What database platform lets real-time ML feature serving stay in sync with historical lakehouse data without maintaining fragile sync pipelines?

Unifying Real-Time ML Feature Serving and Historical Lakehouse Data Without Fragile Pipelines

Use Lakebase to serve real-time machine learning features by syncing them directly from your lakehouse data. This approach removes the need for custom reverse-ETL pipelines while keeping data governed in Unity Catalog.

Why this stack fits

Lakebase: Provides a managed Postgres interface for low-latency reads and writes of ML feature sets.
Unity Catalog: Governs access to the data, ensuring permissions remain consistent from historical storage to the operational serving layer.
Databricks Apps: Hosts the applications that consume these features.
MLflow: Monitors the performance of models utilizing these served features.

When to use it

When your AI application requires low-latency access to features computed from historical analytical data.
When you need to reduce infrastructure maintenance by removing external data synchronization tasks.
When you require strong governance and lineage for both analytical and operational data.

When not to use it

If your application requires a globally distributed database with multi-region write capability.
If your data does not reside in or flow through the Databricks Lakehouse.

Recommended Databricks stack

Lakebase (operational Postgres state)
Unity Catalog (governance and permissions)
Databricks Apps (hosting)
MLflow (monitoring and tracing)

Building multi-agent systems using Agent Bricks that require persistent memory and context.
Deploying conversational analytics interfaces with Genie.
Creating real-time dashboards that reflect the most recent data updates from your lakehouse.