trellis-datamodel 0.5.0b1

Visual data model editor for dbt projects

Trellis Data

A lightweight, local-first tool to bridge Conceptual Data Modeling, Logical Data Modeling and the Physical Implementation (currently with dbt-core).

Motivation

Current workflow pains:

• ERD diagrams live in separate tools (Lucidchart, draw.io) and quickly become stale or unreadable for large projects
• Data transformations are done isolated from the conceptual data model.
• No single view connecting business concepts to logical schema
• Stakeholders can't easily understand model structure without technical context
• Holistic Data Warehouse Automation Tools exists but do not integrate well with dbt and the Modern Data Stack

How Trellis helps:

• Visual data model that stays in sync — reads directly from manifest.json / catalog.json
• Sketch entities and with their fields and auto-generate schema.yml's for dbt
• Draw relationships on canvas → auto-generates dbt relationships tests
• Two views: Conceptual (entity names, descriptions) and Logical (columns, types, materializations) to jump between high-level architect and execution-view.
• Organize entities based on subdirectories and tags from your pyhsical implementation.
• Write description or tags back to your dbt-project

Two Ways of getting started

• Greenfield: draft entities and fields before writing SQL, then sync to dbt YAML
• Brownfield: document your existing data model by loading existing dbt models and utilize relationship tests to infer links

Tutorial & Guide

Check out our Full Tutorial with video clips showing the core features in action. Also General Information is available.

Vision

trellis is currently designed and tested specifically for dbt-core, but the vision is to be tool-agnostic. As the saying goes: "tools evolve, concepts don't" — data modeling concepts persist regardless of the transformation framework you use.

If this project gains traction, we might explore support for:

• dbt-fusion through adapter support
• Pydantic models as a simple output format
• Other frameworks like SQLMesh or Bruin through adapter patterns, where compatibility allows

This remains a vision for now — the current focus is on making Trellis work well with dbt-core.

Prerequisites

• Node.js 22+ (or 20.19+) & npm
  • Recommended: Use nvm to install a compatible version (e.g., nvm install 22).
  • Note: System packages (apt-get) may be too old for the frontend dependencies.
  • A .nvmrc file is included; run nvm use to switch to the correct version automatically.
• Python 3.11+ & uv
  • Install uv via curl -LsSf https://astral.sh/uv/install.sh | sh and ensure it's on your $PATH.
• Make (optional) for convenience targets defined in the Makefile.

Installation

Install from PyPI

pip install trellis-datamodel
# or with uv
uv pip install trellis-datamodel

Install from Source (Development)

# Clone the repository
git clone https://github.com/timhiebenthal/trellis-datamodel.git
cd trellis-datamodel

# Install in editable mode
pip install -e .
# or with uv
uv pip install -e .

Quick Start

1. Navigate to your dbt project directory

   cd /path/to/your/dbt-project
   

2. Initialize configuration

   trellis init
   

   This creates a trellis.yml file. Edit it to point to your dbt manifest and catalog locations.

3. Start the server

   trellis run
   

   The server will start on http://localhost:8089 and automatically open your browser.

Development Setup

For local development with hot reload:

Install Dependencies

Run these once per machine (or when dependencies change).

1. Backend

   uv sync
   

2. Frontend

   cd frontend
   npm install
   

Terminal 1 – Backend

make backend
# or
uv run trellis run

Backend serves the API at http://localhost:8089.

Terminal 2 – Frontend

make frontend
# or
cd frontend && npm run dev

Frontend runs at http://localhost:5173 (for development with hot reload).

Building for Distribution

To build the package with bundled frontend:

make build-package

This will:

1. Build the frontend (npm run build)
2. Copy static files to trellis_datamodel/static/
3. Build the Python wheel (uv build)

The wheel will be in dist/ and can be installed with pip install dist/trellis_datamodel-*.whl.

CLI Options

trellis run [OPTIONS]

Options:
  --port, -p INTEGER    Port to run the server on [default: 8089]
  --config, -c TEXT     Path to config file (trellis.yml or config.yml)
  --no-browser          Don't open browser automatically
  --help                Show help message

dbt Metadata

• Generate manifest.json and catalog.json by running dbt docs generate in your dbt project.
• The UI reads these artifacts to power the ERD modeller.
• Without these artifacts, the UI loads but shows no dbt models.

Configuration

Run trellis init to create a starter trellis.yml file in your project.

