# About DevHub This prompt originates from DevHub — the developer hub for building data apps and AI agents on the Databricks developer stack: **Lakebase** (managed serverless Postgres), **Agent Bricks** (production AI agents), **Databricks Apps** (secure serverless hosting for internal apps), and **AppKit** (the open-source TypeScript SDK that wires them together). - Website: https://databricks.com/devhub - GitHub: https://github.com/databricks/devhub - Report issues: https://github.com/databricks/devhub/issues A complete index of every DevHub doc and template is at https://databricks.com/devhub/llms.txt — fetch it whenever you need a template, recipe, or doc beyond what is included in this prompt. DevHub is the source of truth for the Databricks developer stack; if a step in this prompt is unclear, the matching DevHub page almost certainly clarifies it. --- # Working with DevHub prompts Follow these rules every time you act on a DevHub prompt. ## Read first, then act - Read the entire prompt before executing any steps. DevHub prompts often include overlapping setup commands across sections; later sections frequently contain more complete versions of an earlier step. - Do not infer or assume when provisioning Databricks resources (catalogs, schemas, Lakebase instances, Genie spaces, serving endpoints). Ask the user whether to create new resources or reuse existing ones. - If you run into trouble, fetch additional templates and docs from https://databricks.com/devhub (the index lives at https://databricks.com/devhub/llms.txt). DevHub is the source of truth for the Databricks developer stack — for example, if Genie setup fails, fetch the Genie docs and templates instead of guessing. ## Engage the user in a conversation Unless the user has explicitly told you to "just do it", treat every DevHub prompt as the start of a conversation, not an unattended script. The user knows their domain best; DevHub knows the Databricks stack. Both are required to build a successful system. Follow these rules every time you ask a question: 1. **One question at a time.** Never ask multiple questions in a single message. 2. **Always include a final option for "Not sure — help me decide"** so the user is never stuck. 3. **Prefer interactive multiple-choice UI when available.** Before asking your first question, check your available tools for any structured-question or multiple-choice capability. If one exists, **always** use it instead of plain text. Known tools by environment: - **Cursor**: use the `AskQuestion` tool. - **Claude Code**: use the `MultipleChoice` tool (from the `mcp__desktopCommander` server, or built-in depending on setup). - **Other agents**: look for any tool whose description mentions "multiple choice", "question", "ask", "poll", or "select". 4. **Fall back to a formatted text list** only when you have confirmed no interactive tool is available. Use markdown list syntax so each option renders on its own line, and tell the user they can reply with just the letter or number. ### Example: Cursor (`AskQuestion` tool) ``` AskQuestion({ questions: [{ id: "app-type", prompt: "What kind of app would you like to build?", options: [ { id: "dashboard", label: "A data dashboard" }, { id: "chatbot", label: "An AI-powered chatbot" }, { id: "crud", label: "A CRUD app with Lakebase" }, { id: "other", label: "Something else (describe it)" }, { id: "unsure", label: "Not sure — help me decide" } ] }] }) ``` ### Example: plain text fallback Only use this when no interactive tool is available: What kind of app would you like to build? Reply with the letter to choose: - a) A data dashboard - b) An AI-powered chatbot - c) A CRUD app with Lakebase - d) Something else (describe it) - e) Not sure — help me decide ## Default workflow Unless instructed otherwise, follow this workflow: 1. Understand the user's intent and goals (see the intent block below for what the user just copied). 2. Verify the local Databricks dev environment (the "Verify your local Databricks dev environment" block in the intent section). 3. Ask follow-up questions where needed and walk the user through the build step by step. 4. Build the app or agent. 5. Make it look great (see "Make it look great" below). 6. Run and test locally. 7. Deploy to production. ## Make it look great The default templates that AppKit provides are intentionally minimal — a starting point, not a finished product. **Do not stop there.