Integrated automated testing in ETL development presents a unique set of challenges. This is often the most cost-effective method for a DW that may have a long maintenance life because even minor patches or enhancements over the lifetime of the warehouse can cause features to break which were working earlier. Once tests have been automated, they can be run quickly and repeatedly. DW test automation involves writing programs for testing that would otherwise need to be done manually. In addition, manual tests may not be effective in finding certain classes of defects. Commonly, test automation involves automating a manual process already in place that uses a formalized testing process.Īlthough manual ETL tests may find many data defects, it is a laborious and time-consuming process. Data Warehouse test automationĭata warehouse test automation is described as the use of tools to control 1) the execution of tests, 2) the comparison of actual outcomes to predicted outcomes, 3) the setting up of test preconditions, and other test control and test reporting functions. Test automation requires initial planning and ongoing diligence, but once technical teams embrace automation, project success is more assured. While seeking to embrace and adapt to change, we must always be confident that features that were “done, complete!” in previous iterations retain their high quality considering the changing systems. Second, manual ETL testing is not sufficiently repeatable for regression testing. Teams that rely primarily on manual testing ultimately end up deferring testing until dedicated testing periods, which allows bugs to accumulate. There are two key problems with manual testing.įirst, it takes too long and is therefore a significant inhibitor to the frequent delivery of working software. Manual testing is not practical in a highly iterative and adaptive development environment. Foremost it means integrating QA efforts and automation into ETL development iterations.Įssential to integrated ETL testing is test automation. This objective requires a unique approach to quality assurance methods and tools. At the end of each iteration of DW ETLs, data tables are expected to be of sufficient quality for the next ETL phase. You can contact Wayne at IntroductionĪ characteristic of DW development is the frequent release of high quality data for user feedback and acceptance. He continues to lead numerous ETL testing and coaching projects on a consulting basis. Additionally, Wayne has taught IIST (International Institute of Software Testing) courses on data warehouse, ETL, and data integration testing. Morgan Chase, Credit Suisse, Standard and Poor’s, AIG, Oppenheimer Funds, IBM, and Achieve3000. Editor’s notes: Wayne Yaddow is an independent consultant with over 20 years’ experience leading data migration/integration/ETL testing projects at organizations including J.P.
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