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Method and architecture for automated optimization of ETL throughput in data warehousing applications

机译:在数据仓库应用程序中自动优化ETL吞吐量的方法和体系结构

摘要

A computer software architecture to automatically optimize the throughput of the data extraction/transformation/loading (ETL) process in data warehousing applications. This architecture has a componentized aspect and a pipeline-based aspect. The componentized aspect refers to the fact that every transformation used in this architecture is built up with transformation components selected from an extensible set of transformation components. Besides simplifying source code maintenance and adjustment for the data warehouse users, these transformation components also provide these users the building blocks to effectively construct pertinent and functionally sophisticated transformations in a pipelined manner. Within a pipeline, each transformation component automatically stages or streams its data to optimize ETL throughput. Furthermore, each transformation either pushes data to another transformation component, pulls data from another transformation component, or performs a push/pull operation on the data. Thereby, the pipelining; staging/streaming; and pushing/pulling features of the transformation components effectively optimizes the throughput of the ETL process.
机译:一种计算机软件体系结构,用于自动优化数据仓库应用程序中数据提取/转换/加载(ETL)过程的吞吐量。此体系结构具有组件化方面和基于管道的方面。组件化方面指的是以下事实:此体系结构中使用的每个转换都是使用从可扩展的转换组件集中选择的转换组件构建的。除了简化数据仓库用户的源代码维护和调整之外,这些转换组件还为这些用户提供了构建块,以便以流水线方式有效地构建相关且功能复杂的转换。在管道中,每个转换组件都会自动暂存或传输其数据以优化ETL吞吐量。此外,每个变换都将数据推入另一个变换组件,从另一个变换组件拉取数据,或者对数据执行推/拉操作。从而,流水线化;分期/流媒体;转换组件的推/拉功能有效地优化了ETL过程的吞吐量。

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