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What is Lakebase?

Preview

This feature is in Public Preview in the following regions: us-east-1, us-west-2, eu-west-1, ap-southeast-1, ap-southeast-2, eu-central-1, us-east-2, ap-south-1.

This page introduces Databricks Lakebase, a Postgres OLTP engine, integrated into the Databricks Data Intelligence Platform. A database instance is a compute type that provides fully managed storage and compute for a Postgres database.

Overview​

An online transaction processing (OLTP) database is a specialized type of database system designed to efficiently handle high volumes of real-time transactional data. Lakebase allows you to create an OLTP database on Databricks, and integrate OLTP workloads with your Lakehouse. This OLTP database enables you to create and manage databases stored in Databricks-managed storage.

Using an OLTP database in conjunction with the Databricks platform significantly reduces application complexity. Lakebase is well integrated with Feature engineering and serving, SQL warehouses, and Databricks Apps. It is an simple and performant way to sync data between OLTP and online analytical processing (OLAP) workloads.

Based on Postgres and fully integrated with the Databricks Data Intelligence Platform, Lakebase inherits several core platform capabilities, including:

  • Simplified management: Leverages existing Databricks infrastructure to deploy instances with decoupled compute and storage, managed change data capture with Delta Lake, and support for multi-cloud deployments.
  • Integrated artificial intelligence (AI) and machine learning (ML) capabilities: Supports feature and model serving, retrieval-augmented generation (RAG), and other AI and ML integrations.
  • Integrated authentication and governance: Optionally, use Unity Catalog to enforce secure access to data.

Example use cases​

The following examples show how organizations in different industries use Databricks integrations for real-time decision-making and workflow automation:

  • E-commerce: Use pre-calculated customer segments and analytics to support workflows such as preferential delivery, offer targeting, and personalized product recommendations.
  • Healthcare: Manage clinical trial data and surface relevant insights through recommendation systems embedded in clinical workflows.
  • Financial services: Enable automatic market trading based on streaming data and pretrained models.
  • Retail: Use a chatbot that incorporates recent conversation history and real-time data (for example, shopping cart contents) to personalize responses and drive engagement.
  • Manufacturing: Track and manage machine telemetry and IoT data to support low-latency decision-making and automated maintenance workflows.

Workload types​

  • Data serving: Serve analytical data at a low latency to applications.
  • Store application state: Manage your workflow state in our transactional data store.
  • Feature Serving: Serve featurized data at a low latency to models.
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