Data Quality Checks: Null Rates, Type Checks, and Anomaly Detection
Implement data quality checks in pipelines: validate null rates, enforce types, detect duplicates, and alert on 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. Completeness Checks (Null Rate) Null rates measure what percentage of a column's values are missing. Every column should have a defined acceptable null rate — zero for primary keys, low for required fields, higher for optional attributes. Python Implementation Tracking Null Rate Trends…
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