Database change management on Databricks: migrations, environments, and AI-generated change
You wouldn’t ship untested SQL Server database changes – Why is Databricks different?
Databricks is where the data estate is growing, and increasingly where AI workloads run and generate change. But schema change there still happens the hard way: views and stored procedures managed through manually versioned scripts, drift between workspaces discovered when a deployment fails, and no reliable record of what changed, when, where, or why. As AI raises the volume and speed of schema change, these gaps widen.
Join the database experts for a live discussion on practical ways to introduce a governance framework into Databricks and Lakebase, so problems surface before something breaks, not after.
Here’s what they’ll cover:
- Why clean, governed data must come before AI workloads – not after
- Actual steps to migrate SQL Server and Oracle workloads to Databricks
- Pairing Flyway Enterprise and Unity Catalog for stronger schema management
- Why good governance is less disruptive than you think
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