Every catalog and schema, on one canvas

Databricks Lakehouse Schema Design and SQL Workspace

DbSchema connects to a Databricks SQL Warehouse and draws Unity Catalog — catalogs, schemas, tables — as ER diagrams, then adds point-and-click queries, browsable Delta table data, shareable documentation, and DDL you read before it runs.

  • See the catalog-schema-table hierarchy as diagrams instead of a tree
  • Query Delta tables from the desktop — no notebook required
  • Give teammates HTML documentation instead of workspace credentials
  • Preview the DDL of every schema change before executing it
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Free Community Edition No signup required 15-day Architect trial included
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DbSchema rendering Databricks Unity Catalog schemas as an ER diagram
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The Lakehouse Keeps Growing
The Big Picture Keeps Shrinking

A maturing lakehouse spreads over dozens of catalogs and hundreds of schemas, and every team knows only its own corner. The workspace UI shows one object at a time, so cross-schema dependencies live in people's heads.

DbSchema reads Unity Catalog through your SQL Warehouse and lays the whole structure on a canvas — a shared picture for architects planning restructuring work and for analysts finding the right table.

Explore, Query & Document
Unity Catalog from Your Desktop

Schema on the canvas, data in the grid, documentation in the browser — all from one desktop app.

Reverse engineer Databricks Unity Catalog into an ER diagram
Map Three Levels of Namespace

Connect to a SQL Warehouse and DbSchema renders catalogs, schemas, and tables as a navigable ER model — structure you would otherwise piece together screen by screen.

Organize Databricks schemas into focused diagrams
Keep Hundreds of Schemas Readable

Split the model into focused diagrams per catalog or domain, color-code groups, and export the views for architecture discussions.

Build Spark SQL queries visually for Databricks
Point, Click, Spark SQL

The visual builder turns table and column picks into Spark SQL and runs it on the warehouse — analysts query without touching a notebook.

Browse Databricks Delta table data in DbSchema
Sample Rows Right After the Pipeline Runs

Browse Delta table contents, filter by column values, and page through results to audit data quality or verify an ingestion job.

Generate HTML documentation from Unity Catalog metadata
Publish the Lakehouse Reference

Generate HTML documentation from Unity Catalog metadata — diagrams, table definitions, and column descriptions in one shareable export.

Preview Databricks DDL before executing it
DDL You Approve Before It Runs

Design changes to Unity Catalog objects on the model, then read the generated statements first — nothing reaches the warehouse unread.

Design the lakehouse together without sharing its data.

Notebooks Hold Your Code
The Model Holds Your Schema

The schema design lives in its own model file — editable offline, tracked in Git, and applied only after you have read the DDL it produces.

Model the Databricks schema offline
Model While Disconnected

Refine the design anywhere; connect to the warehouse only when it is time to apply.

Track the Databricks model in Git
Diff Schemas Like Source

Track the model in Git so reviewers see structural changes as clean diffs.

Read generated Databricks DDL statements
Statements You Read First

Each planned change is spelled out as DDL before anything executes.

Roll out Databricks schema changes stepwise
Roll Out Step by Step

Move an approved change through each environment on your own schedule.

Lakehouse Schema History,
Kept in Your Repository

With the model in Git, the Unity Catalog structure rides along with your pipeline code: branch it, review it, merge it. Once the review passes, DbSchema produces the DDL for the change.

Databricks schema synchronization between the Git model and the SQL Warehouse
Read Git Workflow Docs

Schema Answers
With a Link, Not a Login

One export builds interactive HTML5 documentation: searchable diagrams, table definitions, and comments that open in any browser. Teammates who need schema context get it without direct Databricks access.

Open the Documentation Tool

Interactive HTML5 documentation of Databricks Unity Catalog schemas

An AI Assistant That Knows Your Tables

Type what you need in plain English and get the query back; paste an unfamiliar statement and get an explanation. The Assistant reads your design model, so it speaks in your catalogs and tables.

See What the AI Assistant Can Do

DbSchema AI assistant working with a Databricks design model

Connecting DbSchema to a Databricks SQL Warehouse

Download the Databricks JDBC driver from the Databricks driver download page and register it in DbSchema's driver manager. The URL takes the form jdbc:databricks://workspace.azuredatabricks.net:443/default;httpPath=/sql/1.0/warehouses/warehouse_id, where httpPath identifies your SQL Warehouse. Authenticate with a personal access token or your workspace's OAuth settings, and set the catalog and schema connection properties to open a specific namespace.

Put Unity Catalog on One Canvas

Download free, no account needed — Community Edition plus a 15-day Architect trial.

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Tutorials and guides

Frequently asked questions

Through the Databricks JDBC driver with your workspace URL, HTTP path, and personal access token or OAuth settings.

Yes. Connected catalogs and schemas appear in the model so teams can navigate tables and relationships.

Yes. The free Community edition includes reverse engineering, diagrams, and the SQL editor; documentation export and schema compare are Pro features with a 15-day trial.

DbSchema supports SQL-accessible lakehouse tables; use it for modeling, SQL, and documentation alongside your Databricks workflows.

Teams working with Databricks often use these engines too. Explore dedicated guides and JDBC setup for each.

Browse all 100+ supported databases