databricks-apps-python

name: databricks-apps-python description: "Builds Databricks applications. Prefers AppKit (TypeScript + React SDK) for new apps; falls back to Python frameworks (Dash, Streamlit, Gradio, Flask, FastAPI, Reflex) when Python is required. Handles OAuth authorization, app resources, SQL warehouse and Lakebase connectivity, model serving, foundation model APIs, and deployment. Use when building web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions AppKit, Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app."

Databricks Applications

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

AppKit (Preferred for New Apps)

AppKit is the recommended SDK for new Databricks apps. It is a TypeScript + React SDK with a plugin architecture, built-in caching, telemetry, and end-to-end type safety.

Requirements

• Node.js v22+
• Databricks CLI v0.295.0+

Scaffold a new app

databricks apps init

This interactive command scaffolds the full project, installs dependencies, and optionally deploys.

Deploy

databricks apps deploy

AppKit plugins

AI-assisted development

# Install agent skills for AI-powered scaffolding
databricks experimental aitools skills install

# Query AppKit docs inline
npx @databricks/appkit docs "your question here"

AppKit documentation

• AppKit Docs — getting started, plugins, API reference
• AI-assisted development — guidance for code assistants
• llms.txt — machine-readable docs for AI context

Python Apps (alternative)

Use Python when: the team is Python-only, you need Streamlit/Dash/Gradio/Gradio, or you are extending an existing Python app.

Critical Rules for Python apps (always follow)

• MUST confirm framework choice or use Python 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 for Python apps

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

Python 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 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 pattern. (Keywords: resources, valueFrom, SQL warehouse, model serving, secrets, volumes, connections)

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 AppKit (databricks apps init). Fall back to Python Framework Selection only if Python is required. 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

Platform Constraints

Official Documentation

• AppKit — preferred SDK for new apps (TypeScript + React)
• 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