databricks-app-python

To build a Python Databricks app, choose Dash, Streamlit, or Flask, follow the framework-specific guide to scaffold the project, validate with a mock backend, then use the provided deployment workflows for Databricks Apps.

The best way to structure a Databricks app connecting to Unity Catalog is using the core architecture patterns, which include Pydantic data models and backend implementations designed for seamless connectivity.

Yes, the Databricks SDK supports local app development by providing both mock and real backend patterns. You can run a mock backend to validate the workflow locally before switching to a real backend.

You can use Flask to build a Databricks app, as the framework provides framework-agnostic guides and specific sections for Dash, Streamlit, and Flask, including structure, components, and best practices.

Deploying Databricks apps requires Python tooling, the Databricks SDK, and framework-specific dependencies to scaffold, run, and deploy the applications on the Databricks platform.

You should switch from a mock backend to a real backend in a Databricks app after validating your local workflow. The skill provides both mock and real backend patterns to transition from local setup to production deployment.