/databricks-apps-python

name: databricks-apps-python
description: "Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs."
compatibility: Requires databricks CLI (>= v1.0.0)
metadata:
  version: "0.1.0"
parent: databricks-core

Databricks Applications — Python backends

> **First, confirm this skill is the right one.** The default for new Databricks Apps is **[databricks-apps](../databricks-apps/SKILL.md)** (AppKit — Node.js + TypeScript + React SDK). Load that skill first unless the user explicitly asks for a Python backend, is extending an existing Python app, or the team is Python-only. Everything below is the Python-backend alternative.

Critical Rules for Python apps (always follow)

• **MUST** confirm framework choice or use [Python Framework Selection](#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 | |-----------|----------|------------------| | **FastAPI** (default) | Any Python backend by default — async APIs, auto-generated OpenAPI docs, JSON-serving apps | `["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]` | | **Flask** | Custom REST APIs, lightweight apps, webhooks | `["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"]` | | **Dash** | Production dashboards, BI tools, complex interactivity | `["python", "app.py"]` | | **Streamlit** | Rapid prototyping, data science apps, internal tools where the UI is a series of Python widgets | `["streamlit", "run", "app.py"]` | | **Gradio** | ML demos, model interfaces, chat UIs | `["python", "app.py"]` | | **Reflex** | Full-stack Python apps without JavaScript | `["reflex", "run", "--env", "prod"]` |

**Default: FastAPI.** Reach for FastAPI unless the user explicitly asks for Streamlit-style widget prototyping (Streamlit), a heavy dashboard grid (Dash), or a Gradio-style ML demo. FastAPI pairs naturally with a JS/HTML frontend or a JSON-consuming caller — the same posture `databricks-apps` uses on the Node side.

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Quick Reference

| Concept | Details | |---------|---------| | **Runtime** | Python 3.11, Ubuntu 22.04, 2 vCPU, 6 GB RAM | | **Pre-installed** | Dash 2.18.1, Streamlit 1.38.0, Gradio 4.44.0, Flask 3.0.3, FastAPI 0.115.0 | | **Auth (app)** | Service principal via `Config()` — auto-injected `DATABRICKS_CLIENT_ID`/`DATABRICKS_CLIENT_SECRET` | | **Auth (user)** | `x-forwarded-access-token` header — see [references/1-authorization.md](references/1-authorization.md) | | **Resources** | `valueFrom` in app.yaml — see [references/2-app-resources.md](references/2-app-resources.md) | | **SDK / Foundation Models / Vector Search / Model Serving** | Use the `databricks-python-sdk` skill — same `WorkspaceClient` and OpenAI-compatible foundation-model patterns work inside a Databricks App | | **Docs** | https://docs.databricks.com/dev-tools/databricks-apps/ |

---

Detailed Guides

**Authorization**: Use [references/1-authorization.md](references/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 [references/2-app-resources.md](references/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 [references/3-frameworks.md](references/3-frameworks.md) for Databricks-specific patterns per framework — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex — with auth integration and deployment commands. (Keywords: FastAPI, Flask, Dash, Streamlit, Gradio, Reflex, framework selection)

**Deployment**: Use [references/4-deployment.md](references/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 [references/5-lakebase.md](references/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)

**CLI commands**: Use [references/6-cli-approach.md](references/6-cli-approach.md) for managing app lifecycle via CLI — covers creating, deploying, monitoring, and deleting apps. (Keywords: CLI, create app, deploy app, app logs)

**Foundation Models / SDK / Vector Search / Model Serving**: Use the **[databricks-python-sdk](../databricks-python-sdk/SKILL.md)** skill for the OpenAI-compatible foun