Data Warehouse Testing: Key Aspects, Checklists, Challenges, Tools
Data warehouse testing is a testing technique in which the data placed inside a data warehouse is tested for accuracy, integrity, reliability, and consistency in accordance with the company’s data framework. Data warehouse testing aims to ensure that the data that is integrated inside the data warehouse is reliable enough to make the best decisions for company benefits.
Three basic testing levels like unit testing, Integration, system testing with data warehouse testing checklists are crucial for making data warehouse systems efficient and running them correctly.
The process of data warehouse testing is very different from the app testing as it requires a data-centric testing approach while testing. Programmers who face various challenges are as follow:
- Data warehouse testing depends upon the availability of test data and requires several test scenarios.
- In data warehouse testing, sometimes it is essential to compare large volumes of data with millions of records.
- Data should be collected from heterogeneous data sources for comparisons such as flat files and databases.
- Data is always converted, and it may require complicated SQL queries to compare.
- BI tools such as Cognos, OBIEE, Business Objects, and Tableau produce reports on the fly based on a metadata model. Performing testing with numerous combinations of attributes and measures is very challenging.
- A huge volume of reports and data make it very challenging to test for functionality, stress, and regression.
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