatabricksClassificação de comerciantesDatabricksClassificação de propensãonewDatabricksCombate à lavagem de dinheiroDatabricksConstrução de coortes usando gráficos de conhecimentoDatabricksCorrespondência aproximada de itensDatabricksDetecção de ameaças com DNSDatabricksDetecção de efeitos colaterais de medicamentosnewDatabricksDeterminantes sociais da saúdeDatabricksEficácia geral do equipamentoDatabricksEstoque de segurançaDatabricksEstudos de associação em todo o genomaDatabricksEvidências do mundo realDatabricksFundamentos da visão computacionalDatabricksGêmeos digitaisDatabricksGeração de rotas escaláveisDatabricksGerenciamento de retençãoDatabricksGerenciamento de riscosDatabricksInteroperabilidade de FHIR com dbigniteDatabricksInteroperabilidade de HL7v2 com SmolderDatabricksManutenção preditiva (IoT)DatabricksMecanismo de recomendaçãoDatabricksModernização de plataformas de investimentoDatabricksOtimização da separação de pedidosDatabricksOtimização de licitações em tempo realDatabricksOtimização de P&D com gráficos de conhecimentonewDatabricksPrevenção de fraudes financeiras em tempo realDatabricksPrevisão de perda de assinantesDatabricksPrevisão de vendas e atribuição de anúncios🔥DatabricksQualidade de experiência em vídeoDatabricksRelatórios regulatóriosDatabricksResolução da entidade do clienteDatabricksRisco de reputaçãoDatabricksSegmentação de clientesDatabricksSíntese de dados reais para oncologiaDatabricksTransparência de preçosnewDatabricksValor vitalício do cliente🔥Perguntas frequentesQuanto custam os aceleradores de solução?Os aceleradores de solução estão disponíveis gratuitamente para todos os clientes Databricks.Preciso ser um cliente Databricks para usar um acelerador de solu ções?Você pode implementar os aceleradores de soluções com uma avaliação gratuita da Databricks ou com sua conta existente.O que esperar de um acelerador de soluções?Os aceleradores de soluções foram criados para ajudar você a economizar tempo nos processos de descoberta, design, desenvolvimento e testes. Nosso objetivo: acelerar o início de seus casos de uso de dados e IA fornecendo os recursos apropriados (notebooks, modelos comprovados e práticas recomendadas). Você pode ir da ideia à prova de conceito (PoC) em apenas duas semanas.Veja como nossos clientes usam a DatabricksExplore histórias de clientesProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers
at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Aviso de privacidade|Termos de Uso|Suas opções de privacidade|Seus direitos de privacidade na Califórnia |
https://www.databricks.com/dataaisummit/speaker/matteo-quattrocchi/# | Matteo Quattrocchi - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingMatteo QuattrocchiDirector, Policy-EMEA at BSABack to speakersMatteo is Director, Policy — EMEA at BSA | The Software Alliance in Brussels. In this role, he works with BSA members to develop and advance policy positions on a range of key issues, with a focus on artificial intelligence, copyright and government access to data. Prior to joining BSA, Matteo worked at the U.S. Mission to the EU in the Public Affairs Office, and in lobbying and law firms. Matteo holds an LL.M. from Georgetown University, and a Master’s in European Law at LUISS (Italy).Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/br/solutions/industries/manufacturing-industry-solutions | Soluções para o setor de manufatura – DatabricksSkip to main contentPlataformaDatabricks Lakehouse PlatformDelta LakeGovernança de dadosData EngineeringStreaming de dadosArmazenamento de dadosData SharingMachine LearningData SciencePreçosMarketplaceTecnologia de código abertoCentro de segurança e confiançaWEBINAR Maio 18 / 8 AM PT
Adeus, Data Warehouse. Olá, Lakehouse.
Participe para entender como um data lakehouse se encaixa em sua pilha de dados moderna.
Inscreva-se agoraSoluçõesSoluções por setorServiços financeirosSaúde e ciências da vidaProdução industrialComunicações, mídia e entretenimentoSetor públicoVarejoVer todos os setoresSoluções por caso de usoAceleradores de soluçãoServiços profissionaisNegócios nativos digitaisMigração da plataforma de dados9 de maio | 8h PT
Descubra a Lakehouse para Manufatura
Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWDescubra o Lakehouse para manufaturaReduza custos, aumente a produtividade e unifique seu ecossistema de dados na única plataforma de dados criada para a maneira como os fabricantes usam os dadosInscrever-seEntre em contatoMenor TCO. Melhor desempenho. Maior escalabilidade.Lakehouse para manufaturaQuando você pode unificar todos os seus dados, funções analíticas e cargas de trabalho de IA — com compartilhamento e governança integrados — equipes internas e externas têm acesso aos dados de que precisam, quando precisam.Impacto em toda a cadeia de valorEngajamento do clienteResultados precisos e experiências sem atritos para os clientesCom uma visão de 360 graus dos clientes, operações e ativos, você pode oferecer o mais alto tempo de atividade, qualidade de serviço e valor econômico em todo o ciclo de vida do produto, potencializando resultados personalizados para os clientes, prestação proativa de serviços de campo e soluções diferenciadas de missão crítica.Eficiência operacionalProdutividade dos funcionáriosInovação de produtosSoluções e parceiros de produçãoSoluções de análises de dados e IA sem compromisso criadas especificamente para fabricantesOs aceleradores de soluções da Databricks são guias criados especificamente para acelerar os resultados de fabricação usando notebooks totalmente funcionais e práticas recomendadas. Economize tempo de descoberta, design, desenvolvimento e testes em casos de uso, como gêmeos digitais, eficácia geral do equipamento, previsão e muito mais.Eficácia geral do equipamento e monitoramento de KPIObtenha monitoramento de equipamentos de ponta a ponta com alto desempenho e escalabilidade
Ingira e processe dados incrementalmente de sensores/dispositivos IoT em diversos formatos e calcule KPIs e métricas de superfície para gerar insights valiosos.Comece agoraPrevisão em nível parcialPreveja a demanda no nível da peça para uma fabricação simplificada
Realize a previsão de demanda no nível da peça, em vez do nível agregado, para minimizar as interrupções em sua cadeia de suprimentos e aumentar as vendas.Comece agoraGêmeos digitaisAumente a eficiência operacional e melhore a tomada de decisões
Processe dados do mundo real em tempo real, calcule insights em grande escala e entregue múltiplas aplicações downstream, além de otimizar as operações da fábrica com decisões baseadas em dados.Comece agoraExplore os aceleradores de manufaturaTemos parceria com as principais empresas de consultoria para oferecer soluções inovadoras e dedicadas a cada setor de negócios. As soluções Databricks Brickbuilder ajudam a reduzir custos e maximizar o valor dos seus dados. Elas são apoiadas por décadas de experiência no setor e projetadas para a Plataforma Databricks Lakehouse para atender às suas necessidades.Manufatura inteligente Use seus dados, estimule a interoperabilidade e forneça insights aprimorados em escala usando funções analíticas e IA.Saiba maisInspetor de qualidadeAutomatize seu controle de qualidade com visão do computador para detectar defeitos, objetos estranhos, anomalias ou configuração incorreta.Saiba maisGestão preditiva de risco de fornecimentoImpulsione a visibilidade granular dos fluxos de pedidos e desempenho dos fornecedores para aumentar a eficiência, gerenciar exceções e melhorar a resiliência.Saiba maisVer todas as soluções de parceiros"O Databricks Lakehouse nos permite reduzir a barreira de entrada para o acesso aos dados em toda a nossa organização. Com isso, podemos construir os veículos elétricos mais inovadores e confiáveis do mundo."
– Wassym Bensaid, vice-presidente de desenvolvimento de software da Rivian
“Ao longo dos anos, o uso da Databricks aumentou consideravelmente. No início, a Databricks era nossa plataforma de big data e IA, mas o escopo foi ampliado. Uma classe totalmente nova de engenheiros e data scientists cidadãos está usando hoje como uma ferramenta moderna de business intelligence para tomar decisões de negócios com mais informações.”
— Daniel Jeavons, Diretor Geral, Centro de Excelência para Análises Avançadas, Shell
"A plataforma Databricks nos ajudou a minimizar os riscos à disponibilidade de motores, reduzir os prazos de entrega de peças de reposição e gerar mais eficiência no giro de estoque. Tudo isso nos permite fornecer o TotalCare, o principal programa de manutenção Power-by-the-Hour (PBH) do setor de aviação."
