Data warehouse automation
Data warehouse automation or DWA refers to the process of accelerating and automating the data warehouse development cycles, while assuring quality and consistency. DWA is believed to provide automation of the entire lifecycle of a data warehouse, from source system analysis to testing to documentation. It helps improve productivity, reduce cost, and improve overall quality.[1]
General
Data warehouse automation primarily focuses on automation of each and every step involved in the lifecycle of a data warehouse, thus reducing the efforts required in managing it.[2] Data warehouse automation works on the principles of design patterns. It comprises a central repository of design patterns, which encapsulate architectural standards as well as best practices for data design, data management, data integration, and data usage.[3] In November 2015, an analyst firm has published a guide Which Data Warehouse Automation Tool is Right for You? covering four of the leading products in the DWA space.[4]
Benefits
Data warehouse automation can provide advantages like source data exploration, warehouse data models, ETL generation, test automation, metadata management, managed deployment, scheduling, change impact analysis and easier maintenance and modification of the data warehouse.[5] More important than the technical features of DWA tools, however, is the ability to deliver projects faster and with less resources.[6]
References
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- http://www.datawarehousemanagement.org/download/free_files/Documents/Whitepaper-Barry-Devlin-Data-warehouse-done-final.pdf; Quipu datawarehouse management Retrieved 12 May 2016