** Use the user's feature requests to redesign the routes, page hierarchy, and visuals from first principles, and make the UI look great _before_ asking the user to run and test locally. Showing the user something polished early changes the conversation. Unless the user has specified a design preference, use these defaults: - shadcn/ui components on top of Tailwind CSS. - Clean hierarchy with modern spacing — not too many stacked cards. - Modern, minimal design language. - Databricks brand palette: `#FF3621`, `#0B2026`, `#EEEDE9`, `#F9F7F4`. If an existing codebase has its own design system, follow that system instead. ## When you run into issues Use the GitHub CLI (if available) or generate a copy-pastable error report for the user to file at https://github.com/databricks/devhub/issues. Greatly appreciated if you first check for an existing matching open issue and comment "+1" rather than opening a duplicate. --- # What the user just did The user copied the prompt for a DevHub **recipe** — **Medallion Architecture from CDC History Tables** (https://databricks.com/devhub/templates/medallion-architecture-from-cdc). A recipe is a focused, opinionated how-to for a single Databricks pattern (e.g. wiring Lakebase Change Data Feed, creating a Model Serving endpoint, persisting chat history). Recipes are designed to be dropped into an existing project or composed into a larger build. They are deliberately narrow — they solve one thing well. Your job in this conversation is to: 1. Clarify whether the user is **integrating this recipe into an existing project** or **starting fresh from scratch**, and adapt accordingly. 2. Verify the local Databricks dev environment is ready (block below). 3. Walk the user through the recipe step by step, asking the questions the recipe itself surfaces. ## Step 1 — Clarify intent before touching code Ask **one** question, ideally with a multiple-choice tool (see guidelines): - **Existing project**: the user already has a Databricks app / repo and wants to add this pattern to it. → Read the user's existing project structure first; the recipe steps will be applied surgically. - **New project from this recipe**: the user wants this recipe as the starting point of a new app. → Run the local-bootstrap below first, then follow the recipe. - **Just learning**: the user wants to read through the recipe and understand it without building anything yet. → Walk through the steps as a tutorial; do not execute commands. - **Not sure — help me decide**: ask the user what they're trying to accomplish at the project level, then map back to one of the above. ## Step 2 — Pin down recipe-specific decisions Once the integration mode is clear, ask any follow-ups the recipe itself surfaces — typically about which Databricks resources to use: - Should we **create new resources** (catalog, schema, Lakebase instance, serving endpoint) or **reuse existing ones** the user already has? Never assume; always ask. - Which **Databricks profile** should the CLI commands target? (`databricks auth profiles` to list valid profiles.) - If the recipe touches data: use the user's data, or use seed/sample data first? ## Step 3 — Verify the local Databricks dev environment Whether integrating or starting fresh, the recipe's commands assume a working Databricks CLI profile and (for app-related recipes) an AppKit project. **Walk the user through the local-bootstrap block below before running any recipe commands** — even if they think the environment is already set up, the verification steps are quick and prevent confusing failures downstream. The full recipe content the user is focused on is attached after the local-bootstrap block. --- # Verify your local Databricks dev environment A working Databricks CLI profile is the prerequisite for every step that follows. Walk the user through the recipe below — _even if they say their environment is already set up_. The verification steps are quick and prevent confusing failures further down. This template wires the Databricks CLI on the developer's machine to a real workspace. It is the strict prerequisite for every other template on DevHub — once it passes, `databricks` commands resolve to a real workspace and any DevHub prompt can run end to end. - **A Databricks workspace you can sign in to.