Databricks Data Tools

🚧 Public Beta — all features are free during the 30-day beta. AI features are bring-your-own-API-key. What happens after the beta?

See it in action

Compare your project against a live workspace — with a safety verdict on every change

Diff your .sql files against any live Unity Catalog workspace (or a built artifact) in any direction. Every proposed change is classified SAFE, DESTRUCTIVE, EXPENSIVE, or UNRECOVERABLE — before anything touches Databricks. DDT knows UC-specific failure modes generic SQL tools miss: dropping a managed table deletes its files; dropping a streaming table loses its checkpoint.

Deploys that refuse to destroy data silently

Destructive operations come out blocked, as comments — they only deploy after an explicit opt-in in your project file. Turn on safety cloning and DDT pre-creates a DEEP CLONE so any deploy is one command away from rollback.

Reverse-engineer an entire workspace in seconds

One command pulls every catalog, schema, and object into a project tree of .sql files — one file per object, ready for git.

Why DDT

Databricks ships building blocks — the SDK, the CLI, Terraform providers — but no end-to-end workflow for managing Unity Catalog schemas: no compare engine, no safety classification, no rollback story. DDT fills that gap. You author each UC object as a .sql file (a desired-state spec using CREATE OR REPLACE, the Databricks idiom), and DDT computes the deltas between your project and the live workspace — you never hand-write forward/backward migration files. Every proposed change is classified as SAFE, DESTRUCTIVE, EXPENSIVE, or UNRECOVERABLE, and anything destructive refuses to run without an explicit opt-in. DDT understands UC-specific failure modes — dropping a streaming table loses its checkpoint cursor; dropping a managed table deletes its files — that generic SQL tools miss.

It is the Databricks counterpart to SDT (Snowflake Data Tools): same design, same CLI surface, so a team that knows one learns the other in a day.

Key features

All of this is reachable from the DDT activity-bar view (Suites, Object Explorer, Deploy History) and the command palette. Press Ctrl+K F (Cmd+K F on macOS) to Find a Feature.

Note: Databricks users predominantly drive DDT from the CLI and CI. The project-lifecycle commands (init / build / publish / extract) are available through the @ddt-tools/cli; the VS Code surface focuses on browsing, comparing, diagramming, and reviewing.

Quick start

1. Install the extension and open a folder in VS Code.
2. Configure a connection — run DDT: Open connection setup docs for the walkthrough, then add a PAT or OAuth-M2M profile to ~/.ddt/profiles.json.
3. Validate the workspace — run ddt validate in a terminal to confirm Unity Catalog is reachable and your profile resolves.
4. Extract or author — pull a live workspace into .sql files with ddt extract, or author objects by hand.
5. Compare — diff your project against a target with ddt compare (or browse and diff from the Object Explorer). Read the safety classification on each change.
6. Deploy — build with ddt build and publish with ddt publish. Destructive changes refuse to apply until you opt in explicitly.

A guided Get started with DDT walkthrough is available from the VS Code Welcome page.

AI features (bring your own key)

AI is a layer on top of the deterministic engine — every AI feature composes with the compare/safety core, so the product is fully useful without it. You supply your own API key; DDT never ships one.

Supported providers: Anthropic, OpenAI, Azure OpenAI, any OpenAI-compatible endpoint, and self-hosted models. Enterprise deployments can additionally route through Databricks Foundation Models so prompts stay inside the workspace boundary.

What it does in the extension:

• Sketch Object from Description — prose → idiomatic CREATE DDL with a review-before-deploy header.
• Suggest Safer Alternative — right-click a destructive change and get a safer pattern (soft-delete, shallow-clone-then-swap, archive table).
• Ask DDT chat panel — converse with the engine as context (project model, target metadata, last compare). It proposes rename / change-type / drop / move operations you confirm via a modal before they touch your files.

During the beta all AI features are unlocked; after the beta they live in the Pro tier (the deterministic compare/safety/lint core stays free). Configure your provider and key from the DDT settings; calls are billed by your provider, not by DDT.

Pricing & beta

This is a 30-day public beta. During the beta, every feature is free.

After the beta:

• Free core stays free, forever — schema compare, safe deploy with the safety classifier, extract, lint, format, lineage, Object Explorer, and the interactive schema diagram.
• Pro features keep working with warnings — there is no hard paywall that locks you out mid-workflow. Pro adds AI write-side features, multi-project/Slice+Suite ownership, and the column-refactor UX.
• AI features are always bring-your-own-key — you pay your AI provider directly; DDT adds no inference markup.

Full tier details live on the public repo: https://github.com/GVOrganization/ddt-tools.

Documentation

📚 Full documentation — every feature, end to end:

Reporting bugs & feedback

Three channels:

1. In VS Code — run DDT: Report a Bug… from the command palette.
2. CLI — run ddt feedback if you have the @ddt-tools/cli installed.
3. GitHub Issues — https://github.com/GVOrganization/ddt-tools/issues.

Privacy

DDT includes opt-in automatic error reporting. When enabled, it sends only sanitized diagnostics — error fingerprints, stack traces with paths stripped, command name, and product version. It never transmits your SQL, identifiers, catalog / schema / table / column names, data, or credentials. Reporting is off until you consent on first run, and can be toggled at any time (ddt telemetry from the CLI). Full details: PRIVACY.md on the public repo.

Requirements & supported platforms

• VS Code 1.90.0 or newer.
• Node.js 20+ (only needed if you also use the ddt CLI; the extension bundles its runtime).
• Databricks — works against any workspace with Unity Catalog enabled (AWS, Azure, or GCP). Connection profiles support Personal Access Token (PAT) and OAuth machine-to-machine (M2M) auth. The hive_metastore legacy is not the focus — DDT manages the UC schema layer.
• OS — Windows, macOS, and Linux (pure TypeScript, no .NET dependency).

SDT + DDT

DDT has a sibling: SDT — Snowflake Data Tools — the same design, CLI surface, and safety model for Snowflake. A team that knows one learns the other in a day.

DDT is an independent tool and is not affiliated with or endorsed by Databricks Inc. "Databricks", "Unity Catalog", and "Delta Lake" are trademarks of Databricks Inc., used here for descriptive purposes only.

License: Apache-2.0.