Databricks Workflow API Integration
A backend service that lets business applications or AI agents trigger Databricks jobs, track status, retrieve results, and expose data workflows through clean APIs.
Problem
Databricks holds valuable data workflows, but triggering and consuming them from applications or AI agents usually means bespoke, brittle glue code with no consistent status tracking or result contract.
What was built / modernized
We built a Python service that wraps the Databricks REST API behind clean, documented endpoints. Applications and agents can start jobs, poll or subscribe to status, and retrieve structured results, with the service handling authentication, retries, and result normalisation.
Value delivered
- Data workflows become callable, observable services
- Consistent status tracking and result contracts across consumers
- AI agents and apps reuse the same governed integration
Technologies
- Python
- FastAPI
- Databricks REST API
- Jobs / Workflows
- PySpark
- Delta Lake
- MLflow
- Azure
- PostgreSQL
Relevant roles
- Databricks Engineer
- Python Backend Engineer
- Data Engineer
Next step
Discuss a similar project
We can adapt this pattern to your systems and provide the engineers to build it. Reach us at info@inovativi.com.