| 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
| Plugin | Purpose |
|---|
| Analytics | SQL queries against Databricks SQL Warehouses — file-based, typed, cached |
| Genie | Conversational AI/BI interface with natural language queries |
| Files | Browse/upload Unity Catalog Volumes |
| Lakebase | OLTP PostgreSQL via Lakebase with OAuth token management |
AI-assisted development
databricks experimental aitools skills install
npx @databricks/appkit docs "your question here"
AppKit documentation
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
| Framework | Best For | app.yaml Command |
|---|
| Dash | Production dashboards, BI tools, complex interactivity | ["python", "app.py"] |
| Streamlit | Rapid prototyping, data science apps, internal tools | ["streamlit", "run", "app.py"] |
| Gradio | ML demos, model interfaces, chat UIs | ["python", "app.py"] |
| Flask | Custom REST APIs, lightweight apps, webhooks | ["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"] |
| FastAPI | Async APIs, auto-generated OpenAPI docs | ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] |
| Reflex | Full-stack Python apps without JavaScript | ["reflex", "run", "--env", "prod"] |
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
-
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
-
Follow the instructions in the relevant guide
-
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()
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
| Issue | Solution |
|---|
| Connection exhausted | Use @st.cache_resource (Streamlit) or connection pooling |
| Auth token not found | Check x-forwarded-access-token header — only available when deployed, not locally |
| App won't start | Check app.yaml command matches framework; check databricks apps logs <name> |
| Resource not accessible | Add resource via UI, verify SP has permissions, use valueFrom in app.yaml |
| Import error on deploy | Add missing packages to requirements.txt (pre-installed packages don't need listing) |
| Lakebase app crashes on start | psycopg2/asyncpg are NOT pre-installed — MUST add to requirements.txt |
| Port conflict | Apps must bind to DATABRICKS_APP_PORT env var (defaults to 8000). Never use 8080. Streamlit is auto-configured; for others, read the env var in code or use 8000 in app.yaml command |
| Streamlit: set_page_config error | st.set_page_config() must be the first Streamlit command |
| Dash: unstyled layout | Add dash-bootstrap-components; use dbc.themes.BOOTSTRAP |
| Slow queries | Use Lakebase for transactional/low-latency; SQL warehouse for analytical queries |
Platform Constraints
| Constraint | Details |
|---|
| Runtime | Python 3.11, Ubuntu 22.04 LTS |
| Compute | 2 vCPUs, 6 GB memory (default) |
| Pre-installed frameworks | Dash, Streamlit, Gradio, Flask, FastAPI, Shiny |
| Custom packages | Add to requirements.txt in app root |
| Network | Apps can reach Databricks APIs; external access depends on workspace config |
| User auth | Public Preview — workspace admin must enable before adding scopes |
Official Documentation
Related Skills
