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 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

["python", "app.py"]

["streamlit", "run", "app.py"]

["python", "app.py"]

["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"]

["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

["reflex", "run", "--env", "prod"]

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

Quick Reference

Config()

DATABRICKS_CLIENT_ID

DATABRICKS_CLIENT_SECRET

x-forwarded-access-token

valueFrom

Detailed Guides

Authorization: Use 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 when connecting your app to Databricks resources — covers SQL warehouses, Lakebase, model serving, secrets, volumes, and the

valueFrom

Frameworks: See 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 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 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 for managing app lifecycle via MCP tools — covers creating, deploying, monitoring, and deleting apps programmatically. (Keywords: MCP, create app, deploy app, app logs)

Foundation Models: See examples/llm_config.py for calling Databricks foundation model APIs — covers OAuth M2M auth, OpenAI-compatible client wiring, and token caching. (Keywords: foundation model, LLM, OpenAI client, chat completions)

Workflow

1. Determine the task type:

   New app from scratch? → Use Framework Selection, then read 3-frameworks.md Setting up authorization? → Read 1-authorization.md Connecting to data/resources? → Read 2-app-resources.md Using Lakebase (PostgreSQL)? → Read 5-lakebase.md Deploying to Databricks? → Read 4-deployment.md Using MCP tools? → Read 6-mcp-approach.md Calling foundation model/LLM APIs? → See examples/llm_config.py

2. Follow the instructions in the relevant guide

3. 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

@st.cache_resource

x-forwarded-access-token

app.yaml

databricks apps logs <name>

valueFrom

requirements.txt

psycopg2

asyncpg

requirements.txt

DATABRICKS_APP_PORT

st.set_page_config()

dash-bootstrap-components

dbc.themes.BOOTSTRAP

Platform Constraints

requirements.txt

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 - full-stack apps with FastAPI + React
• databricks-bundles - deploying apps via DABs
• databricks-python-sdk - backend SDK integration
• databricks-lakebase-provisioned - adding persistent PostgreSQL state
• databricks-model-serving - serving ML models for app integration