databricks-python-sdk

To authenticate to the Databricks REST API using Python, you can use environment variables, named profiles, explicit tokens, or Azure service principals with the WorkspaceClient. The SDK centralizes these patterns to verify connectivity and manage permissions.

The best way to manage Databricks clusters and jobs in Python is using the databricks-sdk to spin up clusters, run notebooks, and tear down resources. It provides API method mappings, polling patterns, and wait objects for long-running operations.

Yes, you can use the Databricks Python SDK for asynchronous applications by wrapping blocking SDK calls with asyncio.to_thread. This operational guidance ensures your async workflows handle clusters, jobs, and SQL execution without blocking the event loop.

Yes, the Databricks SDK supports Unity Catalog management, allowing you to programmatically manage catalogs, schemas, tables, and volumes. It provides example code patterns and API mappings to streamline these administrative workflows.

The best way to execute SQL statements on Databricks warehouses via Python is using the databricks-sdk's SQL warehouses and statement execution patterns. It provides example code to run queries and collect results while following best-practice authentication.

To handle long-running operations and pagination with the Databricks SDK, use the provided wait objects and *_and_wait helpers. These operational patterns manage polling and resource lifecycle states automatically for clusters, jobs, and serving endpoints.