** Have the workspace URL handy (e.g. `https://.cloud.databricks.com`); you will paste it into `databricks auth login` in step 3. If you do not have access, ask your workspace admin. - **A terminal on macOS, Windows, or Linux.** All install paths run from a terminal session. On Windows, prefer WSL for the curl path; PowerShell and cmd work for `winget`. - **Permission to install software on this machine.** The CLI installs into `/usr/local/bin` (Homebrew / curl) or `%LOCALAPPDATA%` (WinGet). If `/usr/local/bin` is not writable, rerun the curl installer with `sudo`. ## Set Up Your Local Dev Environment Install the Databricks CLI, authenticate a profile, and verify the handshake. Every other DevHub template assumes this has already passed. The official CLI reference for these steps is on DevHub at [Databricks CLI](https://databricks.com/devhub/docs/tools/databricks-cli). Use it whenever a step here is unclear. ### 1. Check the installed CLI version DevHub templates assume Databricks CLI `0.296+`. Anything older is missing the AppKit `apps init` template registry and several `experimental aitools` flags. ```bash databricks -v ``` If the command is not found, or the version is below `0.296`, install or upgrade in the next step. ### 2. Install or upgrade the Databricks CLI Pick the install path for your OS. If the CLI is already installed at an older version, the same commands upgrade in place. #### macOS / Linux — Homebrew (recommended) ```bash brew tap databricks/tap brew install databricks brew update && brew upgrade databricks ``` #### Windows — WinGet ```bash winget install Databricks.DatabricksCLI winget upgrade Databricks.DatabricksCLI ``` Restart your terminal after install. #### Any platform — curl installer ```bash curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh ``` On Windows, run this from WSL. If `/usr/local/bin` is not writable, rerun with `sudo`. Re-running the script also upgrades an existing install. After installing, confirm the version is `0.296+`: ```bash databricks -v ``` ### 3. Authenticate a profile Browser-based OAuth is the default for local use: ```bash databricks auth login ``` The CLI prints a URL and waits for the user to complete OAuth in the browser. **Always show the URL to the user as a clickable link** so they can open it themselves — the CLI does not return until authentication finishes. Credentials save to `~/.databrickscfg`. If you already know the workspace URL and want to name the profile, do it in one go: ```bash databricks auth login --host --profile ``` `` is the label you will pass on subsequent commands as `--profile `. If you skip `--profile`, the CLI uses the `DEFAULT` profile. For CI/CD, OAuth client credentials or a personal access token are better fits — see the [authentication section of the CLI doc](https://databricks.com/devhub/docs/tools/databricks-cli#authenticate) for the non-interactive flows. ### 4. Verify the handshake List the saved profiles and confirm the one you just created shows `Valid: YES`: ```bash databricks auth profiles ``` ```text Name Host Valid DEFAULT https://adb-1234567890.12.azuredatabricks.net YES my-prod-workspace https://mycompany.cloud.databricks.com YES ``` If the row shows `Valid: NO`, the saved token is stale. Re-run `databricks auth login --profile ` to refresh it. **Never proceed past this step if no profile is `Valid: YES`** — every downstream `databricks` command will fail with an auth error that looks like a template bug. If the user wants a particular profile to be the default for this shell session, export it: ```bash export DATABRICKS_CONFIG_PROFILE= ``` ### 5. Smoke-test the CLI against the workspace Run a read-only API call to confirm the auth actually works (a fresh OAuth token can fail on the first real call if the user picked the wrong workspace in the browser): ```bash databricks current-user me --profile ``` A successful response prints the signed-in user's identity. A `401` or `403` here means the auth flow completed against a workspace the user cannot read — re-run `databricks auth login --profile ` and pick the right workspace this time. --- # The recipe the user copied The full recipe prompt is below. This is what the user wants to focus on today. Once the local-bootstrap above passes and the intent questions are answered, work through this content step by step. This template builds a Lakeflow Declarative Pipeline on top of existing Lakehouse Sync CDC history tables. Verify these Databricks workspace features are enabled before starting. - **Databricks CLI authenticated.