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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
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 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...
See all Data Engineering 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.
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https://www.databricks.com/kr/discover/demos | Databricks 제품 및 파트너 데모 허브 - 솔루션즈 액셀러레이터 데모 - 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)
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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
기존 Azure 서비스와 안전하게 통합할 수 있는 단순한 오픈 협업형 플랫폼입니다. 이 데모에서는 Azure Data Lake Storage(ADLS), Azure Data Factory(ADF), Azure IoT Hub, Azure Synapse Analytics, Power BI 등 가장 보편적인 Azure Databricks 통합 사례를 몇 가지 다루고자 합니다.자세히AWS 기반 Databricks 클라우드 통합Google Cloud에 Databricks 배포Partner Connect산업 솔루션2주 이내에 구상에서 개념 증명 단계까지 완료Databricks 솔루션즈 액셀러레이터는 모든 기능을 갖춘 노트북과 모범 사례를 포함한 전용 가이드를 통해 빠른 성과를 제공합니다. Databricks 고객은 발견, 설계, 개발, 테스트 시간을 단축해, 대부분 2주 이내에 구상에서 개념 증명(PoC) 단계까지 완료했습니다.액셀러레이터 둘러보기Databricks 계정 생성하기1/2이름성회사 이메일회사직함전화번호(선택사항)보기 중에서 선택하세요국가시작하기제품플랫폼 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.
160 Spear Street, 13th Floor
San Francisco, CA 94105
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. |
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DAIS-2023 Dataset
This dataset contains scraped text data from the Databricks Data and AI Summit 2023 (DAIS 2023) homepage as well as text from any public page that is linked in that page or is a two-hop linked page.
We have used this dataset to fine-tune our DAIS DLite model, along with our dataset of AI-generated question-answer pairs generated from this dataset. Feel free to check them out!
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Size of downloaded dataset files:
30.1 MB
Size of the auto-converted Parquet files:
8.9 MB
Number of rows:
2,057
Models trained or fine-tuned on aisquared/dais-2023