— Stuart Hughes, diretor de informação e digital da Rolls-Royce Civil Aerospace
Recursose-booksColoque seus dados de ERP para trabalharWebinarsMelhorar a manutenção preditiva para fabricantes com dados e IAe-booksQuatro forças impulsionando a produção inteligenteTudo pronto para começar?Adoraríamos saber seus objetivos de negócios. Nossa equipe de serviços fará todo o possível para ajudar sua empresa a ter sucesso.Experimente o Databricks gratuitamenteEntre em contatoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoProdutoVisão geral da plataformaPreçosTecnologia de código abertoExperimente DatabricksDemoAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineAprendizagem e suporteDocumentaçãoGlossárioTreinamento e certificaçãoCentral de ajudaInformações legaisComunidade onlineSoluçõesPor setorServiços profissionaisSoluçõesPor setorServiços profissionaisEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoEmpresaQuem somosCarreiras em DatabricksDiversidade e inclusãoBlog da empresaEntre em contatoSee Careers
at DatabricksMundialEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Aviso de privacidade|Termos de Uso|Suas opções de privacidade|Seus direitos de privacidade na Califórnia |
https://www.databricks.com/dataaisummit/speaker/rahul-pandey/# | Rahul Pandey - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRahul PandeySolution Architect at adidasBack to speakersRahul is working on Data Engineering and Data Science projects as a Solution Architect at Adidas. His goal is to build cost-effective and efficient architecture designs. He is motivated to raise awareness about sustainability in AI within Data Science teams.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/julien-le-dem/# | Julien Le Dem - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJulien Le DemChief Architect at AstronomerBack to speakersJulien Le Dem is the Chief Architect of Astronomer and Co-Founder of Datakin. He co-created Apache Parquet and is involved in several open source projects including OpenLineage, Marquez (LFAI&Data), Apache Arrow, Apache Iceberg and a few others. Previously, he held senior roles at Wework, Dremio, Twitter where he also obtained a two-character Twitter handle (@J_) and Yahoo, where he received his Hadoop initiation. His French accent makes his talks particularly attractive.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/erni-durdevic | Erni Durdevic - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingErni DurdevicSpecialist Solutions Architect (Geospatial) at DatabricksBack to speakersErni is a Big Data specialist helping Databricks customers to develop breakthrough applications at scale.
He is specialised in Data Engineering, focusing on geospatial and time series data workloads. He is the co-creator of DiscoverX, an active contributor to the open-source geospatial library Mosaic, and an enthusiastic member of the Geospatial SME group at Databricks.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/sarah-pollitt | Sarah Pollitt - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingSarah PollittGroup Product Manager at MatillionBack to speakersSarah has over 10 years experience in data management and software delivery in a wide variety of major industries.
Sarah is passionate about supporting people make their lives easier with data, supporting women in tech and ensuring equity in the Product and Technology spaceLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/xuefu-wang/# | Xuefu Wang - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingXuefu WangSr. Data Scientist at The Trade DeskBack to speakersXuefu Wang is a Sr. Data Scientist at The Trade Desk, the world's largest demand-side platform for accessing premium advertisement inventories across multiple channels. He has a PhD in statistics and previously worked in data science at JP Morgan ChaseLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/kr/product/aws?itm_data=menu-item-awsProduct | AWS 기반 Databricks 데이터 플랫폼 - DatabricksSkip to main content플랫폼Databricks 레이크하우스 플랫폼Delta Lake데이터 거버넌스데이터 엔지니어링데이터 스트리밍데이터 웨어하우징데이터 공유머신 러닝데이터 사이언스가격Marketplace오픈 소스 기술보안 및 신뢰 센터웨비나 5월 18일 / 오전 8시(태평양 표준시)
안녕, 데이터 웨어하우스. 안녕하세요, 레이크하우스입니다.
데이터 레이크하우스가 최신 데이터 스택에 어떻게 부합하는지 이해하려면 참석하십시오.
지금 등록하세요솔루션산업별 솔루션금융 서비스의료 서비스 및 생명 공학제조커뮤니케이션, 미디어 및 엔터테인먼트공공 부문리테일모든 산업 보기사용 사례별 솔루션솔루션 액셀러레이터프로페셔널 서비스디지털 네이티브 비즈니스데이터 플랫폼 마이그레이션5월 9일 | 오전 8시(태평양 표준시)
제조업을 위한 레이크하우스 살펴보기
코닝이 수동 검사를 최소화하고 운송 비용을 절감하며 고객 만족도를 높이는 중요한 결정을 내리는 방법을 들어보십시오.지금 등록하세요학습관련 문서교육 및 인증데모리소스온라인 커뮤니티University Alliance이벤트Data + AI Summit블로그LabsBeacons2023년 6월 26일~29일
직접 참석하거나 키노트 라이브스트림을 시청하세요.지금 등록하기고객파트너클라우드 파트너AWSAzureGoogle CloudPartner Connect기술 및 데이터 파트너기술 파트너 프로그램데이터 파트너 프로그램Built on Databricks Partner Program컨설팅 & SI 파트너C&SI 파트너 프로그램파트너 솔루션클릭 몇 번만으로 검증된 파트너 솔루션과 연결됩니다.자세히회사Databricks 채용Databricks 팀이사회회사 블로그보도 자료Databricks 벤처수상 실적문의처Gartner가 Databricks를 2년 연속 리더로 선정한 이유 알아보기보고서 받기Databricks 이용해 보기데모 보기문의처로그인JUNE 26-29REGISTER NOWAWS 기반 DatabricksAWS와 매끄럽게 통합되는 단순한 통합 데이터 플랫폼
시작하기데모 예약AWS 기반 Databricks를 사용하면 데이터 웨어하우스와 데이터 레이크의 장점을 결합한 간단한 개방형 레이크하우스 플랫폼에 모든 데이터를 저장하여 관리하고, 모든 분석 및 AI 워크로드를 통합할 수 있습니다.신뢰할 수 있는데이터 엔지니어링모든 데이터에서 SQL 분석 실행협업형 데이터 사이언스프로덕션 머신 러닝AWS에서 Databricks를 사용해야 하는 이유심플
Databricks는 S3에서 단일 통합형 데이터 아키텍처를 사용하여 SQL 분석, 데이터 사이언스 및 머신 러닝을 지원합니다.가격-성능 12배 향상
SQL 최적화 컴퓨팅 클러스터를 통해 데이터 레이크의 경제적인 가격으로 데이터 웨어하우스 성능을 확보하세요.검증된 성능
수천 곳의 고객사가 AWS에서 Databricks를 구현하여 모든 분석 및 AI 사용 사례를 다루는 획기적인 분석 플랫폼을 제공했습니다.Dollar Shave Club: Databricks로 고객 경험 개인화
eBook 다운로드Hotels.com: 머신 러닝으로 고객 경험 최적화
사례 연구 다운로드HP: 데이터 준비에서 딥 러닝까지 — HP에서 Databricks를 활용하여 분석을 통합한 방법
온디맨드 웨비나 시청추천 통합AWS GravitonAWS GravitonDatabricks 클러스터는 AWS Graviton 인스턴스를 지원합니다. 이들 인스턴스는 AWS에서 Arm64 명령 세트 아키텍처를 기반으로 설계한 Graviton 프로세서를 사용합니다. AWS에서는 이들 프로세서를 사용하는 인스턴스 유형은 Amazon EC2의 어떤 인스턴스 유형보다도 가장 우수한 가성비를 자랑한다고 말합니다. 자세히 읽기AWS SecurityAmazon RedshiftAWS Glue엔터프라이즈 롤아웃사용 사례개별 맞춤형 추천 엔진
실시간으로 모든 데이터를 처리하고 가장 관련이 깊은 제품과 서비스 추천을 제공합니다.유전체 서열 분석
기술 스택을 현대화하여 가장 빠른 대규모 DNASeq 파이프라인으로 환자 및 의사를 위한 환경을 개선합니다.사기 탐지 및 예방
실시간 데이터 스트림과 더불어 완전한 과거 데이터를 활용하여 익명 및 의심스러운 금융 거래를 빠르게 찾아냅니다.리소스백서데이터 레이크 마이닝을 통한 분석 인사이트금융 서비스 산업에서 빅데이터와 AI 통합하둡 마이그레이션의 숨겨진 가치웨비나Databricks 및 AWS로 신뢰할 수 있는 데이터 및 분석 플랫폼 현대화데이터 중심적 스타트업이 레이크하우스에서 구축하는 이유Quby에서 실시간 분석 기능으로 빠른 의사 결정 지원LoyaltyOne에서 Delta Lake로 데이터 분석 파이프라인 단순화 및 확장데이터 레이크의 잠재력 실현DoorDash 및 Grammarly의 데이터 레이크하우스 구축산업Prognos에서 AI/ML을 활용하여 모집단 규모 임상 실험 데이터에서 실용적인 인사이트 추출정부 기관에서 머신 러닝으로 데이터 분석을 혁신하는 방법시작할 준비가 되셨나요?Databricks 무료로 시작하기제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모제품플랫폼 개요가격오픈 소스 기술Databricks 이용해 보기데모학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티학습 및 지원관련 문서용어집교육 및 인증헬프 센터법적 고지온라인 커뮤니티솔루션산업 기준프로페셔널 서비스솔루션산업 기준프로페셔널 서비스회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처회사Databricks 소개Databricks 채용다양성 및 포용성회사 블로그문의처Databricks
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https://www.databricks.com/company/newsroom/press-releases/databricks-secures-strategic-investment-q-tel-deliver-cloud-based-apache-spark-platform | Databricks Secures Strategic Investment From In-Q-Tel to Deliver Cloud-Based Apache Spark PlatformPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks Secures Strategic Investment From In-Q-Tel to Deliver Cloud-Based Apache Spark PlatformPartnership Will Empower the U.S. Intelligence Community to Perform Critical Missions Using Apache Spark
June 21, 2016Share this postSAN FRANCISCO, CA--(Marketwired - Jun 21, 2016) - Databricks, the company behind Apache Spark, today announced a strategic partnership agreement with and investment from In-Q-Tel, Inc. (IQT). IQT is the investment organization that identifies innovative technologies to support the mission of the U.S. Intelligence Community. Through this strategic partnership, Databricks will extend Apache Spark's position as the leading processing engine built for speed, ease of use, and sophisticated analytics by delivering a cloud-based Spark platform that supports the operational requirements of the U.S. Intelligence Community.
