We all know how important data testing is in this digital transformation world. ETL testing mainly consists of ensuring that data has safely traveled from its source to its destination. Data processing is prone to errors, and you may end up with some data loss, corrupted, or irrelevant data as a result of various issues during the transformation phase. This is why ETL testing is so important: it ensures that nothing has been lost or corrupted along the way.

To validate the data, the tester usually writes the ETL script or SQL by hand. The scripts will be run against the source and destination, and the results will be compared to validate the data. In this article, we’ll look at how we can use Great Expectations, Databricks, and C# code to automate data quality and completeness tests.

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