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Getting Started

Welcome to dbdeux. The modern, cloud-native IDE built for data teams who want to move fast with dbt.

What is dbdeux?​

dbdeux is a complete development platform for dbt projects. It replaces the fragmented workflow of local editors, terminal sessions, and manual deploys with a single, integrated experience:

What you getWhat it replaces
Cloud IDE with SQL/YAML/Jinja editing, per-file run buttons, and compiled SQL tabVS Code + dbt extensions + local Python environment
Worksheets for ad-hoc SQL with results grid, charts, and exportSeparate SQL clients (DataGrip, DBeaver, etc.)
Database Explorer with drag-and-drop schema browsingManual INFORMATION_SCHEMA queries
Visual DAG Explorer with column-level lineageManual dbt docs generate + static site
Slimmer CI with AI code review, metric gate, schema diffs, and state comparisonCustom CI pipelines + manual dbt Cloud CI setup
One-click runs against your warehouseTerminal + manual dbt run commands
Schema Diff showing exact changes between runsManual schema comparison scripts
Automated scheduling with dependency chainsExternal orchestrators (Airflow, Dagster, etc.)
Built-in testing with visual resultsTerminal test output + manual review
Team collaboration with Git workflows (GitHub, GitLab, Azure DevOps, Bitbucket)Separate Git client + code review tools
Atlas AI Co-pilot grounded in your schema and modelsSeparate AI tools with no context
A Semantic Layer with catalog browser, live SQL preview, and metric diffsSpreadsheets, LookML, or no shared metric layer
Organizations with AI budgets, member roles, and instant switchingManual team management
Multi-version dbt (Core 1.8, 1.10, Fusion) selectable per projectPinned to a single dbt version
Connection Tags with environment classification, production guardrail, and org-wide filteringManual naming conventions
Data Products with governance, SLA tiers, and reference validationSpreadsheets or tribal knowledge about data assets
Share Links for external catalog sharing without loginScreenshots and exported CSVs
Atlas Catalog with project overview, zoomable DAG graph, all eight node types, coverage scoring, and column lineagedbt docs serve + manual coverage audits
Shared Workspaces with private/team/org visibility and project copyAll projects visible to everyone
Audit Log with full action historyCustom logging solutions
Notifications via Slack, email, and PagerDuty, with auditable delivery historyCustom alerting scripts
Post-Run Actions triggering Power BI refresh and API endpoints after buildsCustom scripts and manual triggers
Integrations with a central credential store for Power BI, API Endpoints, and PagerDutySecrets duplicated across pipelines
Development environments with per-developer schema isolationManual schema naming conventions
Service Accounts & API with scoped tokens to trigger jobs programmaticallyCustom auth wrappers or shared credentials

Get Started in 5 Minutes​

Step 1: Sign In​

Go to dbdeux.datalakehouse.io and click Continue with DLH.io. dbdeux uses single sign-on across the Datalakehouse product family, so there is no separate dbdeux password to create or manage. If you are already signed in to another Datalakehouse product, you land straight in the editor without seeing a sign-in form at all.

On your first sign-in, dbdeux creates a private workspace called My Workspace for you, so you can start working immediately instead of setting up scaffolding first.

Step 2: Pick the Workspace You Will Work In​

Everything you create (projects, worksheets, and connections) lives inside a workspace, so the workspace selector in the sidebar decides where your work lands. My Workspace is already there and selected, so you can skip ahead if you are just getting started.

Create another workspace when you want a clean separation or want to bring other people in. Open the workspace selector and choose:

ChoiceUse it when
Private workspaceYou want a personal space only you can see. You can widen the sharing later without recreating anything
Shared workspaceYou want teammates in from the start. Share with the whole organization, or with specific people & roles

Name it, add an optional description, and it becomes the active workspace. → More on workspaces

Step 3: Connect Your Warehouse​

Add your data warehouse credentials so dbdeux can execute queries on your behalf:

  1. Open Databases in the left navigation
  2. Click New Connection
  3. Choose your warehouse (Snowflake, BigQuery, Redshift, Athena, Databricks, PostgreSQL, MotherDuck, Microsoft Fabric, Azure Synapse, Fabric Lakehouse, or StarRocks)
  4. Enter your credentials in the connection drawer
  5. Click Test Connection to verify, then create the connection

All credentials are encrypted and never stored in plain text. If a teammate has already set up a connection, ask them for a Connection Template token to pre-fill your config instantly. → Detailed guide

Step 4: Create Your Project​

Open the project selector in the top bar and choose New Project. The wizard has two steps: pick where the project comes from, then name it.