Databricks is focused on the singular mission of making big data simple. The Databricks platform is a fully managed development and production system for advanced analytics powered by Spark. It empowers individuals and organizations to seamlessly transition from data ingest through exploration to production. Enterprises utilize Databricks to achieve a wide variety of objectives, including integrating disparate data silos, synthesizing actionable insights in real-time, and deploying advanced analytics solutions.
"Enterprises today know there is untapped value in their data, especially within the intelligence community, but selecting the tools to unlock that value is often a complex process. Databricks, powered by Apache Spark, is built for developers, data scientists, data engineers and other IT professionals to get up and running very quickly via one end-to-end platform," said Ion Stoica, Executive Chairman at Databricks. "This investment by IQT further validates Databricks' mission and reflects the need for big data simplicity and accessibility that we're seeing among several industries, including federal."
"A partnership with Databricks continues IQT's tradition of working with world-class analytics companies," said Dan Gwak, Partner, IQT Investments. "We are proud to partner with Databricks to advance the intelligence mission."
About In-Q-Tel
In-Q-Tel is the not-for-profit, strategic investor that works to identify, adapt, and deliver innovative technology solutions to support the missions of the U.S. Intelligence Community. Launched in 1999 as a private, independent organization, IQT's mission is to identify and partner with companies developing cutting-edge technologies that serve the national security interests of the United States. For more information, visit http://www.iqt.org.
About Databricks
Databricks' vision is to empower anyone to easily build and deploy advanced analytics solutions. The company was founded by the team who created Apache® Spark™, a powerful open source data processing engine built for sophisticated analytics, ease of use, and speed. Databricks is the largest contributor to the open source Apache Spark project providing 10x more code than any other company. The company has also trained over 20,000 users on Apache Spark, and has the largest number of customers deploying Spark to date. Databricks provides a just-in-time data platform, to simplify data integration, real-time experimentation, and robust deployment of production applications. Databricks is venture-backed by Andreessen Horowitz and NEA. For more information, contact [email protected].
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Saiba como a Corning está tomando decisões críticas que minimizam as inspeções manuais, reduzem os custos de envio e aumentam a satisfação do cliente.Inscreva-se hojeAprenderDocumentaçãoTreinamento e certificaçãoDemosRecursosComunidade onlineAliança com universidadesEventosData+AI SummitBlogLaboratóriosBeaconsA maior conferência de dados, análises e IA do mundo retorna a São Francisco, de 26 a 29 de junho. ParticipeClientesParceirosParceiros de nuvemAWSAzureGoogle CloudConexão de parceirosParceiros de tecnologia e dadosPrograma de parceiros de tecnologiaPrograma de parceiros de dadosBuilt on Databricks Partner ProgramParceiros de consultoria e ISPrograma de parceiros de C&ISSoluções para parceirosConecte-se com apenas alguns cliques a soluções de parceiros validadas.Saiba maisEmpresaCarreiras em DatabricksNossa equipeConselho de AdministraçãoBlog da empresaImprensaDatabricks VenturesPrêmios e reconhecimentoEntre em contatoVeja por que o Gartner nomeou a Databricks como líder pelo segundo ano consecutivoObtenha o relatórioExperimente DatabricksAssista às DemosEntre em contatoInício de sessãoJUNE 26-29REGISTER NOWLakehouse para empresas nascidas na nuvemCrie recursos de dados, análises e IA e dimensione-os mais rapidamente em uma única plataforma de dados de alto desempenho
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https://www.databricks.com/resources/ebook/a-new-approach-to-data-sharing | A New Approach to Data Sharing | DatabrickseBookShare live data across platformsExplore the new Delta Sharing solutionHomegrown data-sharing solutions based on SFTP or APIs aren’t scalable and saddle you with operational overhead. Off-the-shelf data-sharing solutions only work on specific sharing networks, promoting vendor lock-in and can be costly. Databricks Delta Sharing provides an open solution to securely share live data from your lakehouse to any computing platform without any replication so you can reach your customers where they are. Get the whole story in this eBook.You’ll discover how you can:Share live data from where it lives, without replicating or moving it to another systemEasily share existing data in Delta Lake and Apache Parquet formats. Recipients don’t have to be on the Databricks platform, the same cloud or a cloud at all.Drive collaboration with your partners, suppliers and lines of businessMeet governance, security and compliance needs while sharing assetsAccelerate time to value by consuming shared data directly from the tools of your choiceGet the eBookProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/dataaisummit/speaker/rob-saker | Rob Saker - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRob SakerGlobal VP, Retail and Manufacturing at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/jeffrey-hess/# | Jeffrey Hess - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJeffrey HessLead Technologist at Booz Allen HamiltonBack to speakersJeff is a Lead Technologist at Booz Allen Hamilton responsible for standardizing enterprise data environments and has led multiple large-scale transformation projects to help stakeholders make the most of their data. Jeff oversees the entire data ecosystem, from moving and storing data, transforming and prepping data, visualizing data, and securing data. Jeff began as a business intelligence developer and continued to grow his passion for analytics, leading to a diverse skillset that sets his clients up for success.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/p/databricks-weekly-demo | Databricks Events | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks EventsExplore upcoming Databricks meetups, webinars, conferences and more.Join Generation AIJune 26–29Learn about the latest innovations with LLMs like Dolly and other open source Data + AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingRegister nowLoading...Browse All Upcoming EventsProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/it/product/databricks-sql | Databricks SQL | DatabricksSkip to main contentPiattaformaThe Databricks Lakehouse PlatformDelta LakeGovernance dei datiIngegneria dei datiStreaming di datiData warehouseCondivisione dei datiMachine LearningData SciencePrezziMarketplaceTecnologia open-sourceSecurity and Trust CenterWEBINAR 18 maggio / 8 AM PT
Addio, Data Warehouse. Ciao, Lakehouse.
Partecipa per capire come una data lakehouse si inserisce nel tuo stack di dati moderno.
Registrati oraSoluzioniSoluzioni per settoreServizi finanziariSanità e bioscienzeIndustria manifatturieraComunicazioni, media e intrattenimentoSettore pubblicoretailVedi tutti i settoriSoluzioni per tipo di applicazioneAcceleratoriServizi professionaliAziende native digitaliMigrazione della piattaforma di dati9 maggio | 8am PT
Scopri la Lakehouse for Manufacturing
Scopri come Corning sta prendendo decisioni critiche che riducono al minimo le ispezioni manuali, riducono i costi di spedizione e aumentano la soddisfazione dei clienti.Registrati oggi stessoFormazioneDocumentazioneFormazione e certificazioneDemoRisorseCommunity onlineUniversity AllianceEventiConvegno Dati + AIBlogLabsBeacons
26–29 giugno 2023
Partecipa di persona o sintonizzati per il live streaming del keynoteRegistratiClientiPartnerPartner cloudAWSAzureGoogle CloudPartner ConnectPartner per tecnologie e gestione dei datiProgramma Partner TecnologiciProgramma Data PartnerBuilt on Databricks Partner ProgramPartner di consulenza e SIProgramma partner consulenti e integratori (C&SI)Soluzioni dei partnerConnettiti con soluzioni validate dei nostri partner in pochi clic.RegistratiChi siamoLavorare in DatabricksIl nostro teamConsiglio direttivoBlog aziendaleSala stampaDatabricks VenturesPremi e riconoscimentiContattiScopri perché Gartner ha nominato Databricks fra le aziende leader per il secondo anno consecutivoRichiedi il reportProva DatabricksGuarda le demoContattiAccediJUNE 26-29REGISTER NOWPromozione per Databricks SQL — Risparmia oltre il 40%Approfitta della promozione di 15 mesi su Serverless SQL e sul nuovissimo SQL ProMaggiori informazioniDatabricks SQLIl data warehouse migliore è il lakehouseCominciaGuarda la demo
WEBINAR • Goodbye, Data Warehouse. Hello, Lakehouse.
