My data warehouse project team is configuring one of our QA environments to be a dynamic read-only copy of production. I’m salivating as I try to wrap my head around the testing possibilities.
We are taking about 10 transactional databases from one of our QA environments, and replacing them with 10 databases replicated from their production counterparts. This means, when any of our users perform a transaction in production, said data change will be reflected in our QA environment instantly.
- Excellent Soak Testing – We’ll be able to deploy a pre-production build of our product to our Prod-replicated-QA-environment and see how it handles actual production data updates. This is huge because we have been unable to find some bugs until our product builds experience real live usage.
- Use real live user scenarios to drive tests – We have a suite of automated checks that invoke fake updates in our transactional data bases, then expect data warehouse updates within certain time spans. The checks use fake updates. Until now. With the Prod-replicated-QA-environment, we are attempting to programmatically detect real live data updates via logging, and measure those against expected results.
- Comparing reports – A new flavor of automated checks is now possible. With the Prod-replicated-QA-environment, we are attempting to use production report results as a golden master to compare to QA report results sitting on the pre-production QA build data warehouse. Since the data warehouse data to support the reports should be the same, we can expect the report results to match.
- The Prod-replicated-QA-environment will be read-only. This means instead of creating fake user actions whenever we want, we will need to wait until they occur. What if some don’t occur…within the soak test window?
- No more data comparing? - Comparing transactional data to data warehouse data has always been a bread and butter automated check we’ve performed. These checks check data integrity and data loading. Comparing a real live quickly changing source to a slowly updating target will be difficult at best.
Labels: Data Warehouse Testing