From Data Chaos to a Trust Score for Every Table
DatabricksUnity Catalog · Lakeflow Connect · Databricks Apps +3
Databricks' own internal Data Platform team rebuilt data governance across its internal lakehouse of more than 100,000 tables spanning engineering, go-to-market, HR, finance and product telemetry, using Unity Catalog to move from inconsistent, team-by-team access patterns to automatic, measurable governance. The team built a universal classification model tagging every table by sensitivity and domain, an internal system called Fortress enforcing purpose-bound, time-limited access via Unity Catalog's security APIs, and a continuous Data Governance Score (0-100) per dataset covering documentation, reliability and governance, propagated automatically through data lineage. The resulting governance layer now underpins AI agents used across every function at Databricks, from engineering to finance to facilities, letting teams check a dataset's governance score before using it to train a model instead of running manual reviews.
Overview
Databricks' own internal Data Platform team rebuilt data governance across its internal lakehouse of more than 100,000 tables spanning engineering, go-to-market, HR, finance and product telemetry, using Unity Catalog to move from inconsistent, team-by-team access patterns to automatic, measurable governance. The team built a universal classification model tagging every table by sensitivity and domain, an internal system called Fortress enforcing purpose-bound, time-limited access via Unity Catalog's security APIs, and a continuous Data Governance Score (0-100) per dataset covering documentation, reliability and governance, propagated automatically through data lineage. The resulting governance layer now underpins AI agents used across every function at Databricks, from engineering to finance to facilities, letting teams check a dataset's governance score before using it to train a model instead of running manual reviews.
This entry has 10 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Before Unity Catalog, governance was inconsistent: access patterns varied by team, classifications were incomplete, and no one could confidently say which datasets were safe for AI training. Security, legal and engineering teams spent hours chasing spreadsheets to answer basic questions during compliance reviews, and new AI projects stalled because no one could confirm whether a dataset met privacy requirements.
The solution
Reported business value
Over 100,000 tables are now classified and governed automatically, eliminating manual audit cycles, with the Data Governance Score giving every dataset a measurable trust rating tied to AI readiness, used daily by more than 10,000 Databricks employees across every function.
Sources
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