Can you describe how you implement unit testing to ensure data accuracy during migration projects, and what tools and methods you have found effective?

For migration projects I write unit tests that validate each transformation step in isolation. I use PyTest with the pyspark-testing library to create small Spark DataFrames that represent source and expected target rows. I then apply the transformation function and assert equality of the resulting DataFrame. To catch schema drift, I use Great Expectations to generate a snapshot of the source schema and compare it against the target schema after migration. I also employ golden file testing: after a successful migration, I export a subset of the target data to a CSV and store it as a reference; subsequent runs compare the new output to the golden file. For large datasets, I use Delta Lake's MERGE and VACUUM commands to maintain a clean history, and I run a checksum comparison across source and target tables. These methods give me confidence that the data remains accurate and consistent throughout the migration.