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数据摄取快慢:如何通过正确的时间处理提高数据可用性和数据质量.pdf

上传人: 2*** 编号:139020 2023-06-04 26页 4.63MB

1、Data Ingestion,Fast and SlowDillon BostwickDatabricks20232023 Databricks Inc.All rights reservedConfidential and Proprietary3The Worlds Data Is Real-Time2023 Databricks Inc.All rights reservedConfidential and Proprietary4The World Needs Real-Time Analytics2023 Databricks Inc.All rights reservedConfi

2、dential and ProprietaryAnd Not JustDigital Transformation52023 Databricks Inc.All rights reservedConfidential and ProprietaryConventional“Batch vs.Streaming”Dichotomy Is Limiting62023 Databricks Inc.All rights reservedConfidential and ProprietaryA Data Engineers LifeFrom:Retailer CEORetailer CEOSubj

3、ect:Need an analysis ASAP!Subject:Need an analysis ASAP!To:Dillon Bostwick Great report!Can you update it every dayevery day?every minuteWeekly Sales Forecast2023 Databricks Inc.All rights reservedConfidential and Proprietary8Process Data at Real Time2023 Databricks Inc.All rights reservedConfidenti

4、al and Proprietary9Process Data at RightTime2023 Databricks Inc.All rights reservedConfidential and Proprietary10Think Right-TimeAdjust Data Freshness to Business Need,at Any SpeedCostLatencySecondsMinutesHoursDaysWeeks1.Reduce risk of decisions on stale data2.Reduce risk of inaccurate models(drift)

5、3.Improve agility in transition to real-time4.Improve cost predictability2023 Databricks Inc.All rights reservedConfidential and ProprietaryStep 1:Scoping Right-Time Use Cases11Fraud DetectionRecommendersConcept DriftReal-Time BiddingComposable CDPRegulatory ReportingIntraday ValuationLoan ApprovalI

6、nventory RebalancingSpoilage ReductionOn-Shelf AvailabilityStock tickerTradingPoint of SaleERP(Inventory)OT(digital twin,IoT)Retail/ManufacturingFinancial ServicesMediaHorizontal/DigitalUse CaseData SourcesDSP*CRMClickstreamUser preferencesLTV*Demand Side Platform2023 Databricks Inc.All rights reser

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本文主要讨论了实时数据处理的重要性以及实时分析的挑战。文章指出,传统的“批量处理与流处理”的二分法限制了数据工程师的工作。随着数据量的实时增长,世界需要实时分析,而不仅仅是数字转型。文章强调了根据业务需求调整数据新鲜度的关键性,并提出了减少决策风险、提高模型准确性、提高实时适应性和成本可预测性四个方面的好处。在确定实时用例时,应关注数据源特性和可用性,并提出了一个框架来划分何时使用流处理。文章还介绍了使用Spark和Delta统一数据流程的方法,以及使用分布式账本技术(DLT)简化从查询到生产管道的步骤。最后,文章讨论了实现大规模数据流管道实时新鲜度所面临的挑战,并预告了自动优化增量更新的技术即将推出。
"实时数据处理如何影响企业决策?" "如何利用Spark和Delta统一数据流程?" "实时数据新鲜度对不同行业案例的影响是什么?"
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