Attend on May 18 and get a $100 credit toward a Databricks certification course
Register nowDatabricks SQL (DB SQL) è un data warehouse serverless su Databricks Lakehouse Platform che consente di eseguire tutte le applicazioni SQL e BI su larga scala, con un rapporto prezzo/prestazioni fino a 12 volte migliore, un modello di governance unificato, formati e API aperti, e i tuoi strumenti preferiti... senza vincoli.Miglior rapporto prezzo/prestazioniRiduci i costi, ottieni il miglior rapporto prezzo/prestazioni ed elimina la necessità di gestire, configurare o scalare l'infrastruttura cloud con tecnologie serverless.Governance integrataCrea un'unica copia di tutti i dati utilizzando standard aperti e un livello di governance unificato per tutti i team di gestione dei dati utilizzando SQL standard.Ricco ecosistemaUtilizzando SQL e strumenti come Fivetran, dbt, Power BI o Tableau insieme a Databricks, tutti i dati possono essere acquisiti, trasformati e interrogati sul posto.Abbatti i silosOgni analista sarà in grado di accedere più velocemente ai dati più recenti per effettuare analisi a valle in tempo reale e passare senza difficoltà da BI a ML.Come funziona?Integrazioni dirette con l'ecosistema Facilità d'uso Prestazioni nel mondo reale Governance centralizzata Un data lake aperto e affidabile come fondamentaAcquisisci, trasforma e orchestra facilmente i dati da qualsiasi luogoLavora con i tuoi dati ovunque si trovino. Le funzionalità "chiavi in mano" consentono ad analisti e tecnici dell'analisi di acquisire facilmente i dati da qualsiasi sorgente, da sistemi di storage in cloud ad applicazioni aziendali come Salesforce, Google Analytics o Marketo, utilizzando Fivetran. Basta un clic. Poi si possono gestire facilmente le dipendenze e trasformare i dati sul posto con funzionalità ETL integrate sul lakehouse, oppure utilizzando i propri strumenti preferiti come dbt su Databricks SQL per avere il massimo delle prestazioni. “La combinazione fra Databricks e Fivetran ci ha consentito di costruire una pipeline di dati moderna e robusta in pochissimo tempo. Fivetran aveva tutti i connettori e le integrazioni che ci servivano.”— Justin Wille, Director of Insights and Analytics, Kreg ToolMaggiori informazioniAnalisi e BI moderne con i tuoi strumenti preferitiLavora in modo completamente integrato con gli strumenti BI più diffusi come Tableau, Power BI e Looker. Ora gli analisti possono utilizzare i loro strumenti preferiti per ricavare nuove informazioni approfondite e dettagliate dai dati più completi e aggiornati. Databricks SQL consente a ogni analista di cercare, trovare e condividere velocemente nuove informazioni attraverso l'editor SQL integrato, visualizzazioni e dashboard. “Avendo i dati a portata di mano, siamo molto più fiduciosi perché sappiamo di utilizzare i dati più recenti e completi per alimentare i nostri dashboard e report di Power BI.”— Jake Stone, Senior Manager, Business Analytics, ButcherBoxMaggiori informazioniElimina la gestione delle risorse con il calcolo serverlessDatabricks SQL serverless elimina la necessità di gestire, configurare o scalare l'infrastruttura cloud sul lakehouse, liberando il team di gestione dei dati per svolgere le mansioni per cui è più idoneo. I warehouse SQL di Databricks forniscono un'elaborazione SQL istantanea ed elastica, scollegata dallo storage, e si ridimensionano automaticamente per offrire concorrenze illimitate senza interruzioni, per gestire un numero elevato di casi d'uso contemporanei. “Databricks SQL Serverless ci consente di sfruttare la potenza di Databricks SQL, oltre a essere molto più efficiente con la nostra infrastruttura.”— R. Tyler Croy, Director of Platform Engineering, ScribdMaggiori informazioniConcepito fin dall'inizio per ottenere il massimo delle prestazioniDatabricks SQL contiene migliaia di ottimizzazioni per offrire le prestazioni migliori per tutti gli strumenti, i tipi di query e le applicazioni nel mondo reale. È compreso il motore di query vettorializzato di nuova generazione Photon che, insieme ai warehouse SQL, offre un rapporto prezzo/prestazioni fino a 12 volte migliore rispetto ad altri data warehouse in cloud. “La Databricks Lakehouse Platform ci consente di effettuare analisi che riducono da settimane a minuti i tempi necessari per ricavare informazioni sul comportamento del pubblico radiotelevisivo.”— Stephane Caron, Sr. Director of Business Intelligence, CBC/Radio-CanadaMaggiori informazioniConserva e gestisci tutti i dati centralmente con SQL standardCrea un'unica copia di tutti i dati utilizzando il formato aperto Delta Lake per evitare vincoli ed esegui analisi e processi ETL/ELT sul posto direttamente sul tuo lakehouse, senza più spostare i dati o creare copie in sistemi scollegati. Poi scopri, proteggi e gestisci facilmente tutti i dati con governance granulare, provenienza dei dati e SQL standard su tutti i cloud con Databricks Unity Catalog. “Databricks è fondamentale per la nostra attività, perché la sua architettura lakehouse ci fornisce un ambiente unificato per accedere, conservare e condividere dati fruibili.”— Jagan Mangalampalli, Director of Big Data, PunchhMaggiori informazioniBasato su un'infrastruttura di dati comune, supportata dalla piattaforma lakehouseDatabricks Lakehouse Platform è la soluzione di data warehousing più completa per le moderne esigenze di analisi, e molto altro. Prestazioni di alto livello a una frazione del costo dei data warehouse in cloud. Tempi più rapidi per trasformare i dati grezzi in informazioni fruibili su larga scala e per unificare batch e streaming. Inoltre, il lakehouse consente ai team di gestione dei dati di passare senza difficoltà dall'analisi descrittiva a quella predittiva per estrapolare nuove informazioni. “Databricks ha messo a disposizione un'unica piattaforma per tutti i nostri team di gestione e analisi dei dati, che possono accedere ai dati presenti in tutta l'organizzazione di ABN AMRO, fornendo soluzioni basate su ML che promuovono l'automazione e l'estrapolazione di informazioni approfondite e dettagliate in tutta l'azienda.”— Stefan Groot, Head of Analytics Engineering, ABN AMROMaggiori informazioniMigrazione a DatabricksStanco dei silos di dati, della lentezza e dei costi esorbitanti di sistemi obsoleti come Hadoop e i data warehouse aziendali? Migra a Databricks Lakehouse, la piattaforma moderna per tutti i casi d'uso di gestione dei dati, analisi e AI.Migrazione a DatabricksIntegrazioniIntegrazioni dirette con l'ecosistema garantiscono la massima flessibilità ai team di gestione dei dati. I dati critici per l'azienda possono essere acquisiti con Fivetran e trasformati "sul posto" con dbt per estrapolare nuove informazioni con Power BI, Tableau o Looker, tutto senza trasferire i dati in un data warehouse tradizionale.Acquisizione ed ETL di datiGovernance dei datiBI e dashboard+ Qualsiasi altro client compatibile con Apache Spark™️“Oggi più che mai, le organizzazioni hanno bisogno di una strategia dei dati che offra velocità e agilità adattabili. Con la rapida migrazione dei dati aziendali verso il cloud, vediamo un crescente interesse per l'analisi sul data lake. Databricks SQL offre un'esperienza completamente nuova ai clienti, che possono estrapolare informazioni da enormi quantità di dati, con le prestazioni, l'affidabilità e la scalabilità di cui hanno bisogno. Siamo orgogliosi di collaborare con Databricks per offrire questa opportunità.”— Francois ajenstat, Chief product officer, TableauReferenzeScopri di piùDelta LakePartner ConnectUnity CatalogDelta Live TablesContenuti associati
Tutte le risorse di cui hai bisogno in un unico posto.
Esplora la libreria di risorse per trovare e-book e video sui vantaggi del lakehouse.
Esplora risorseeBookCostruire il Data Lakehouse, di Bill Inmon, padre del data warehousePerché il lakehouse è il nuovo data warehouseGovernance di dati, analisi e intelligenza artificialeThe Big Book of Data EngineeringMigrazione da data warehouse a data lakehouse per principiantiEventiI meccanismi interni del lakehouse dai Data + AI World TourWebinar sulle best practice di ottimizzazione delle prestazioni sul lakehouse: viaggio nella vita di una queryCorso gratuito di Databricks SQL – Su richiestaIl data warehouse migliore è il lakehouseBlogDatabricks stabilisce il record ufficiale di prestazioni di data warehouseAnnunciata la disponibilità in commercio di Databricks SQLEvoluzione del linguaggio Sql in Databricks: Ansi Standard di default e migrazioni più facili dai data warehouseImplementare dbt su Databricks è ancora più sempliceTecniche di modellazione dei data warehouse e implementazione su Databricks Lakehouse PlatformCome costruire una soluzione per l'analisi del marketing con Fivetran e dbt su Databricks LakehousePronto per cominciare?Prova gratuitaEntra nella communityProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineSoluzioniPer settoreServizi professionaliSoluzioniPer settoreServizi professionaliChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiPosizioni aperte
in DatabricksMondoEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Informativa sulla privacy|Condizioni d'uso|Le vostre scelte sulla privacy|I vostri diritti di privacy in California |
