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]
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.[4] More important than the technical features of DWA tools, however, is the ability to deliver projects faster and with less resources.[5]
References
- ^ "Automate and accelerate your data transformations". www.attunity.com/. Attunity. Retrieved 7 December 2015.
- ^ "New Buzzword! Data Warehouse Automation". blogs.jetreports.com/. jetreports. Retrieved 7 December 2015.
- ^ "Data Warehouse Automation - A Decision Guide" (PDF). www.wherescape.com. David L. Wells, Infocentric LLC. Retrieved 7 December 2015.
- ^ "Data Warehouse Automation (DWA)?". timextender.com. TimeXtender Software 2015. Retrieved 7 December 2015.
- ^ "Deliver Faster". kalido.com. Magnitude Software. Retrieved 9 December 2015.