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Deploying Databricks Apps

Three deployment options: Databricks CLI (simplest), Asset Bundles (multi-environment), or MCP tools (programmatic).

Cookbook deployment guide: https://apps-cookbook.dev/docs/deploy

Option 1: Databricks CLI

Best for: quick deployments, single environment.

Step 1: Create app.yaml

command:
  - "python"        # Adjust per framework — see table below
  - "app.py"

env:
  - name: DATABRICKS_WAREHOUSE_ID
    valueFrom: sql-warehouse
  - name: USE_MOCK_BACKEND
    value: "false"

app.yaml Commands Per Framework

Framework Command
Dash ["python", "app.py"]
Streamlit ["streamlit", "run", "app.py"]
Gradio ["python", "app.py"]
Flask ["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"]
FastAPI ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
Reflex ["reflex", "run", "--env", "prod"]

Step 2: Create and Deploy

# Create the app
databricks apps create <app-name>

# Upload source code
databricks workspace mkdirs /Workspace/Users/<user>/apps/<app-name>
databricks workspace import-dir . /Workspace/Users/<user>/apps/<app-name>

# Deploy
databricks apps deploy <app-name> \
  --source-code-path /Workspace/Users/<user>/apps/<app-name>

# Add resources via UI (SQL warehouse, Lakebase, etc.)

# Check status and URL
databricks apps get <app-name>

Redeployment

databricks workspace delete /Workspace/Users/<user>/apps/<app-name> --recursive
databricks workspace import-dir . /Workspace/Users/<user>/apps/<app-name>
databricks apps deploy <app-name> \
  --source-code-path /Workspace/Users/<user>/apps/<app-name>

Option 2: Databricks Asset Bundles (DABs)

Best for: multi-environment deployments (dev/staging/prod), version-controlled infrastructure.

Recommended workflow: deploy via CLI first to validate, then generate bundle config.

Generate Bundle from Existing App

databricks bundle generate app \
  --existing-app-name <app-name> \
  --key <resource_key>

This creates:

  • resources/<key>.app.yml — app resource definition
  • src/app/ — app source files including app.yaml

Deploy with Bundles

# Validate
databricks bundle validate -t dev

# Deploy
databricks bundle deploy -t dev

# Start the app (required after deployment)
databricks bundle run <resource_key> -t dev

# Production
databricks bundle deploy -t prod
databricks bundle run <resource_key> -t prod

Key difference from other resources: environment variables go in src/app/app.yaml, not databricks.yml.

For complete DABs guidance, use the databricks-bundles skill.

Option 3: MCP Tools

For programmatic app lifecycle management, see 6-mcp-approach.md.

Post-Deployment

Check Logs

databricks apps logs <app-name>

Key patterns in logs:

  • [SYSTEM] — deployment status, file updates, dependency installation
  • [APP] — application output, framework messages
  • Deployment successful — app deployed correctly
  • App started successfully — app is running
  • Error: — check stack traces

Verify

  1. Access app URL (from databricks apps get <app-name>)
  2. Check all pages load correctly
  3. Verify data connectivity (look for backend initialization messages in logs)
  4. Test user authorization flow if enabled

Configure Permissions

  • Set CAN USE for approved users/groups
  • Set CAN MANAGE only for trusted developers
  • Verify service principal has required resource permissions