Govern Data at the Code Level: A Guide to Source Code Governance
Blog post from Foundational
Data catalogs are useful for documenting existing data assets but cannot prevent issues because their scans occur after schema, transformation, or access changes have already been deployed. Code-level governance instead analyzes SQL, Python, Java, dbt, Spark, ORM, and AI pipeline code during pull requests, checking proposed changes for schema and data-contract violations, downstream impacts, sensitive-field classification and masking needs, and accurate lineage. Integrated into CI/CD as an automated quality gate, this approach can block problematic changes before review or deployment while fitting into engineers’ established workflows. The post presents Foundational as a platform for this type of governance and cites SuperPlay’s reported 80% reduction in PR cycle time alongside a doubling of released pull requests.
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