Data Quality Checks: Null Rates, Type Checks, Anomaly
Implement data quality checks in pipelines: validate null rates, enforce types, detect duplicates, and flag distribution shifts using dbt or Great Expectations.
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Tags: data, quality, pipelines
Data Quality Checks: Null Rates, Type Checks, and Anomaly Detection Bad data is worse than no data. When downstream analysts and applications trust your pipeline, corrupted data propagates silently into reports, models, and decisions before anyone notices. Data quality checks are the automated gatekeepers that catch problems before they reach consumers. This guide covers the practical categories of checks you need and how to implement them — both in code and with tools like Great Expectations and dbt tests. --- The Five Dimensions of Data Quality Before writing checks, be clear on what you're measuring: Completeness — Are expected fields populated? What's the null rate? Validity — Do values conform to the expected type, format, and range? Uniqueness — Are there duplicate rows for fields…
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