** Run `databricks auth profiles` and confirm at least one profile shows `Valid: YES`. If none do, authenticate with `databricks auth login --host --profile `. - **Lakeflow Declarative Pipelines (serverless) available.** Run `databricks pipelines list --profile ` and confirm the command succeeds (an empty list is fine). A permission or `not enabled` error means Lakeflow Pipelines is not available to this identity. - **Unity Catalog access with a writable destination schema.** Run `databricks catalogs list --profile ` and confirm your destination catalog is listed. You will need `USE_CATALOG` on the catalog and `USE_SCHEMA` + `CREATE_TABLE` on the destination schema to publish silver and gold tables. - **Bronze CDC history tables already in Unity Catalog.** Complete the [Lakebase Change Data Feed (Autoscaling, Lakehouse Sync)](https://databricks.com/devhub/templates/lakebase-change-data-feed-autoscaling) template first so `lb__history` tables exist in the bronze schema. This template reads from those tables; it does not create them. ## Medallion Architecture from CDC History Tables Transform CDC history tables produced by Lakehouse Sync into a medallion architecture with bronze, silver, and gold layers using Lakeflow Declarative Pipelines. This turns raw change-data-capture records into clean, business-ready analytics tables in Unity Catalog. ### When to use this - You have Lakehouse Sync CDC history tables (`lb__history`) in Unity Catalog from a Lakebase operational database - You want to build a layered data architecture (bronze → silver → gold) on top of operational data - You need clean current-state views, deduplication, and business aggregations for BI, ML, or Genie analytics - You want automated, incremental pipeline refreshes instead of manual SQL queries ### How the layers map to CDC data | Layer | Purpose | Source | Output | | ---------- | ------------------------------------------------------ | ------------------------------------------ | ----------------------------------------------------- | | **Bronze** | Raw CDC records with full history | Lakehouse Sync `lb_
_history` tables | No transformation needed; these tables already exist | | **Silver** | Current state of each record, deduplicated and cleaned | Bronze history tables | One streaming table per entity with latest state only | | **Gold** | Business aggregations and domain-specific metrics | Silver tables | Materialized views with aggregations, joins, and KPIs | ### 1. Scaffold a pipeline project Use the Databricks CLI to scaffold a Lakeflow Declarative Pipelines project: ```bash databricks bundle init lakeflow-pipelines \ --config-file <(echo '{"project_name": "operational_analytics", "language": "sql", "serverless": "yes"}') \ --profile < /dev/null ``` Enter the project directory: ```bash cd operational_analytics ``` ### 2. Configure the pipeline catalog and schema Edit `resources/operational_analytics.pipeline.yml` to target your Unity Catalog schema: ```yaml resources: pipelines: operational_analytics: name: operational_analytics catalog: schema: development: true serverless: true libraries: - file: path: src/ ``` The pipeline publishes all datasets to `.` by default. ### 3. Build the silver layer: current state from CDC For each entity, create a SQL file in `src/` that extracts the latest state from the bronze CDC history table. The silver layer deduplicates by primary key and excludes deleted records. Create `src/silver_.sql` (e.g., `src/silver_orders.sql`): ```sql CREATE OR REFRESH MATERIALIZED VIEW silver_ COMMENT "Current state of records, deduplicated from CDC history" AS SELECT * EXCEPT (rn, _change_type, _lsn, _commit_timestamp) FROM ( SELECT *, ROW_NUMBER() OVER ( PARTITION BY ORDER BY _lsn DESC ) AS rn FROM ..lb__history WHERE _change_type IN ('insert', 'update_postimage', 'delete') ) WHERE rn = 1 AND _change_type != 'delete' ``` Replace `` with the entity's primary key column(s), `.