Pytest Basics Every Data Engineer Should Know to Prevent Pipeline Failures

A technical guide published on DEV Community outlines foundational pytest practices specifically aimed at data engineers who build and maintain data pipelines. The article explains that untested pipeline code is a liability, as silent errors in transformations can produce incorrect results in production without any obvious failure. It walks through the core Arrange-Act-Assert testing pattern using practical examples such as cleaning raw CSV values and validating DataFrame schemas with PySpark. The guide also covers how to use pytest.raises to confirm correct exception handling and pytest.mark.xfail to manage known bugs without blocking test runs. Finally, it addresses mocking external dependencies like S3 and databases so that unit tests remain fast, isolated, and deterministic.