# 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** — **Lakebase Change Data Feed: Sync Lakebase to Unity Catalog (Autoscaling)** (https://databricks.com/devhub/templates/lakebase-change-data-feed-autoscaling). 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. Verify these Databricks workspace features are enabled before starting. Lakehouse Sync is a Beta feature and has stricter workspace requirements than most other templates. - **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 `. - **AWS workspace in an Autoscaling region.** Lakehouse Sync is currently available in **Beta on AWS only**. Azure is not yet supported. Confirm your workspace host is `*.cloud.databricks.com` (AWS) rather than `*.azuredatabricks.net` (Azure). - **A Lakebase Autoscaling project with tables.** Run `databricks postgres list-projects --profile ` and confirm your Autoscaling project appears. A `not enabled` error means Lakebase is unavailable to this identity. This template does not cover project creation — see [Create a Lakebase Instance](https://databricks.com/devhub/templates/lakebase-create-instance) if you need one. - **Unity Catalog access.** Run `databricks catalogs list --profile ` and confirm the destination catalog and schema you want to replicate into are present. You will select them when enabling Lakehouse Sync in Step 3. ## Replicate Lakebase Tables to Unity Catalog with Lakehouse Sync Replicate your Lakebase Autoscaling Postgres tables into Unity Catalog as managed Delta tables using Lakehouse Sync. CDC captures every row-level change and writes them as SCD Type 2 history, giving you a full audit trail queryable from the lakehouse. > This template is for **Lakebase Autoscaling** (projects/branches/endpoints with scale-to-zero). ### When to use this - You want to analyze operational data (orders, user activity, support tickets) in the lakehouse - You need a historical record of every insert, update, and delete from your Postgres tables - You want to join operational data with analytics data in Spark, SQL, or BI tools - You need to feed Lakebase data into downstream pipelines or ML models ### How it works > **Note:** Lakehouse Sync is currently in **Beta on AWS only** (all Autoscaling regions). Azure support is not yet available. It is a native Lakebase feature with no external compute, pipelines, or jobs required, and there is no incremental charge for replication beyond the underlying Lakebase compute and storage costs. Lakehouse Sync uses Change Data Capture (CDC) to stream changes from Lakebase Postgres into Unity Catalog. For each synced table, a Delta history table is created: ``` lb__history ``` Each row includes metadata columns: - `_change_type`: `insert`, `update_preimage`, `update_postimage`, or `delete` - `_lsn`: Log Sequence Number for ordering changes - `_commit_timestamp`: When the change was captured ### 1. Verify table replica identity Lakehouse Sync requires the right replica identity for capturing changes. Connect to your Lakebase database and check: ```sql SELECT n.nspname AS table_schema, c.relname AS table_name, CASE c.relreplident WHEN 'd' THEN 'default' WHEN 'n' THEN 'nothing' WHEN 'f' THEN 'full' WHEN 'i' THEN 'index' END AS replica_identity FROM pg_class c JOIN pg_namespace n ON n.oid = c.relnamespace WHERE c.relkind = 'r' AND n.nspname = 'public' ORDER BY n.nspname, c.relname; ``` If a table shows `default` or `nothing`, set it to `FULL`: ```sql ALTER TABLE REPLICA IDENTITY FULL; ``` ### 2. Check for unsupported data types ```sql SELECT c.table_schema, c.table_name, c.column_name, c.udt_name AS data_type FROM information_schema.columns c JOIN pg_catalog.pg_type t ON t.typname = c.udt_name WHERE c.table_schema = 'public' AND c.table_name IN ( SELECT tablename FROM pg_tables WHERE schemaname = c.table_schema ) AND NOT ( c.udt_name IN ( 'bool', 'int2', 'int4', 'int8', 'text', 'varchar', 'bpchar', 'jsonb', 'numeric', 'date', 'timestamp', 'timestamptz', 'real', 'float4', 'float8' ) OR t.typcategory = 'E' ) ORDER BY c.table_schema, c.table_name, c.ordinal_position; ``` If unsupported types appear, restructure those columns before enabling sync. ### 3. Enable Lakehouse Sync > **Note:** This step is not yet available via CLI or REST API and must be completed through the Databricks UI: > > In **Catalog**, open your Autoscaling project → branch → **Lakehouse Sync** → **Start Sync**, then select the source database/schema, destination catalog/schema, and tables. ### 4. Monitor sync status Check active syncs from Postgres (the `wal2delta` schema only exists after Lakehouse Sync has been enabled in Step 3): ```sql SELECT * FROM wal2delta.tables; ``` ### 5. Query the history tables #### Latest state of each row ```sql SELECT * FROM ( SELECT *, ROW_NUMBER() OVER (PARTITION BY id ORDER BY _lsn DESC) AS rn FROM ..lb__history WHERE _change_type IN ('insert', 'update_postimage', 'delete') ) WHERE rn = 1 AND _change_type != 'delete'; ``` #### Full change history for a record ```sql SELECT * FROM ..lb__history WHERE id = 12345 ORDER BY _lsn; ``` ### 6. Handle schema changes If you need to change a synced table's schema in Postgres, use the rename-and-swap pattern: ```sql CREATE TABLE users_v2 ( id INT PRIMARY KEY, name TEXT, new_column TEXT ); ALTER TABLE users_v2 REPLICA IDENTITY FULL; INSERT INTO users_v2 SELECT *, NULL FROM users; BEGIN; ALTER TABLE users RENAME TO users_backup; ALTER TABLE users_v2 RENAME TO users; COMMIT; ``` ### What you end up with - **Delta history tables** in Unity Catalog (`lb__history`) with full SCD Type 2 change tracking - **Continuous replication.** Changes stream from Postgres to Delta automatically. - **No external compute.** Lakehouse Sync is a native Lakebase feature. - Operational data queryable in Spark SQL, notebooks, BI tools, and downstream pipelines ### Troubleshooting | Issue | Fix | | -------------------------------- | ------------------------------------------------------------------ | | Table not appearing in sync | Ensure it has a primary key or `REPLICA IDENTITY FULL` | | Unsupported data type error | Check column types with the query in Step 2 | | Sync lag increasing | Check Lakebase endpoint health and compute scaling | | Missing changes on update/delete | Verify `REPLICA IDENTITY FULL`. `default` only captures PK columns | ### Limitations - **AWS only.** Lakehouse Sync Beta is available in all Autoscaling regions on AWS. Azure support is not yet available. - **No incremental charge.** Replication cost is included in your Lakebase compute and storage. - **Works alongside synced tables.** You can use Lakehouse Sync in a project/schema that also has synced tables. #### References - [Lakehouse Sync (Autoscaling)](https://docs.databricks.com/aws/en/oltp/projects/lakehouse-sync) - [Register Lakebase in Unity Catalog](https://docs.databricks.com/aws/en/oltp/projects/register-uc) - [SCD Type 2 in Databricks](https://docs.databricks.com/aws/en/ingestion/lakeflow-connect/scd)