Databricks
Add your lakehouse data to any app.
Databricks is a unified data and AI platform where teams run their most complex analytics and ML workloads. Now your app can query it directly: surface model outputs, pull lakehouse data, and build tools on top of your most powerful data infrastructure - without spinning up a notebook.
What’s included
- Query Databricks SQL tables
- Read from Unity Catalog
- Surface model outputs and feature data
- Access lakehouse tables directly
What you can build
ML teams
Model performance tracker
A model comparison view with per-metric charts, version history, and drift indicators.
Build a dashboard that queries our Databricks model_metrics table and shows accuracy, precision, and recall for each model in production over the last 30 daysData analysts
Analyst self-serve tool
A query interface that translates questions to SQL, shows results in a table, and logs every query run.
Build a tool that lets analysts on my team query our Databricks Unity Catalog tables by typing a question in plain EnglishData engineers
Pipeline health board
A job history view with run status badges, duration charts, and expandable error logs.
Build a board that shows the last 10 runs of each job in our Databricks workspace with status, duration, and any error messagesHow to get started
- Connect your Databricks workspace from the Lovable connectors panel using your host URL and personal access token, select which catalogs and schemas your app can query, then start pulling live data and model outputs into your app.
Works well with
Snowflake — Compare results across both warehouses, or layer Snowflake data alongside your Databricks lakehouse in a single view.
Slack — Post automated Databricks job alerts and model performance summaries to your team's Slack channels.
Google Sheets — Export query results and model outputs from Databricks into a spreadsheet your team can review and share.
