Databricks Python Application

Build Python-based Databricks applications. For full examples and recipes, see the Databricks Apps Cookbook.

Critical Rules (always follow)

• MUST confirm framework choice or use [Framework Selection](#framework-selection) below
• MUST use SDK Config() for authentication (never hardcode tokens)
• MUST use app.yaml valueFrom for resources (never hardcode resource IDs)
• MUST use dash-bootstrap-components for Dash app layout and styling
• MUST use @st.cache_resource for Streamlit database connections
• MUST deploy Flask with Gunicorn, FastAPI with uvicorn (not dev servers)

Required Steps

Copy this checklist and verify each item:

- [ ] Framework selected
- [ ] Auth strategy decided: app auth, user auth, or both
- [ ] App resources identified (SQL warehouse, Lakebase, serving endpoint, etc.)
- [ ] Backend data strategy decided (SQL warehouse, Lakebase, or SDK)
- [ ] Deployment method: CLI or DABs

Framework Selection

Default: Recommend Streamlit for prototypes, Dash for production dashboards, FastAPI for APIs, Gradio for ML demos.

Quick Reference

Detailed Guides

Authorization: Use [1-authorization.md](1-authorization.md) when configuring app or user authorization — covers service principal auth, on-behalf-of user tokens, OAuth scopes, and per-framework code examples. (Keywords: OAuth, service principal, user auth, on-behalf-of, access token, scopes)

App resources: Use [2-app-resources.md](2-app-resources.md) when connecting your app to Databricks resources — covers SQL warehouses, Lakebase, model serving, secrets, volumes, and the valueFrom pattern. (Keywords: resources, valueFrom, SQL warehouse, model serving, secrets, volumes, connections)

Frameworks: See [3-frameworks.md](3-frameworks.md) for Databricks-specific patterns per framework — covers Dash, Streamlit, Gradio, Flask, FastAPI, and Reflex with auth integration, deployment commands, and Cookbook links. (Keywords: Dash, Streamlit, Gradio, Flask, FastAPI, Reflex, framework selection)

Deployment: Use [4-deployment.md](4-deployment.md) when deploying your app — covers Databricks CLI, Asset Bundles (DABs), app.yaml configuration, and post-deployment verification. (Keywords: deploy, CLI, DABs, asset bundles, app.yaml, logs)

Lakebase: Use [5-lakebase.md](5-lakebase.md) when using Lakebase (PostgreSQL) as your app's data layer — covers auto-injected env vars, psycopg2/asyncpg patterns, and when to choose Lakebase vs SQL warehouse. (Keywords: Lakebase, PostgreSQL, psycopg2, asyncpg, transactional, PGHOST)

MCP tools: Use [6-mcp-approach.md](6-mcp-approach.md) for managing app lifecycle via MCP tools — covers creating, deploying, monitoring, and deleting apps programmatically. (Keywords: MCP, create app, deploy app, app logs)

Workflow

1. Determine the task type:

New app from scratch? → Use [Framework Selection](#framework-selection), then read [3-frameworks.md](3-frameworks.md) Setting up authorization? → Read [1-authorization.md](1-authorization.md) Connecting to data/resources? → Read [2-app-resources.md](2-app-resources.md) Using Lakebase (PostgreSQL)? → Read [5-lakebase.md](5-lakebase.md) Deploying to Databricks? → Read [4-deployment.md](4-deployment.md) Using MCP tools? → Read [6-mcp-approach.md](6-mcp-approach.md)

1. Follow the instructions in the relevant guide
2. For full code examples, browse https://apps-cookbook.dev/

Core Architecture

All Python Databricks apps follow this pattern:

app-directory/
├── app.py                 # Main application (or framework-specific name)
├── models.py              # Pydantic data models
├── backend.py             # Data access layer
├── requirements.txt       # Additional Python dependencies
├── app.yaml               # Databricks Apps configuration
└── README.md

Backend Toggle Pattern

import os
from databricks.sdk.core import Config

USE_MOCK = os.getenv("USE_MOCK_BACKEND", "true").lower() == "true"

if USE_MOCK:
    from backend_mock import MockBackend as Backend
else:
    from backend_real import RealBackend as Backend

backend = Backend()

SQL Warehouse Connection (shared across all frameworks)

from databricks.sdk.core import Config
from databricks import sql

cfg = Config()  # Auto-detects credentials from environment
conn = sql.connect(
    server_hostname=cfg.host,
    http_path=f"/sql/1.0/warehouses/{os.getenv('DATABRICKS_WAREHOUSE_ID')}",
    credentials_provider=lambda: cfg.authenticate,
)

Pydantic Models

from pydantic import BaseModel, Field
from datetime import datetime
from enum import Enum

class Status(str, Enum):
    ACTIVE = "active"
    PENDING = "pending"

class EntityOut(BaseModel):
    id: str
    name: str
    status: Status
    created_at: datetime

class EntityIn(BaseModel):
    name: str = Field(..., min_length=1)
    status: Status = Status.PENDING

Common Issues

Platform Constraints

Official Documentation

• Databricks Apps Overview — main docs hub
• Apps Cookbook — ready-to-use code snippets (Streamlit, Dash, Reflex, FastAPI)
• Authorization — app auth and user auth
• Resources — SQL warehouse, Lakebase, serving, secrets
• app.yaml Reference — command and env config
• System Environment — pre-installed packages, runtime details

Related Skills

• [databricks-app-apx](../databricks-app-apx/SKILL.md) - full-stack apps with FastAPI + React
• [databricks-asset-bundles](../databricks-asset-bundles/SKILL.md) - deploying apps via DABs
• [databricks-python-sdk](../databricks-python-sdk/SKILL.md) - backend SDK integration
• [databricks-lakebase-provisioned](../databricks-lakebase-provisioned/SKILL.md) - adding persistent PostgreSQL state
• [databricks-model-serving](../databricks-model-serving/SKILL.md) - serving ML models for app integration