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Supported Frameworks

All frameworks below are pre-installed in the Databricks Apps runtime. Claude already knows how to use them — this guide covers only Databricks-specific patterns. For full examples and recipes, see the Databricks Apps Cookbook.

Dash

Best for: Production dashboards, BI tools, complex interactive visualizations.

Critical: Always use dash-bootstrap-components for layout and styling.

import dash
import dash_bootstrap_components as dbc

app = dash.Dash(
    __name__,
    external_stylesheets=[dbc.themes.BOOTSTRAP, dbc.icons.FONT_AWESOME],
    title="My Dashboard",
)
Detail Value
Pre-installed version 2.18.1
app.yaml command ["python", "app.py"]
Default port 8050 — override in code: app.run(port=int(os.environ.get("DATABRICKS_APP_PORT", 8000)))
Auth header request.headers.get('x-forwarded-access-token') (Flask under the hood)

Databricks tips:

  • Use dbc.themes.BOOTSTRAP and dbc.icons.FONT_AWESOME for consistent styling
  • Use Bootstrap badge color names ("success", "danger"), not hex colors, for dbc.Badge
  • Use prevent_initial_call=True on expensive callbacks
  • Use dcc.Store for client-side caching

Cookbook: apps-cookbook.dev/docs/category/dash — tables, volumes, AI/ML, workflows, dashboards, compute, auth, external services.

Streamlit

Best for: Rapid prototyping, data science apps, internal tools, notebook-to-app workflow.

Critical: Always use @st.cache_resource for database connections.

import streamlit as st
from databricks.sdk.core import Config
from databricks import sql

st.set_page_config(page_title="My App", layout="wide")  # Must be first!

@st.cache_resource(ttl=300)
def get_connection():
    cfg = Config()
    return sql.connect(
        server_hostname=cfg.host,
        http_path="/sql/1.0/warehouses/<id>",
        credentials_provider=lambda: cfg.authenticate,
    )
Detail Value
Pre-installed version 1.38.0
app.yaml command ["streamlit", "run", "app.py"]
Auth header st.context.headers.get('x-forwarded-access-token')

Databricks tips:

  • st.set_page_config() must be the first Streamlit command
  • @st.cache_resource for connections/models; @st.cache_data(ttl=...) for query results
  • Use st.form() to batch inputs and prevent reruns on every keystroke
  • Use st.column_config for formatted DataFrames (currency, dates)

Cookbook: apps-cookbook.dev/docs/category/streamlit — tables, volumes, AI/ML, workflows, visualizations, dashboards, compute, auth, external services.

Gradio

Best for: ML model demos, chat interfaces, image/audio/video processing UIs.

Critical: Use gr.Request parameter to access auth headers.

import os
import gradio as gr
import requests
from databricks.sdk.core import Config

cfg = Config()

def predict(message, request: gr.Request):
    user_token = request.headers.get("x-forwarded-access-token")
    # Query model serving endpoint
    headers = {**cfg.authenticate(), "Content-Type": "application/json"}
    resp = requests.post(
        f"https://{cfg.host}/serving-endpoints/my-model/invocations",
        headers=headers,
        json={"inputs": [{"prompt": message}]},
    )
    return resp.json()["predictions"][0]

demo = gr.Interface(fn=predict, inputs="text", outputs="text")
port = int(os.environ.get("DATABRICKS_APP_PORT", 8000))
demo.launch(server_name="0.0.0.0", server_port=port)
Detail Value
Pre-installed version 4.44.0
app.yaml command ["python", "app.py"]
Default port 7860 — override in code: server_port=int(os.environ.get("DATABRICKS_APP_PORT", 8000))
Auth header request.headers.get('x-forwarded-access-token') via gr.Request

Databricks tips:

  • Natural fit for model serving endpoint integration
  • Use gr.ChatInterface for conversational AI demos
  • Use gr.Blocks for complex multi-component layouts

Docs: gradio.app/docs

Flask

Best for: Custom REST APIs, lightweight web apps, webhook receivers.

