用于流数据的 Spark 4.0 和 Delta 4.0.pdf

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1、Spark 4.0 and Delta 4.0 For Streaming DataBryce BartmannForward-looking StatementThis presentation has been prepared for informational purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the dat

2、e of this presentation unless stated otherwise.This presentation and the accompanying oral commentary may contain forward-looking statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“

3、believes”,“estimates”,“predicts”,or“continue”,or the negative of these words or other similar terms or expressions that concern Databricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently

4、subject to risks and uncertainties that could cause actual results to differ materially from those expressed in or suggested by the forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not

5、undertake any obligation to publicly update or revise any forward-looking statement,whether as a result of new information,future developments or otherwise.3Comprehensive functionality to support Time Series workloads and dataLeverage Spark 4.0 and Delta 4.0 as a Time Series Lakehouse4Challenge:Succ

6、ess:Three goalsSimplify the Data ModelOptimize the Data LayoutIngest Diverse Range of Data Sources5Simplifying the Data ModelVariant TypesStreaming Data TypesSemi-structured json payloads are commonValues can be mixed data types for the same column numeric,string or boolean,for exampleData Models ar