Most organizations run their transactional workloads on legacy PostgreSQL, MySQL, or commercial RDBMS platforms that sit outside the lakehouse - creating data silos, fragile ETL pipelines, and governance blind spots. Databricks Lakebase changes the equation: a fully managed, serverless PostgreSQL database built directly into the Data Intelligence Platform, powered by Neon technology and designed for developers and AI agents alike.
In this joint webinar with Advancing Analytics, we will walk through why and how to migrate your operational databases to Lakebase - and what this means for unifying your OLTP and analytical estates under a single governance, security, and compute model.
Whether you are running self-managed Postgres, Amazon RDS/Aurora, Azure Database for PostgreSQL, or Cloud SQL - this session will show you how Lakebase eliminates operational overhead, slashes costs, and unlocks capabilities that legacy managed databases simply cannot offer.
What You Will Learn
- The strategic case for consolidating OLTP workloads onto Databricks Lakebase and the benefits of a unified data platform.
- How Lakebase's serverless PostgreSQL architecture (autoscaling, scale-to-zero, branching) compares to traditional managed databases like RDS, Aurora, and Azure Database for PostgreSQL.
- Querying Lakebase data from the Databricks SQL Editor
- Practical migration patterns and tooling for moving existing PostgreSQL and similar databases to Lakebase, including the upcoming Import Database capability.
- How to leverage Native Lakehouse Sync and Accelerated Synced Tables to eliminate fragile ETL between your operational and analytical layers.
- Real-world migration approaches from Advancing Analytics' delivery experience across Financial Services, Healthcare, and Media sectors.
- What's Coming Next - Roadmap Highlights with a live demo of the production-grade application:
- Provisioning a Lakebase database in seconds
- Setting up branching for dev/test isolation
- Configuring Accelerated Synced Tables for reverse ETL
- Querying Lakebase data from the Databricks SQL Editor