https://www.databricks.com/legal/advisory-services-schedule | Advisory Services Schedule | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWLegalTermsDatabricks Master Cloud Services AgreementAdvisory ServicesTraining ServicesUS Public Sector ServicesExternal User TermsWebsite Terms of UseCommunity Edition Terms of ServiceAcceptable Use PolicyPrivacyPrivacy NoticeCookie NoticeApplicant Privacy NoticeDatabricks SubprocessorsPrivacy FAQsDatabricks Data Processing AddendumAmendment to Data Processing AddendumSecurityDatabricks SecuritySecurity AddendumLegal Compliance and EthicsLegal Compliance & EthicsCode of ConductThird Party Code of ConductModern Slavery StatementFrance Pay Equity ReportSubscribe to UpdatesAdvisory Services ScheduleThis Schedule sets forth terms related to the Advisory Services and is incorporated as part of the Master Cloud Services Agreement ("MCSA"). The MCSA and this Schedule, together with any other Schedules that reference or are otherwise incorporated into the MCSA, and any accompanying or future Order you enter into with Databricks issued under the MCSA, comprise the Agreement. This Schedule will co-terminate with the MCSA. Other Schedules do not apply to the services ordered under this Schedule unless expressly referenced as being applicable. Capitalized terms used but not defined in this Schedule have the meaning assigned to them in the MCSA.Additional Definitions.“Customer Materials” means the information and materials you provide to Databricks for Databricks to perform the Advisory Services.Advisory Services.Generally. Subject to the Order, Databricks will provide Advisory Services to facilitate your use of the Databricks platform. Unless otherwise agreed by the parties, Advisory Services will expire one year after the Start Date indicated on the Order and will be booked on the basis of 8-hour service days.Intellectual Property.License. Upon your payment of all Fees under an applicable Order, Databricks grants you a non-exclusive, perpetual, fully paid-up, royalty-free license to use, copy, modify, or create derivative works based on any Advisory Services work product delivered by Databricks to you under the Order (the “Deliverables”). If and to the extent Databricks incorporates any Databricks Materials (as defined below) into the Deliverables, Databricks grants to you a non-exclusive, perpetual, fully paid-up, royalty-free license to use, copy, modify or create derivative works based on such Databricks Materials, solely as incorporated into the Deliverables and solely for your internal business use as reasonably necessary to use the Deliverables for their intended purposes. For the avoidance of doubt, no part of the Platform Services will be deemed to be incorporated into the Deliverables.Databricks Materials. Subject to your rights in your Confidential Information, Databricks will exclusively own all rights, title and interest in and to: (i) the Deliverables; and (ii) any software programs, tools, utilities, processes, inventions, devices, methodologies, specifications, documentation, techniques, training materials, and other materials of any kind used or developed by Databricks or its personnel in connection with performing the Advisory Services, or any other Databricks Services (collectively “Databricks Materials”), including all Intellectual Property Rights in any of the foregoing.No Maintenance. Unless otherwise set forth in an Order the Deliverables are not subject to any maintenance, support or updates after the termination of the Order.Requirements; Limitations. Databricks will provide the Advisory Services remotely or at a mutually agreed location. While on Customer’s premises, Databricks will adhere to reasonable policies provided by Customer in writing in advance. For the avoidance of doubt, no such policies will be deemed to modify the terms of the Agreement.Use of Workspace during Performance of Advisory Services. You may be required to use a Workspace in order to receive Advisory Services. Your use of the Workspace constitutes acceptance of the Platform Services terms of the MCSA unless the Workspace is provided as part of a Databricks Powered Service.Your Obligations; Customer Materials. Your Responsibilities. You:
are responsible for taking reasonable steps at all times to maintain the security, protection and backup of all Customer Materials, including within the Platform Services and any Customer Systems;acknowledge that: (i) Databricks does not provide data backup services; and that (ii) Databricks is not responsible for any loss, destruction, alteration, unauthorized disclosure or corruption of Customer Materials not caused by the gross negligence or willful misconduct of Databricks or any third party under the control of Databricks;agree not to provide Databricks with access to more data than is reasonably necessary to permit Databricks to perform the Advisory Services; andacknowledge that successful delivery of the Advisory Services depends on your full and timely cooperation. You agree to make available any reasonably requested personnel and/or information in a timely manner to allow Databricks to perform such services.Restrictions on Use. You will not:
copy, modify, disassemble, decompile, reverse engineer, or attempt to view or discover the source code of any Deliverables provided to you in object code, in whole or in part, or permit or authorize a third party to do so, except to the extent such activities are expressly permitted by the Agreement or by law notwithstanding this prohibition;use the Databricks Services to develop or offer a service made available to any third party that could reasonably be seen to serve as a substitute for such third party's possible purchase of any Databricks product or service; ortransfer or assign any of your rights hereunder except as permitted under Section 12.5 (Assignment) of the MCSA.Customer Materials. You represent and warrant to Databricks that Customer Materials will not contain:
any data for which you do not have all rights, power and authority necessary for its collection, use and processing as contemplated by the Agreement; orexcept as otherwise specified in an Order, any (x) bank, credit card or other financial account numbers or login credentials, (y) social security, tax, driver’s license or other government-issued identification numbers, or (z) health information identifiable to a particular individual.Data Protection. Databricks will maintain appropriate administrative, physical, and technical safeguards according to ISO/IEC 27001:2013 (the “ISMS Standard”) for protection of the security and confidentiality of Customer Materials under Databricks’ control. Unless specified otherwise in an Order, Databricks engages in the performance of Advisory Services with the expectation that Customer is not engaging Databricks for the purpose of having Databricks act as a data processor for Customer. Nevertheless, except with respect to free Advisory Services, unless you have entered into the DPA, the terms of the DPA are hereby incorporated by reference and will apply to the extent Databricks is deemed to act as Customer’s data processor during the performance of Advisory Services when the Customer Materials include Personal Data, as defined in the DPA. This Schedule and the DPA do not govern the protection of Customer Content and Databricks does not act as a data processor with respect to any data processed by or within a Databricks Powered Service.Expenses. You agree to reimburse Databricks for reasonable travel and lodging expenses actually incurred by Databricks.Warranties; Disclaimer.Warranties. Databricks warrants that the Advisory Services will be provided in a professional and workmanlike manner consistent with industry standards. You must notify Databricks of any warranty deficiencies within ninety (90) days from performance of the deficient Advisory Services. Unless set forth in an Order Databricks makes no guarantee as to whether the Advisory Services will be completed within any specific time frame.Disclaimer. THE WARRANTIES IN SECTION 6.1 (WARRANTIES) ARE EXCLUSIVE AND IN LIEU OF ALL OTHER WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, REGARDING THE DATABRICKS’ SERVICES PROVIDED HEREUNDER. DATABRICKS SPECIFICALLY DISCLAIMS ALL IMPLIED WARRANTIES, CONDITIONS AND OTHER TERMS INCLUDING, WITHOUT LIMITATION, IMPLIED WARRANTIES, CONDITIONS AND OTHER TERMS OF MERCHANTABILITY, SATISFACTORY QUALITY OR FITNESS FOR A PARTICULAR PURPOSE WITH RESPECT TO ANY OF THE FOREGOING. NOTWITHSTANDING ANYTHING TO THE CONTRARY HEREIN: (i) SERVICES PROVIDED UNDER ANY FREE TRIAL PERIOD ARE PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND BY DATABRICKS; (ii) WITHOUT LIMITATION, DATABRICKS DOES NOT MAKE ANY WARRANTY OF ACCURACY, COMPLETENESS, TIMELINESS, OR UNINTERRUPTABILITY, OF THE DATABRICKS SERVICES; AND (iii) DATABRICKS IS NOT RESPONSIBLE FOR RESULTS OBTAINED FROM THE USE OF THE DATABRICKS SERVICES OR FOR CONCLUSIONS DRAWN FROM SUCH USE.Exclusive Remedy. FOR ANY BREACH OF THE WARRANTY AT SECTION 6.1 (WARRANTIES), YOUR EXCLUSIVE REMEDY AND DATABRICKS’ ENTIRE LIABILITY WILL BE THE RE-PERFORMANCE OF THE DEFICIENT SERVICES, OR, IF DATABRICKS CANNOT SUBSTANTIALLY CORRECT THE DEFICIENCY IN A COMMERCIALLY REASONABLE MANNER, DATABRICKS WILL END THE DEFICIENT SERVICES AND REFUND TO YOU THE PORTION OF ANY PREPAID FEES PAID BY YOU TO DATABRICKS APPLICABLE TO THE PERIOD FOLLOWING THE COMMENCEMENT OF THE DEFICIENCY.Last Updated August 12, 2022. For earlier versions, please send a request to [email protected] (with “TOS Request” in the subject).ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
San Francisco, CA 94105
1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/glossary/what-is-pycharm | What is PyCharm?PlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWPyCharmAll>PyCharmTry Databricks for freeGet StartedPyCharm is an integrated development environment (IDE) used in computer programming, created for the Python programming language. When using PyCharm on Databricks, by default PyCharm creates a Python Virtual Environment, but you can configure to create a Conda environment or use an existing one.To play this video, click here and accept cookiesAdditional ResourcesHow to Use MLflow, TensorFlow, and Keras with PyCharmDatabricks Connect (Including PyCharm) DocumentationBack to GlossaryProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/dataaisummit/speaker/cyrielle-simeone | Cyrielle Simeone - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingCyrielle SimeonePrincipal Product Marketing Manager at DatabricksBack to speakersLooking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/fr/solutions/industries/financial-services | Lakehouse pour les services financiers | DatabricksSkip to main contentPlateformeThe Databricks Lakehouse PlatformDelta LakeGouvernance des donnéesData EngineeringStreaming de donnéesEntreposage des donnéesPartage de donnéesMachine LearningData ScienceTarifsMarketplaceOpen source techCentre sécurité et confianceWEBINAIRE mai 18 / 8 AM PT
Au revoir, entrepôt de données. Bonjour, Lakehouse.
Assistez pour comprendre comment un data lakehouse s’intègre dans votre pile de données moderne.
