Data Quality Engineering: Validation and Testing with Great Expectations

<p>Silent data quality bugs cost more than crashed pipelines ever will &mdash; they quietly corrupt dashboards, forecasts, and decisions until someone finally notices the damage. This project follows a data engineer at a grocery delivery startup as she adopts Great Expectations to turn assumptions about her data into automated, version-controlled tests. You'll learn the core Great Expectations vocabulary, write a real expectation suite covering schema, nulls, ranges, uniqueness, and referential integrity, wire validation into a production pipeline with the right fail-loud versus warn-quietly behavior, and build a data quality report that stakeholders actually read.</p>

Tools: Python, Great Expectations, Pandas, SQL, Airflow