Options:

• framework: Transformation framework to use. Currently supported: dbt-core. Future: dbt-fusion, sqlmesh, bruin, pydantic. Defaults to dbt-core.
• dbt_project_path: Path to your dbt project directory (relative to config.yml or absolute). Required.
• dbt_manifest_path: Path to manifest.json (relative to dbt_project_path or absolute). Defaults to target/manifest.json.
• dbt_catalog_path: Path to catalog.json (relative to dbt_project_path or absolute). Defaults to target/catalog.json.
• data_model_file: Path where the data model YAML will be saved (relative to dbt_project_path or absolute). Defaults to data_model.yml.
• dbt_model_paths: List of path patterns to filter which dbt models are shown (e.g., ["3_core"]). If empty, all models are included.

Example trellis.yml:

framework: dbt-core
dbt_project_path: "./dbt_built"
dbt_manifest_path: "target/manifest.json"
dbt_catalog_path: "target/catalog.json"
data_model_file: "data_model.yml"
dbt_model_paths:
  - "3_core"

Testing

Frontend

Testing Libraries: The following testing libraries are defined in package.json under devDependencies and are automatically installed when you run npm install:

• Vitest (Unit testing)
• Playwright (End-to-End testing)
• Testing Library (DOM & Svelte testing utilities)
• jsdom (DOM environment)

Playwright system dependencies (Ubuntu/WSL2)

The browsers downloaded by Playwright need a handful of native libraries. Install them before running npm run test:e2e:

sudo apt-get update && sudo apt-get install -y \
  libxcursor1 libxdamage1 libgtk-3-0 libpangocairo-1.0-0 libpango-1.0-0 \
  libatk1.0-0 libcairo-gobject2 libcairo2 libgdk-pixbuf-2.0-0 libasound2 \
  libnspr4 libnss3 libgbm1 libgles2-mesa libgtk-4-1 libgraphene-1.0-0 \
  libxslt1.1 libwoff2dec0 libvpx7 libevent-2.1-7 libopus0 \
  libgstallocators-1.0-0 libgstapp-1.0-0 libgstpbutils-1.0-0 libgstaudio-1.0-0 \
  libgsttag-1.0-0 libgstvideo-1.0-0 libgstgl-1.0-0 libgstcodecparsers-1.0-0 \
  libgstfft-1.0-0 libflite1 libflite1-plugins libwebpdemux2 libavif13 \
  libharfbuzz-icu0 libwebpmux3 libenchant-2-2 libsecret-1-0 libhyphen0 \
  libwayland-server0 libmanette-0.2-0 libx264-163

Running Tests:

The test suite has multiple levels to catch different types of issues:

cd frontend

# Quick smoke test (catches 500 errors, runtime crashes, ESM issues)
# Fastest way to verify the app loads without errors
npm run test:smoke

# TypeScript/compilation check
npm run check

# Unit tests
npm run test:unit

# E2E tests (includes smoke test + full test suite)
# Note: Requires backend running with test data (see Test Data Isolation below)
npm run test:e2e

# Run all tests (check + smoke + unit + e2e)
npm run test

Test Levels:

1. npm run check - TypeScript compilation errors
2. npm run test:smoke - Runtime errors (500s, console errors, ESM issues) - catches app crashes
3. npm run test:unit - Unit tests with Vitest
4. npm run test:e2e - Full E2E tests with Playwright

Using Makefile:

# From project root
make test-smoke     # Quick smoke test
make test-check     # TypeScript check
make test-unit      # Unit tests
make test-e2e       # E2E tests (auto-starts backend with test data)
make test-all       # All tests

Test Data Isolation: E2E tests use a separate test data file (frontend/tests/test_data_model.yml) to avoid polluting your production data model. Playwright automatically starts the backend with the correct environment variable, so you don't need to manage it manually.

# Just run E2E tests - backend starts automatically with test data
make test-e2e
# OR:
# cd frontend && npm run test:e2e

The test data file is automatically cleaned before and after test runs via Playwright's globalSetup and globalTeardown. Your production data_model.yml remains untouched.

Backend

Testing Libraries: The following testing libraries are defined in pyproject.toml under [project.optional-dependencies] in the dev group:

• pytest (Testing framework)
• httpx (Async HTTP client for API testing)

Installation: Unlike npm, uv sync does not install optional dependencies by default. To include the testing libraries, run:

uv sync --extra dev

Running Tests:

uv run pytest

Collaboration

If you want to collaborate, reach out!

Contributing and CLA

• Contributions are welcome! Please read CONTRIBUTING.md for workflow, testing, and PR guidelines.
• All contributors must sign the CLA once per GitHub account. The CLA bot on pull requests will guide you; see CLA.md for details.

Acknowledgments

• Thanks to dbt-colibri for providing lineage extraction capabilities that enhance trellis's data model visualization features.

License

• Trellis Datamodel is licensed under the GNU Affero General Public License v3.0.
• See NOTICE for a summary of copyright and licensing information.