Inscrivez-vous maintenantSolutionsSolutions par secteurServices financiersSanté et sciences du vivantProduction industrielleCommunications, médias et divertissementSecteur publicVente au détailDécouvrez tous les secteurs d'activitéSolutions par cas d'utilisationSolution AcceleratorsServices professionnelsEntreprises digital-nativesMigration des plateformes de données9 mai | 8h PT
Découvrez le Lakehouse pour la fabrication
Découvrez comment Corning prend des décisions critiques qui minimisent les inspections manuelles, réduisent les coûts d’expédition et augmentent la satisfaction des clients.Inscrivez-vous dès aujourd’huiApprendreDocumentationFORMATION ET CERTIFICATIONDémosRessourcesCommunauté en ligneUniversity AllianceÉvénementsSommet Data + IABlogLabosBeacons26-29 juin 2023
Assistez en personne ou connectez-vous pour le livestream du keynoteS'inscrireClientsPartenairesPartenaires cloudAWSAzureGoogle CloudContact partenairesPartenaires technologiques et de donnéesProgramme partenaires technologiquesProgramme Partenaire de donnéesBuilt on Databricks Partner ProgramPartenaires consulting et ISProgramme Partenaire C&SISolutions partenairesConnectez-vous en quelques clics à des solutions partenaires validées.En savoir plusEntrepriseOffres d'emploi chez DatabricksNotre équipeConseil d'administrationBlog de l'entreprisePresseDatabricks VenturesPrix et distinctionsNous contacterDécouvrez pourquoi Gartner a désigné Databricks comme leader pour la deuxième année consécutiveObtenir le rapportEssayer DatabricksRegarder les démosNous contacterLoginJUNE 26-29REGISTER NOWLakehouse pour les services financiersAlimenter l'institution des services financiers de demain articulés autour des données et de l'IADémarrerPlanifier une démoLes services financiers et leurs
quatre défis en matière de donnéesGouvernance et gestion des données
De par l'absence d'agilité des données et de reproductibilité des modèles, il est difficile de répondre aux exigences réglementaires propres aux services financiers.Des insights clients plus approfondis
Les silos de données empêchent de fournir un aperçu complet des comportements clients, des opportunités de vente croisée ainsi que les insights nécessaires à une hyper-personnalisation à grande échelle.Des décisions en temps réel
La dépendance vis-à-vis d'un seul fournisseur ainsi que des outils incohérents entravent la capacité d'effectuer une analytique en temps réel qui stimule et démocratise les décisions financières plus fines et pertinentes.Un accès aux données de tiers
Les technologies existantes ne permettent pas d'exploiter les insights clients et financiers à partir de datasets non structurés et alternatifs qui se développent rapidement. Elles n'offrent pas non plus de capacités de partage de données ouvertes pour favoriser la collaboration.Lakehouse pour les services financiersUne plateforme unifiée d'IA et de données
Une plateforme unique qui rassemble toutes vos charges de travail de données et d'analytique afin de permettre de profondes innovations pour les institutions de services financiers modernes.
Solutions partenaires
Les plus grands fournisseurs de solutions du monde participent au Lakehouse pour les services financiers. Profitez d'offres prédéfinies qui accélèrent la transformation data-driven.
Des outils pour accélérer les résultats commerciaux
Databricks et ses partenaires ont créé toute une gamme d'accélérateurs de solutions pour les services financiers qui facilitent le traitement des cas d'usage courants dans ce domaine, de l'investissement ESG à la prévention de la fraude.
Collaboration dans le secteur
Activez un partage des données sécurisé et ouvert grâce à notre écosyst ème de données — comprenant S&P Global, Intercontinental Exchange, FactSet et Nasdaq — afin de libérer des innovations favorisant la création de valeur durable.
Transformer les services financiers avec
Lakehouse
« La vision du Nasdaq en matière de données et d'IA est alimentée par Lakehouse de Databricks. Nous l'utilisons pour traiter d'énormes quantités de données financières et alternatives complexes afin de créer des données et des insights pour nos clients. Par ailleurs, Databricks joue un rôle essentiel dans nos efforts en vue de moderniser la transmission et la consommation de données. Databricks nous permet de transmettre des données directement dans des espaces de travail analytiques, afin que nos clients puissent analyser et intégrer rapidement des données critiques sans avoir à déplacer des téraoctets. »— Bill Dague, Head of Nasdaq Data Link, NasdaqPourquoi choisir Lakehouse pour les services financiers ?
Regroupez les données et l'IA sur une plateforme ouverte et collaborative vous permettant de minimiser les risques, d'offrir des expériences client enrichies et d'accélérer l'innovationApproche managée de la conformité et de la gestion des risques
Simplifiez la complexité des rapports réglementaires,
de la gestion des risques et de la conformité en rationalisant en toute sécurité
l'acquisition, le traitement et la transmission des données pour
mettre en œuvre de meilleures pratiques de gouvernance.Produits et services personnalisés
Regroupez toute une gamme de données — allant des données du marché aux données alternatives —
pour des expériences hyper-personnalisées favorisant les
opportunités de vente croisée, la satisfaction client et la part de
portefeuille.Des insights en temps réel pour des décisions plus avisées
Intégrez rapidement toutes vos sources de données à l'échelle pour prendre de meilleures décisions
d'investissement, détecter rapidement les nouveaux mécanismes de fraude
et apporter des capacités en temps réel aux pratiques de gestion des risques
.Une monétisation et un partage de données ouvertes
Rassemblez de grandes quantités de données internes et tierces
pour partager des solutions financières innovantes, monétiser de nouveaux produits de données
et fournir des capacités d'analytique avancées à n'importe quel cloud ou outil
sans vous enfermer dans des technologies exclusives
.Télécharger l'ebookPartenaires et solutionsAller vers des formats ouverts et la standardisation des données pour l'analytique et l'IAGestion des risquesIncorporez rapidement des données à des modèles de valeur à risque pour identifier les risques émergents et les menaces potentielles.DémarrerModernisation des activités « cartes » historiques et des portefeuilles de services bancaires cœurs de métierMettez rapidement sur pied une fonction de conversion des données entièrement configurable et industrialisée provenant de systèmes externes.DémarrerPersonnage 360Élaborez des profils client complets et unifiés au sein d'un modèle de données intelligentDémarrerDécouvrez toutes les solutions partenairesLa cybersécurité à l'échelleDétectez rapidement les menaces, étudiez leur impact et réduisez les risques grâce à Splunk et DatabricksDémarrerLa notation ESGAdoptez une vision quantitative de la durabilité et veillez à ce que les entreprises soient responsables de leurs actionsDémarrerUne gestion moderne des risquesAdopter une approche plus agile de la gestion des risques en unifiant les données et l'IA dans le Lakehouse.DémarrerIdentifiez la fraude grâce à l'analytique géospatiale et à l'IAUtilisez des données géospatiales pour mieux comprendre les comportements d'achat des clients, tant sur le plan de leur identité que de leur mode de paiementDémarrerUn enrichissement des transactions grâce aux catégories de marchandsAutomatisez l'enrichissement des transactions pour mieux comprendre les comportements de vos clients et favoriser l'hyper-personnalisationDémarrerDes modèles d'IA basés sur des règles pour lutter contre la fraude financièreModernisez les stratégies de prévention des fraudes pour réduire les frais d'exploitation et augmenter la confiance des clientsDémarrerUne transmission des rapports réglementaires fiable et dans les tempsCombinez les modèles de données du secteur des services financiers avec le cloud pour permettre des normes de gouvernance élevées avec des frais de développement réduitsDémarrerModernisation des plateformes de données d'investissementUtilisez toute la puissance des données des marchés financiers pour vous concentrer sur la fourniture de produits aux clientsDémarrerLutte contre le blanchiment d'argent (AML)Activez les cas d'usage articulés autour de l'IA, comme l'appariement flou et l'analytique d'images, pour lutter contre le blanchiment d'argent et le financement du terrorismeDémarrerVoir toutes les solutionsLakehouse pour les services financiers en pratiqueRéussir ensemble : comment les données + l'IA favorisent la collaboration au sein du LakehouseS'INSCRIRE MAINTENANT →Ressources
Toutes les ressources dont vous avez besoin. Réunies au même endroit.
Explorez la bibliothèque de ressources : vous y trouverez des ebooks et des vidéos sur les données et l'IA pour le secteur des services financiers.
Explorer les ressourceseBooks et infographies[Infographie] Des données pour ancrer une nouvelle ère de la gestion des risquesDécouvrez comment exploiter facilement les avantages des données et de l'IA dans les services financiersTirer parti des données alternatives et tierces dans les services financiers →Faire de l'ESG une réalité grâce à l'analytique des données et à l'IA → Prévenir la fraude grâce aux données et à l'IA : une introduction pour les menaces modernes → Vidéos et téléchargementsDes méthodologies d'investissement ESG explicables et transparentes →Cybersécurité dans les services financiers →Accélérer l'innovation axée sur les données et l'IA dans les services financiers →Analytique ESG: vidéo explicative →Moderniser la gestion des risques →Des plateformes d'investissement modernes avec Lakehouse pour les services financiers →Fiche solution des plans de Lakehouse de l’industrieBlogsAccélérateur pour les banques et les fintechs utilisant les transactions par carte de créditUne approche data-driven de l'environnement, du social et de la gouvernance →Créer une plateforme moderne de gestion des risques dans le domaine des services financiers →Utiliser vos données pour stopper les fraudes sur les cartes de crédit : Capital One et autres bonnes pratiques →Stratégies de modernisation des plateformes de données d'investissement →Améliorer l'expérience client grâce à l'enrichissement des transactions → Les points clés du sommet Data + AI 2022 pour les services financiers →Lakehouse pour les services financiersPrêt à vous lancer ?Nous serions ravis de connaître vos objectifs commerciaux. Notre équipe de services fera tout son possible pour vous aider à réussir.ESSAYER GRATUITEMENT DATABRICKSPlanifier une démoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoProduitPlatform OverviewTarifsOpen Source TechEssayer DatabricksDémoLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneLearn & SupportDocumentationGlossaryFORMATION ET CERTIFICATIONHelp CenterLegalCommunauté en ligneSolutionsBy IndustriesServices professionnelsSolutionsBy IndustriesServices professionnelsEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterEntrepriseNous connaîtreOffres d'emploi chez DatabricksDiversité et inclusionBlog de l'entrepriseNous contacterD écouvrez les offres d'emploi
chez Databrickspays/régionsEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Avis de confidentialité|Conditions d'utilisation|Vos choix de confidentialité|Vos droits de confidentialité en Californie |
https://www.databricks.com/company/newsroom/press-releases/databricks-plans-increase-local-headcount-india-more-50-support | Databricks plans to increase local headcount in India by more than 50% to support business growth and drive customer success; launching new R&D hub in 2023 - DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks plans to increase local headcount in India by more than 50% to support business growth and drive customer success; launching new R&D hub in 2023 May 5, 2023Share this post
Showcases Dolly 2.0 the world’s first truly open instruction-tuned LLM at its inaugural Data + AI World Tour in India
Recently appointed India Country Manager Anil Bhasin to drive further adoption of the lakehouse platform
Bengaluru, India, 5 May 2023 - Databricks, the data and AI company, announced strong data lakehouse adoption in India and plans to increase its local headcount by more than 50% this year, adding roles such as technical specialists, sales and support engineers, and go-to-market resources, to its team of over 250, to support business growth and drive customer success. The company also announced the launch of a new R&D hub in Bengaluru later this year.
