Do we need to buy an enterprise catalog before we start?

No. Start with DataHub or OpenMetadata; you can migrate metadata later if you outgrow them. The value comes from consistent tags, ownership, and lineage—tools are interchangeable if you keep the metadata model clean.

How do we avoid slowing down engineers with access controls?

Use tag-based policies and short-lived credentials. Default to masked views and automate low-risk access approvals. Measure access SLA as a product KPI; aim for hours, not days.

What if our data lives across Snowflake, S3, and Databricks?

Unify governance at the metadata and policy layers: catalog for tags/ownership, OpenLineage for flows, and policy-as-code (OPA/Terraform) to push consistent rules into Snowflake, Lake Formation, and Ranger.

Can we quantify ROI on governance?

Yes: fewer incidents (rate/MTTR), higher DQ pass rate, faster audit cycles, reduced time-to-access, and cost savings from pruning unused data flows identified via lineage.

Where does AI fit into this?

Treat model inputs/outputs as governed datasets. Tag features with sensitivity, enforce contracts on feature schemas, and log lineage into your catalog. Mask or tokenize PII before features hit training or inference.

Data-engineering · Oct 2, 2025 · 10 minute read

The Day the Auditor Found Your S3 Bucket: A Data Governance Framework Engineers Don’t Hate

A pragmatic, engineering-first approach to data governance that boosts reliability, tightens security, and actually ships.

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The Day the Auditor Found Your S3 Bucket: A Data Governance Framework Engineers Don’t Hate

Key takeaways

Implementation checklist