Critical: Deploy with Gunicorn — never use Flask's dev server in production.

from flask import Flask, request, jsonify
from databricks.sdk.core import Config
from databricks import sql

app = Flask(__name__)
cfg = Config()

@app.route("/api/data")
def get_data():
    conn = sql.connect(
        server_hostname=cfg.host,
        http_path="/sql/1.0/warehouses/<id>",
        credentials_provider=lambda: cfg.authenticate,
    )
    with conn.cursor() as cursor:
        cursor.execute("SELECT * FROM catalog.schema.table LIMIT 10")
        return jsonify(cursor.fetchall())
Detail Value
Pre-installed version 3.0.3
app.yaml command ["gunicorn", "app:app", "-w", "4", "-b", "0.0.0.0:8000"]
Auth header request.headers.get('x-forwarded-access-token')

Databricks tips:

  • Use connection pooling (Flask doesn't cache connections like Streamlit)
  • Gunicorn workers (-w 4) handle concurrent requests
  • Use request.headers for user authorization tokens

FastAPI

Best for: Modern async APIs, auto-generated OpenAPI/Swagger docs, high-performance backends.

Critical: Deploy with uvicorn.

from fastapi import FastAPI, Request
from databricks.sdk.core import Config
from databricks import sql

app = FastAPI(title="My API")
cfg = Config()

@app.get("/api/data")
async def get_data(request: Request):
    user_token = request.headers.get("x-forwarded-access-token")
    conn = sql.connect(
        server_hostname=cfg.host,
        http_path="/sql/1.0/warehouses/<id>",
        access_token=user_token,
    )
    with conn.cursor() as cursor:
        cursor.execute("SELECT * FROM catalog.schema.table LIMIT 10")
        return cursor.fetchall()
Detail Value
Pre-installed version 0.115.0
app.yaml command ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
Auth header request.headers.get('x-forwarded-access-token') via Request

Databricks tips:

  • Auto-generates OpenAPI docs at /docs (Swagger) and /redoc
  • Databricks SQL connector is synchronous — use asyncio.to_thread() for async endpoints
  • Good choice for API backends that serve a React/TypeScript frontend

Cookbook: apps-cookbook.dev/docs/category/fastapi — getting started, endpoint examples.

Reflex

Best for: Full-stack Python apps with reactive UIs, no JavaScript required.

import reflex as rx
from databricks.sdk.core import Config

cfg = Config()

class State(rx.State):
    data: list[dict] = []

    def load_data(self):
        from databricks import sql
        conn = sql.connect(
            server_hostname=cfg.host,
            http_path="/sql/1.0/warehouses/<id>",
            credentials_provider=lambda: cfg.authenticate,
        )
        with conn.cursor() as cursor:
            cursor.execute("SELECT * FROM catalog.schema.table LIMIT 10")
            self.data = [dict(zip([d[0] for d in cursor.description], row)) for row in cursor.fetchall()]
Detail Value
app.yaml command ["reflex", "run", "--env", "prod"]
Auth header session.http_conn.headers.get('x-forwarded-access-token')

Cookbook: apps-cookbook.dev/docs/category/reflex — tables, volumes, AI/ML, workflows, dashboards, compute, auth, external services.

Common: All Frameworks

  • All frameworks are pre-installed — no need to add them to requirements.txt
  • Add only additional packages your app needs to requirements.txt
  • SDK Config() auto-detects credentials from injected environment variables
  • Apps must bind to DATABRICKS_APP_PORT env var (defaults to 8000). Streamlit is auto-configured by the runtime; for other frameworks, read the env var in code or hardcode 8000 in app.yaml command. Never use 8080
  • For framework-specific deployment commands, see 4-deployment.md
  • For authorization integration, see 1-authorization.md