The first field is the Workspace the project will be created in. It is set to your active workspace, and you can change it here without leaving the wizard, so you can file a new project into a shared workspace directly. If you have no workspace at all, the wizard tells you so and points you at the workspace switcher rather than leaving you with a button you cannot press.

There are three ways to start:

SourceUse it when
From GitYou already have a dbt project in GitHub, GitLab, Azure DevOps, or Bitbucket. Paste the clone URL or pick from your repositories
ManagedYou want dbdeux to host and store the files for you, with no external Git provider. You can download the project as a zip at any time
BlankYou only need worksheets, connections, and environments to start with

For a Git project, tell dbdeux whether the repository is public or private. Public repositories clone without a credential; private ones use one of your saved Git credentials, which you add from the user menu under Connect Git Provider. Once you pick a credential you can browse your repositories directly instead of pasting a URL.

For a Managed project, dbdeux can initialize it with a starter dbt project: dbt_project.yml, a README, a .gitignore, two example models with schema tests, and the seeds, macros, tests, snapshots, and analyses folders. You choose the dbt version the generated files target, or leave it on Latest so the project always runs on the newest supported engine.

dbdeux reads your dbt_project.yml and packages.yml automatically. A live progress panel shows the clone and setup status while your project is prepared.

Step 5: Start Developing​

A setup checklist tracks the same three things in the same order: a workspace, a project, and an environment. Each item has a button that takes you straight to the right place, and the checklist minimizes to a small progress widget once you dismiss it. The workspace item is already complete thanks to My Workspace, and the environment step is optional, so you can start running SQL before you have promoted anything.

The checklist ticks items off as you finish them, without a page refresh. Create a workspace, add a project (including a dbdeux-hosted one), or set up an environment and the progress updates immediately, so it always reflects where you actually are.

The first time you sign in, a guided tour spotlights the key areas of the app in order: your organization, the editor, workspaces, projects, warehouse connections, environments, the catalog, scheduled jobs, and branches. You can leave the tour at any point, and replay it later from Preferences by turning on Show tour again.

From there, open any model in the editor, make a change, and click Run to see results, or start a scratch worksheet to run ad hoc SQL against your connection.

Explore the Platform​

Now that you are set up, explore what dbdeux can do:

Build​

  • Cloud IDE: Write and edit dbt models in a powerful browser-based editor
  • Worksheets: Run ad-hoc SQL queries and explore data interactively
  • Database Explorer: Browse your warehouse schema with drag-and-drop
  • DAG Explorer: Visualize your project's lineage interactively
  • Version Control: Branch, commit, and collaborate with Git
  • Git Provider Tokens: Required scopes and setup for GitHub, GitLab, Azure DevOps, Bitbucket
  • Atlas AI Co-pilot: AI assistant grounded in your schema and models
  • Semantic Layer: Shared metric catalog with live SQL and branch diffs
  • Data Products: Governed data product catalog with SLAs and ownership
  • Share Links: Share your metric catalog externally without requiring a login
  • Atlas Catalog: Server-indexed documentation browser with project overview, zoomable DAG graph, all eight node types, and coverage scoring
  • Shared Workspaces: Organize projects into private, team, or org-wide workspaces
  • Connecting Your Warehouse: Setup guide including shareable Connection Templates and Connection Tags

Test​

  • Slimmer CI: Automated PR validation with AI code review and schema diffs
  • Data Testing: Validate data quality at every step
  • Schema Diff: See schema changes between runs
  • Environments: Isolate dev, staging, and production with per-environment dbt variables, sandbox schemas, and extended attributes

Deploy​

  • Job Scheduling: Automate runs on any schedule with post-run actions (Power BI refresh, API endpoints)
  • Integrations: Central credential store for Power BI, API Endpoints, and PagerDuty
  • Notifications: Get alerted when things need attention via Slack, email, or PagerDuty
  • Multi-Warehouse: Connect to any major data platform

Govern​

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