PostgreSQL is familiar to SQL professionals, app developers, and data teams. Databricks Lakebase brings that Postgres experience into the Databricks Data Intelligence Platform as a fully managed Postgres database integrated with the Lakehouse. This opens a new pattern: building operational apps, AI agent workflows, and lightweight transactional experiences closer to governed analytical data.

In this session, we will explore where Lakebase fits, what problems it solves, and how it changes the conversation between OLTP, analytics, and AI applications. We will walk through a practical architecture for using Lakebase with Databricks Apps, Databricks SQL, Unity Catalog-governed data, and AI agent use cases. The session will also cover what SQL Server and PostgreSQL professionals should evaluate before adopting it: schema design, security, row-level security, connectivity, Data API usage, application patterns, and workload boundaries.

This is not a product tour. It is a practical guide for data professionals who want to understand how Postgres, Lakehouse, and AI application architecture are coming together.

Session Description for Reviewers

Lakebase is a fully managed Postgres database integrated into the Databricks platform, and Databricks documents it as supporting applications through Databricks Apps, external integrations, and the Data API. It also provides broad compatibility with standard PostgreSQL features and extensions. This session is designed for SQL Saturday attendees who understand relational databases and want a practical explanation of where Lakebase fits in a modern data platform.

The session will focus on real implementation questions: when to use Lakebase, when not to use it, how to connect application patterns to Lakehouse data, how to think about governed access, and how SQL professionals can apply their existing relational skills to Databricks Lakebase.