Across the Asia Pacific region, the company is experiencing 90% year-over-year business growth.
Databricks has pioneered the data lakehouse, which is an open and unified data management architecture that combines the flexibility, cost-efficiency and scalability of data lakes with the data management features of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.
The company gathered data and AI experts and visionaries at its inaugural Data + AI World Tour in Bengaluru today, to connect, share best practices, and get a first look at the company’s latest product innovations. This includes the recent launch of Dolly 2.0, the world’s first open-source, instruction-following large language model (LLM), fine-tuned on a human-generated instruction dataset licensed for research and commercial use.
Dolly 2.0 is a 12B parameter language model based on the EleutherAI pythia model family and fine-tuned exclusively on a new, high-quality human generated instruction following dataset, crowdsourced among Databricks employees. Databricks is open-sourcing the entirety of Dolly 2.0, including the training code, the dataset, and the model weights, all suitable for commercial use. This means that any organization can create, own, and customize powerful LLMs that can talk to people, without paying for API access or sharing data with third parties.
Recently, Databricks also announced the appointment of Anil Bhasin as the Country Manager for India to drive further lakehouse adoption across the country. A tech industry veteran, Bhasin brings more than 30 years of experience to the role and joined Databricks from UiPath where he served as the Managing Director and VP of the company’s India and South Asia business. Previously, Bhasin was the Regional Vice President for Palo Alto Networks.
“I’m thrilled to be leading the Databricks India business at such a pivotal point in history where business leaders in India are awakening to the power of data and AI to transform their businesses. I firmly believe that data and AI adoption will accelerate our country’s transformation to help India become the most data-driven economy by transforming enterprises to become more data-forward,” said Anil Bhasin, Vice President and Country Manager for Databricks India.
Data-forward companies including InMobi, Swiggy, Dream11, Air India, MakeMyTrip, Meesho, Narayana Health, Myntra, CRED, Parle and many more are harnessing the value of their data and applying AI to optimize their business operations and drive innovations through the lakehouse platform.
"Any travel product, almost always, is a very nuanced purchase – it’s very contextual and experiential by its very nature. Given that, the travel ecosystem has multiple variables that must be evaluated – in batch, and, more importantly, in real-time - before a specific set of products can be personalized and recommended to the customer. Databricks has been our trusted partner in unifying data to bolster our ML ecosystem, which helps us deliver quick and effective results in “near-real-time”. The Lakehouse Architecture has helped us launch multiple successful consumer-led innovations," said Piyush Kumar, Vice President, Technology Development, MakeMyTrip.
"We have chosen Databricks Lakehouse Platform as our ideal modern data architecture because we wanted a unified data platform that eliminates data silos within our organization and achieves cost optimization with the increasing amount of data that we are collecting. I am confident that Databricks will be a key enabler in our overall digitization journey, powering faster business analytics to support and enable our business functions to make data-driven business-critical decisions and firm strategies, to ultimately delight our customers from all classes and age groups to enjoy our products to the fullest,” said Sanjay Joshi, CIO of Parle Products Pvt. Ltd.
Databricks also recognized and celebrated outstanding data teams and individuals using data and AI to transform their industry at the inaugural Databricks India Innovation Awards.
Congratulations to the winners of the India Data + AI Awards 2023:
Data + AI Award for Disruptive Innovation: recognizes the data and AI team that embodies innovation and impact — creating new markets and value propositions by employing data and AI technologies, and inspiring the global data and AI community.
Winner: MakeMyTrip
Data + AI Award for Driving a Data-First Culture: recognizes the data and AI team that evangelizes the culture of data-driven decision making, delivering data into the hands of empowered users across the organization — making every team a data team.
Winner: Dream11
Data + AI Visionary Award for Shaping the Future: honors the data and AI team that recognizes the potential of data and AI to transform their organization, and turning that vision into reality, unlocking significant value for the business.
Winner: Meesho
Data + AI Award for Societal Impact: honors the data and AI team that spurs positive social change which benefits society at large.
Winner: Navi
Data + AI Award for Redefining Customer Experience: honors the team that leverages data and AI to deliver seamless, personalized and omnichannel customer experiences.
Winner: InMobi
About Databricks
Databricks is the data and AI company. More than 9,000 organizations worldwide — including Comcast, Condé Nast, H&M, and over 50% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Contact: [email protected]
Recent Press ReleasesMay 5, 2023Databricks plans to increase local headcount in India by more than 50% to support business growth and drive customer success; launching new R&D hub in 2023 Read nowApril 4, 2023Databricks Announces Lakehouse for Manufacturing, Empowering the World’s Leading Manufacturers to Realize the Full Value of Their DataRead nowMarch 30, 2023Databricks Announces EMEA Expansion, Databricks Infrastructure in the AWS France (Paris) RegionRead nowMarch 7, 2023Databricks Launches Simplified Real-Time Machine Learning for the LakehouseRead nowJanuary 17, 2023Databricks Strengthens Commitment in Korea, Appointing Jungwook Jang as Country ManagerRead nowView AllResourcesContactFor press inquires:[email protected]Stay connectedStay up to date and connect with us through our newsletter, social media channels and blog RSS feed.Subscribe to the newsletterGet assetsIf you would like to use Databricks materials, please contact [email protected] and provide the following information:Your name and titleCompany name and location Description of requestView brand guidelinesProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
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https://www.databricks.com/dataaisummit/speaker/lucas-dos-santos-celestino/# | Lucas dos Santos Celestino - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingLucas dos Santos CelestinoPlatform Product Manager at AB InbevBack to speakersCurrently working for Anheuser-Busch InBev (Brazil) as Platform Product Manager with expertise in software development, cloud architecture/infrastructure, UX design, product operations and agile methods.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/jay-yang | Jay Yang - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingJay YangPrincipal Data Engineer at ProcoreBack to speakersJay Yang is a Principal Data Engineer at Procore, where he leads the Data and Analytics Platform team. With more than 17 years of industry experience, Jay excels in creating high-performance data platforms designed to support the company's reporting, analytics, and machine-learning applications. As a strong advocate for data accessibility, he is committed to unlocking the potential of data and generating value for businesses.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/dataaisummit/speaker/chen-guo | Chen Guo - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingChen GuoStaff Software Engineer at CoinbaseBack to speakersChen Guo is a staff software engineer from the Data Platform & Service team at Coinbase. His current work focuses on designing and developing SOON (Spark cOntinuOus iNgestion), a unified streaming ingestion framework, and SONAS (SOON as a Service). Before Coinbase, he worked at LinkedIn's Big Data Infrastructure team to build cloud-based ingestion and offline processing infrastructure using Azure Data Factory and contributed to Apache Gobblin. When not working, he loves reading and sports.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/company/careers/open-positions?location=hangzhou%2C%20china | Current job openings at Databricks | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOW OverviewCultureBenefitsDiversityStudents & new gradsCurrent job openings at DatabricksDepartmentLocationProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/solutions/accelerators/safety-stock | Solution Accelerator - How to build: Safety stock | DatabricksPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWSolution AcceleratorHow to build: Safety stockIncrease sales by ensuring adequate inventory across the supply chain, while minimizing carrying costsCreate fine-grained and viable estimates of buffer stock for raw material, work-in-progress or finished goods inventory items that can be scaled across the supply chain. Free up working capital that would be tied up in inventory and reallocate to more productive uses.Read the full write-upDownload all notebooksBenefits and business valueEnsure buffer inventoryRapidly run safety-stock analysis across all plants and distribution centers multiple times per day
Understand stock patterns over timeEfficiently store past forecasts to analyze over-repeated periods using machine learning
Free up working capitalMinimize excess inventory while maintaining service levels, to improve financial flexibility
Reference ArchitectureData usedT-log, POS, or shipment sales data. Inventory data, promotions, weather, and other causal data sets.Deliver AI innovation faster with Solution Accelerators for popular industry use cases. See our full library of solutionsReady to get started?Try Databricks for freeProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
160 Spear Street, 13th Floor
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Privacy Notice|Terms of Use|Your Privacy Choices|Your California Privacy Rights |
https://www.databricks.com/dataaisummit/speaker/vikas-reddy-aravabhumi | Vikas Reddy Aravabhumi - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingVikas Reddy AravabhumiStaff Backline Engineer at DatabricksBack to speakersVikas Reddy Aravabhumi is an experienced Staff Backline engineer with over 12 years of industry experience. He specializes in resolving Big data problems for customers through the use of Structured Streaming, spark SQL, Delta Lake and DLT. Additionally, he has extensive expertise in implementing Structured Streaming frameworks. Prior to joining Databricks, he worked as a technical lead assisting consulting and R&D companies in deploying their big data ETL pipelines using Spark.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/company/partners/technology?itm_data=TechnologyPartnersProgram-FooterCTA-Find | Databricks Technology Partners - DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
Goodbye, Data Warehouse. Hello, Lakehouse.
