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One small edit. One silently broken table downstream.
What you'll have at the end
A pytest suite that fails on a bad transformation, run automatically before the job ships
You need
A PySpark transformation that already runs correctly end to end (a filter, a derived column, a grouped total), but still mixed into the same script that reads the source and writes the table, with no test file next to it yet, plus a project where pytest can be installed and run.
Not covered
Testing the job's real reads and writes against the actual source or target tables, or catching bad data that arrives at runtime even when the code is correct; this recipe only covers testing the transformation's own logic in isolation.
Your transform looks right until the next edit breaks it
start there instead when the transformation is a plain Python function with no DataFrame in it; this recipe is for a DataFrame-to-DataFrame transformation, with its own session fixture and a schema-aware comparison.
No gate stops bad data from reaching your Spark load
reach for that once this suite ships, since it catches bad data arriving at runtime even when the transformation code itself is fully tested and correct.
Checked 25 Aug 2026
Part of the PySpark cookbook