Technology1 min read

Lakebase Postgres is now generally available on Google Cloud

By · Published by Everything Blog

In short

The offering leverages a Lake Transactional/Analytical Processing architecture that cleanly separates compute from storage while sharing a single storage tier. This setup provides rapid reads and writes while eliminating the overhead of duplicate data, complex ETL pipelines, and fragmented security controls. Users can initiate Lakebase projects directly inside their existing Databricks workspaces on Google Cloud and connect them using standard Postgres tools.

Key points

  • Teams can power agentic and real-time workloads without managing any underlying database…: Teams can power agentic and real-time workloads without managing any underlying database infrastructure.
  • Shared storage eliminates the burden of building traditional ETL pipelines and maintainin…: Shared storage eliminates the burden of building traditional ETL pipelines and maintaining duplicate datasets.
  • Operational and analytical environments now operate under a single, unified security and…: Operational and analytical environments now operate under a single, unified security and governance framework.

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Power agentic and mission-critical workloads on Google Cloud with fully-managed, serverless Postgres.

We're excited to announce that Lakebase Postgres is generally available on Google Cloud. Teams building on Google Cloud can now run production-grade, serverless Postgres directly on the Databricks Data + AI Platform.

The Lakebase architecture separates compute from storage, which removes the resource conflicts and infrastructure overhead of traditional databases. It also shares the same storage layer as your analytical data which eliminates the need to build and maintain pipelines. The result is a Postgres database that scales with your application, shares one governance model with the rest of your data estate, and reduces your total cost of ownership.

For years, transactional databases and analytics platforms have been kept separate. Moving operational data into the lakehouse meant building and maintaining ETL pipelines, duplicating storage and accepting data lag. Now that AI agents act on data in real-time, that operational overhead becomes a bigger liability.

Instead with Lakebase, operational workloads run on the same platform, with the same security and governance as your analytics and AI. There is no…

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