Attend to understand how a data lakehouse fits within your modern data stack.
Register nowSolutionsSolutions by IndustryFinancial ServicesHealthcare and Life SciencesManufacturingCommunications, Media & EntertainmentPublic SectorRetailSee all IndustriesSolutions by Use CaseSolution AcceleratorsProfessional ServicesDigital Native BusinessesData Platform MigrationNew survey of biopharma executives reveals real-world success with real-world evidence.
See survey resultsLearnDocumentationTraining & CertificationDemosResourcesOnline CommunityUniversity AllianceEventsData + AI SummitBlogLabsBeaconsJoin Generation AI in San Francisco
June 26–29
Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWDatabricks Technology PartnersConnect with Databricks Technology Partners to integrate data ingestion, business intelligence and governance capabilities with the Databricks Lakehouse Platform.“With Databricks and Fivetran, we will be able to significantly improve marketing insights in the future. From a technical standpoint, the two tools interact harmoniously together and the integration feels very native.”— Jan-Niklas Mühlenbrock, Team Lead, Business Intelligence & ERP at Paul HewittDatabricks Technology Partners integrate their solutions with Databricks to provide complementary capabilities for ETL, data ingestion, business intelligence, machine learning and governance.These integrations enable customers to leverage the Databricks Lakehouse Platform’s reliability and scalability to innovate faster while deriving valuable data insights.Partner ConnectBring together all your data, analytics and AI tools on one open platform. With Partner Connect, Databricks provides a fast and easy way to connect your existing tools to your lakehouse using validated integrations, and helps you discover and try new solutions.Become a partnerLearn moreLoading...ProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoProductPlatform OverviewPricingOpen Source TechTry DatabricksDemoLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunityLearn & SupportDocumentationGlossaryTraining & CertificationHelp CenterLegalOnline CommunitySolutionsBy IndustriesProfessional ServicesSolutionsBy IndustriesProfessional ServicesCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsCompanyAbout UsCareers at DatabricksDiversity and InclusionCompany BlogContact UsSee Careers
at DatabricksWorldwideEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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https://www.databricks.com/dataaisummit/speaker/robin-sutara | Robin Sutara - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingRobin SutaraField CTO at DatabricksBack to speakersFrom repairing Apache helicopters near the Korean DMZ to the corporate battlefield, Robin has demonstrated success in navigating the high stress, and sometimes combative, complexities of data-led transformations. She has consulted with hundreds of organisations on data strategy, data culture, and building diverse data teams. Robin has had an eclectic career path in technical and business functions with more than two decades in tech companies, including Microsoft and Databricks.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |
https://www.databricks.com/it/product/google-cloud?itm_data=menu-item-gcpProduct | Databricks Google Cloud Platform (GCP) | DatabricksSkip to main contentPiattaformaThe Databricks Lakehouse PlatformDelta LakeGovernance dei datiIngegneria dei datiStreaming di datiData warehouseCondivisione dei datiMachine LearningData SciencePrezziMarketplaceTecnologia open-sourceSecurity and Trust CenterWEBINAR 18 maggio / 8 AM PT
Addio, Data Warehouse. Ciao, Lakehouse.
Partecipa per capire come una data lakehouse si inserisce nel tuo stack di dati moderno.
Registrati oraSoluzioniSoluzioni per settoreServizi finanziariSanità e bioscienzeIndustria manifatturieraComunicazioni, media e intrattenimentoSettore pubblicoretailVedi tutti i settoriSoluzioni per tipo di applicazioneAcceleratoriServizi professionaliAziende native digitaliMigrazione della piattaforma di dati9 maggio | 8am PT
Scopri la Lakehouse for Manufacturing
Scopri come Corning sta prendendo decisioni critiche che riducono al minimo le ispezioni manuali, riducono i costi di spedizione e aumentano la soddisfazione dei clienti.Registrati oggi stessoFormazioneDocumentazioneFormazione e certificazioneDemoRisorseCommunity onlineUniversity AllianceEventiConvegno Dati + AIBlogLabsBeacons
26–29 giugno 2023
Partecipa di persona o sintonizzati per il live streaming del keynoteRegistratiClientiPartnerPartner cloudAWSAzureGoogle CloudPartner ConnectPartner per tecnologie e gestione dei datiProgramma Partner TecnologiciProgramma Data PartnerBuilt on Databricks Partner ProgramPartner di consulenza e SIProgramma partner consulenti e integratori (C&SI)Soluzioni dei partnerConnettiti con soluzioni validate dei nostri partner in pochi clic.RegistratiChi siamoLavorare in DatabricksIl nostro teamConsiglio direttivoBlog aziendaleSala stampaDatabricks VenturesPremi e riconoscimentiContattiScopri perché Gartner ha nominato Databricks fra le aziende leader per il secondo anno consecutivoRichiedi il reportProva DatabricksGuarda le demoContattiAccediJUNE 26-29REGISTER NOWDatabricks su Google CloudLa piattaforma lakehouse aperta incontra il cloud aperto per unificare data engineering, data science e analisiCominciaDatabricks su Google Cloud è un servizio sviluppato congiuntamente che consente di conservare tutti i dati su una piattaforma lakehouse semplice e aperta, che unisce il meglio di data warehouse e data lake per unificare tutti i carichi di lavoro di analisi e AI. Grazie alla stretta integrazione con Google Cloud Storage, BigQuery e Google Cloud AI Platform, Databricks può girare perfettamente su tutti i servizi di gestione dati e AI su Google Cloud.
Data engineering affidabileAnalisi SQL su tutti i datiCollaborative Data ScienceMachine learning in produzione
Perché Databricks su Google Cloud?ApertaSoluzione basata su standard aperti, API aperte e infrastruttura aperta, per poter accedere, elaborare e analizzare i dati alle tue condizioni.OttimizzatoImplementa Databricks su Google Kubernetes Engine, il primo ambiente runtime di Databricks basato su Kubernetes con qualsiasi cloud, per ottenere informazioni approfondite più velocemente.IntegratoAccedi a Databricks con un clic dalla Google Cloud Console, con sicurezza, fatturazione e gestione integrate.Impara dall'esperienza di questi clienti
“Databricks su Google Cloud semplifica il processo di gestire qualsiasi quantità di casi d'uso su una piattaforma di calcolo scalabile, accorciando i cicli di pianificazione necessari per fornire una risposta a ogni domanda o problema”.—Harish Kumar, Global Data Science Director di ReckittIntegrazione snella con Google CloudGoogle Cloud StorageGoogle Cloud StorageConsenti l'accesso diretto in lettura/scrittura a dati in Google Cloud Storage (GCS) e sfrutta il formato aperto Delta Lake per aggiungere affidabilità e prestazioni evolute all'interno di Databricks.Google Kubernetes EngineBigQueryIdentità in grande stileGoogle Cloud AI PlatformGoogle Cloud BillingCercatoreEcosistema di partnerRisorseEventi virtualiVirtual Workshop: Il data lake apertoNotizieDatabricks collabora con Google Cloud per fornire la sua piattaforma a imprese di tutto il mondoBlog e reportAnnuncio del lancio di Databricks su Google CloudPresentazione di Databricks su Google Cloud – Ora in anteprima pubblicaScheda tecnica di Databricks su Google CloudDatabricks su Google Cloud è ora disponibile per tuttiData engineering, data science e analisi con Databricks su Google CloudPronto per cominciare?Prova Databricks gratisProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoProdottoPanoramica della piattaformaPrezziTecnologia open-sourceProva DatabricksDemoFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineFormazione e supportoDocumentazioneGlossaryFormazione e certificazioneHelp CenterLegaleCommunity onlineSoluzioniPer settoreServizi professionaliSoluzioniPer settoreServizi professionaliChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiChi siamoChi siamoLavorare in DatabricksDiversità e inclusioneBlog aziendaleContattiPosizioni aperte
in DatabricksMondoEnglish (United States)Deutsch (Germany)Français (France)Italiano (Italy)日本語 (Japan)한국어 (South Korea)Português (Brazil)Databricks Inc.
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1-866-330-0121© Databricks 2023. All rights reserved. Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.Informativa sulla privacy|Condizioni d'uso|Le vostre scelte sulla privacy|I vostri diritti di privacy in California |
https://www.databricks.com/dataaisummit/speaker/thet-ko/# | Thet Ko - Data + AI Summit 2023 | DatabricksThis site works best with JavaScript enabled.HomepageSAN FRANCISCO, JUNE 26-29VIRTUAL, JUNE 28-29Register NowSession CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingThet KoPrincipal Data Engineer at SEEKBack to speakersThet Ko has 10+ years experience working across the full stack from cloud infrastructure, databases, backend systems to front end development. He is currently a Principal Engineer at SEEK responsible for supporting over 165 engineers and scientists scale their analysis on Databricks.Looking for past sessions?Take a look through the session archive to find even more related content from previous Data + AI Summit conferences.Explore the session archiveRegister today to save your spotRegister NowHomepageOrganized By Session CatalogTrainingSpeakers2022 On DemandWhy AttendSpecial EventsSponsorsAgendaVirtual ExperiencePricingFAQEvent PolicyCode of ConductPrivacy NoticeYour Privacy ChoicesYour California Privacy RightsApache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The Apache Software Foundation has no affiliation with and does not endorse the materials provided at this event. |