Databricks Apps Development
FIRST: Use the parent databricks skill for CLI basics, authentication, profile selection, and data exploration commands.
Build apps that deploy to Databricks Apps platform.
Required Reading by Phase
| Phase | READ BEFORE proceeding |
|---|---|
| Scaffolding | Parent databricks skill (auth, warehouse discovery) |
| Writing SQL queries | SQL Queries Guide |
| Writing UI components | Frontend Guide |
Using useAnalyticsQuery | AppKit SDK |
| Adding API endpoints | tRPC Guide |
Generic Guidelines
These apply regardless of framework:
- •Deployment:
databricks apps deploy --profile <PROFILE>(⚠️ USER CONSENT REQUIRED) - •Validation:
databricks apps validatebefore deploying - •App name: Must be ≤26 characters, lowercase letters/numbers/hyphens only (no underscores). dev- prefix adds 4 chars, max 30 total.
- •Smoke tests: ALWAYS update
tests/smoke.spec.tsselectors BEFORE running validation. Default template checks for "Minimal Databricks App" heading and "hello world" text — these WILL fail in your custom app. See testing guide. - •Authentication: covered by parent
databricksskill
Project Structure (after databricks apps init --features analytics)
- •
client/src/App.tsx— main React component (start here) - •
config/queries/*.sql— SQL query files (queryKey = filename without .sql) - •
server/server.ts— backend entry (tRPC routers) - •
tests/smoke.spec.ts— smoke test (⚠️ MUST UPDATE selectors for your app) - •
client/src/appKitTypes.d.ts— auto-generated types (npm run typegen)
Development Workflow (FOLLOW THIS ORDER)
- •Create SQL files in
config/queries/ - •Run
npm run typegen— verify all queries show ✓ - •Read
client/src/appKitTypes.d.tsto see generated types - •THEN write
App.tsxusing the generated types - •Update
tests/smoke.spec.tsselectors - •Run
databricks apps validate
DO NOT write UI code before running typegen — types won't exist and you'll waste time on compilation errors.
When to Use What
- •Read data → display in chart/table: Use visualization components with
queryKeyprop - •Read data → custom display (KPIs, cards): Use
useAnalyticsQueryhook - •Read data → need computation before display: Still use
useAnalyticsQuery, transform client-side - •Call ML model endpoint: Use tRPC
- •Write/update data (INSERT/UPDATE/DELETE): Use tRPC
- •⚠️ NEVER use tRPC to run SELECT queries — always use SQL files in
config/queries/
Frameworks
AppKit (Recommended)
TypeScript/React framework with type-safe SQL queries and built-in components.
Official Documentation — the source of truth for all API details:
npx @databricks/appkit docs # ← ALWAYS start here to see available pages npx @databricks/appkit docs <path> # then use paths from the index
DO NOT guess doc paths. Run without args first, pick from the index. Docs are the authority on component props, hook signatures, and server APIs — skill files only cover anti-patterns and gotchas.
Scaffold (requires --warehouse-id, see parent skill; DO NOT use npx):
databricks apps init --description "<DESC>" --features analytics --warehouse-id <ID> --name <NAME> --run none --profile <PROFILE>
READ AppKit Overview for project structure, workflow, and pre-implementation checklist.
Other Frameworks
Databricks Apps supports any framework that can run as a web server (Flask, FastAPI, Streamlit, Gradio, etc.). Use standard framework documentation - this skill focuses on AppKit.