Strategies for Testing Data Warehouse Applications

June issue of DM Review magazine has an interesting article on Data Warehouse Testing. This topic has always been up for debate. Traditional testing teams want to test a Data Warehouse like any other transactional systems. Whereas Data Warehouse gurus would always suggest testing the input and output. This article lists seven goals for a successful data warehouse testing, namely;

• Data completeness. Ensures that all expected data is loaded.
• Data transformation. Ensures that all data is transformed correctly according to business rules and/or design specifications.
• Data quality. Ensures that the ETL application correctly rejects, substitutes default values, corrects or ignores and reports invalid data.
• Performance and scalability. Ensures that data loads and queries perform within expected time frames and that the technical architecture is scalable.
• Integration testing. Ensures that the ETL process functions well with other upstream and downstream processes.
• User-acceptance testing. Ensures the solution meets users' current expectations and anticipates their future expectations.
• Regression testing. Ensures existing functionality remains intact each time a new release of code is completed.

This is an interesting read for those who have struggled to find the right balance while implementing strategies for a data warehouse testing.