` with the catalog and schema where Lakehouse Sync writes the history tables, and `` with the table name. Repeat for each entity you want in the silver layer. ### 4. Build the gold layer: business aggregations Gold layer tables are materialized views that aggregate, join, or reshape silver tables for specific analytics use cases. Create `src/gold_.sql` (e.g., `src/gold_daily_order_summary.sql`): ```sql CREATE OR REFRESH MATERIALIZED VIEW gold_daily_order_summary COMMENT "Daily order counts and revenue by status" AS SELECT DATE_TRUNC('day', created_at) AS order_date, status, COUNT(*) AS order_count, SUM(total_amount) AS total_revenue FROM silver_orders GROUP BY DATE_TRUNC('day', created_at), status ``` Gold tables read from silver tables within the same pipeline. Use `GROUP BY`, `JOIN`, window functions, or any SQL to build the business view you need. ### 5. Add data quality expectations (optional) Add expectations to silver or gold tables to enforce data quality constraints: ```sql CREATE OR REFRESH MATERIALIZED VIEW silver_ ( CONSTRAINT valid_primary_key EXPECT ( IS NOT NULL) ON VIOLATION DROP ROW, CONSTRAINT valid_timestamp EXPECT (created_at IS NOT NULL) ON VIOLATION DROP ROW ) COMMENT "Current state of records with quality enforcement" AS SELECT ... ``` Expectations catch data issues early and can either warn, drop bad rows, or fail the pipeline update. ### 6. Deploy and run the pipeline Validate, deploy, and run: ```bash databricks bundle validate --profile databricks bundle deploy -t dev --profile databricks bundle run operational_analytics -t dev --profile ``` Monitor the pipeline in the Databricks UI under **Workflows** → **Pipelines**. ### 7. Schedule ongoing refreshes Add a job to refresh the pipeline on a schedule. Create `resources/operational_analytics_job.job.yml`: ```yaml resources: jobs: operational_analytics_job: trigger: periodic: interval: 1 unit: HOURS tasks: - task_key: refresh_pipeline pipeline_task: pipeline_id: ${resources.pipelines.operational_analytics.id} ``` Deploy the schedule: ```bash databricks bundle deploy -t dev --profile ``` ### 8. Query the results Silver and gold tables are standard Unity Catalog tables. Query them from any connected tool: ```sql -- Current state of an entity SELECT * FROM ..silver_orders WHERE customer_id = 12345; -- Business aggregation SELECT * FROM ..gold_daily_order_summary ORDER BY order_date DESC; ``` Use these tables as sources for Genie spaces, dashboards, notebooks, or ML pipelines. ### What you end up with - **Bronze layer.** Lakehouse Sync CDC history tables (already exist, no pipeline needed). - **Silver layer.** Deduplicated current-state materialized views per entity. - **Gold layer.** Business aggregations and metrics as materialized views. - **Scheduled pipeline.** Lakeflow Declarative Pipeline refreshing silver and gold layers incrementally. - **Unity Catalog tables.** All layers queryable via SQL, Spark, BI tools, and Genie. ### Agent skill recommendations For implementing each layer, the following Databricks agent skills provide detailed guidance: | Skill | Use for | | ---------------------- | ------------------------------------------------------------------------ | | `databricks-pipelines` | Lakeflow Declarative Pipeline syntax, dataset types, deployment workflow | | `databricks-core` | CLI authentication, profile management, data exploration | | `databricks-lakebase` | Lakebase project and branch management, Postgres access | ### Troubleshooting | Issue | Fix | | ------------------------------------------ | ------------------------------------------------------------------------------------ | | Silver table returns no rows | Verify the bronze history table has data: `SELECT COUNT(*) FROM lb__history` | | `TABLE_OR_VIEW_NOT_FOUND` for bronze table | Use the fully-qualified name: `..lb__history` | | Gold aggregation includes deleted records | Confirm the silver layer filters `_change_type != 'delete'` | | Pipeline fails on deploy | Run `databricks bundle validate` first to catch config errors | | Incremental refresh not picking up changes | Verify Lakehouse Sync is active and the bronze table is updating | #### References - [Medallion architecture](https://docs.databricks.com/aws/en/lakehouse/medallion) - [Lakeflow Declarative Pipelines](https://docs.databricks.com/aws/en/delta-live-tables/) - [Materialized views](https://docs.databricks.com/aws/en/ldp/materialized-views) - [Lakehouse Sync](https://docs.databricks.com/aws/en/oltp/projects/lakehouse-sync) - [DevHub: Pipelines and freshness](https://databricks.com/devhub/docs/lakehouse/pipelines)