Lakebase implementation, from the schema to the application on top

A fully managed serverless Postgres that runs inside the Databricks platform, using open source Postgres rather than a fork or a new SQL dialect. Compute is separated from storage and scales to zero when idle, and it adds database branching, point-in-time recovery and read replicas. It registers as a catalog in Unity Catalog, so the governance you already run covers it.

Three things Databricks names: real-time transactional applications, persistent memory for AI agents, and operational data analytics where the operational copy and the analytical one have to stay in step. It is the operational database beside the lakehouse rather than a replacement for Delta tables.

Because the database is the easy half. Lakebase asks somebody to build a transactional application on top of it, and that is application engineering rather than data engineering. A firm whose decade is pipelines, dashboards and models has the wrong muscle for an API under request latency, a product surface people use all day, and a workflow that must never half-complete.

We built custom software for a decade before we went deep on Databricks, and both benches are still here: 115+ people, 80 of them Databricks-certified, average fifteen years of production experience. Lakebase sits exactly on the seam between those two things, which is an unusual place for a data consultancy to be strong and an ordinary one for us.

Only where the operational and analytical copies genuinely have to reconcile, or where an application would rather read from inside the lakehouse than sync out of it. A Postgres that already works, sitting far from your Databricks estate and reconciling fine, is not a migration worth paying for. We would rather say that in the first call than after a discovery.

Branching is the part teams underestimate. You create an instant branch of the database for a preview environment, reset it to production data, and the usual argument about test data being unrepresentative stops happening. Teams that wire that into the delivery pipeline usually see the biggest change in cycle time come from there rather than from anything the agent does.

That is one of the workloads Databricks designed it for, and the governance is the reason it is worth doing there rather than in a side database. Memory held in a Lakebase catalog registered in Unity Catalog is memory an agent reads through the same grants a person would, which is the difference between an agent you can put in front of customer data and one you cannot.