You probably use checklists to record and efficiently execute a wide range of daily work tasks. But if you don’t use checklists for developing and monitoring your data warehouse quality assurance (QA), you’re missing an enormous boost in productivity and proficiency.
Procedural data warehouse checklists serve as concrete reminders of which jobs we need to perform and the order in which we need to perform them. They are an informational aid used to reduce failure by compensating for the potential limits of human memory and attention.
In this article, I will provide test planners and testers with ideas for data warehouse checklists that help them avoid often-overlooked tasks, including tasks that require special attention during the complexity of data warehouse test planning and test execution efforts. Checklists can help with the development of the overall test strategy, the selection and prioritisation of test cases, and the successful completion of troubleshooting.
Some programmers are not well trained as testers. They may like to program, deploy the code, and move on to the next development task without a thorough unit test. A checklist will aid database programmers to systematically test their code before formal QA testing.
An integration test checklist helps ensure that ETL workflows are executed as scheduled with correct dependencies.
As the volume of data in a warehouse grows, ETL execution times can be expected to increase, and performance of queries often degrade. These changes can be mitigated by having a solid technical architecture and efficient ETL design. The aim of performance testing is to point out potential weaknesses in the ETL design, such as reading a file multiple times or creating unnecessary intermediate files. A performance and scalability testing checklist helps discover performance issues.
One of the objectives of data warehouse testing is to help ensure that the required business functions are implemented correctly. This phase includes data verification, which tests the quality of data populated into target tables. A system-testing checklist can help with this process.
Because of the complexity of integrating various source data systems, you can expect some initial problems with the environments. A technical shakedown test is conducted before commencing system, stress and performance, and user acceptance testing to help ensure several needs are met.
The testing checklists provided here are by no means exhaustive. But I hope you see that these kinds of lists can be valuable for a complex series of data warehouse tests. Checklists help improve data warehouse QA success by compensating for potential limits of human memory. They help ensure consistency and completeness in carrying out the complex task of planning and executing data warehouse tests that are essential to the success of your projects.