trellis Data

trellis is a lightweight, local-first app that connects conceptual and logical data modeling with how you actually build the warehouse today—dbt-core first, with a live canvas that stays aligned to your project.

Why trellis

Typical pain

• ERDs live in separate tools and go stale on real projects.
• Transformations drift from the story the business understands.
• Stakeholders can’t see structure without wading through SQL and YAML.
• “All-in-one” warehouse designers rarely meet teams where they are (dbt, git, the modern stack).

What you get with trellis

• Small PyPI install, local web app — run it beside your dbt project; the canvas and model files (data_model.yml, dbt YAML) stay in version control, so names, relationships, and descriptions evolve like the rest of your code—not a one-off diagram export.
• One place to see entities, fields, relationships, and descriptions—tied to your repo, not a dead export.
• Conceptual view for names and meaning; Logical view for columns, types, and materialization detail—switch without losing context.
• Greenfield: sketch entities and attributes, then push structured artifacts into dbt.
• Brownfield: load what you already modeled in dbt, infer links from relationship tests, and push descriptions and tags back into the project.

What you can do

• Visualize your data model — canvas layout, conceptual vs logical views, less manual “diagram maintenance.”
• Work with your dbt project — point at manifest.json / catalog.json, keep the diagram honest, round-trip descriptions and tags, and generate relationship tests from drawn links.
• Optional — Kimball-style modeling — classify facts and dimensions, sensible default placement, and a Bus Matrix when your team thinks in stars/snowflakes; you can stay on plain entities if you prefer.
• Optional — business events & processes — capture events with 7W-style annotations and group them into processes; most useful for greenfield and dimensional workflows. Skip this entirely if it’s not your methodology.

Getting started

Install

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

Run next to your dbt project

1. cd /path/to/your/dbt-project
2. trellis init — creates trellis.yml (point it at your dbt paths and artifacts).
3. trellis run — opens http://localhost:8089 (use trellis run --help for port and config path).

Generate manifest.json and catalog.json with dbt docs generate in your dbt project so trellis can load models; without them, the UI may start but show no dbt-backed entities.

Install from source or hack on the app: see CONTRIBUTING.md.

Examples & walkthroughs

Short video walkthroughs:

More narrative walkthroughs and context: full tutorial · general information.

Configuration

After trellis init, edit trellis.yml. Annotated options and defaults live in trellis.yml.example (paths, modeling style, optional lineage/exposures, entity guidance, prefixes, etc.).

You can also open /config in the app to edit settings in the browser (validated saves; see example file for field meanings).

Landing page and Canvas defaults

Choose the page shown when opening the root URL, and optionally define shared Canvas filters:

start_page: canvas  # or entity-list
canvas:
  default_filters:
    domains: []
    tags: []

start_page affects only /; explicit routes such as /canvas and /entity-list remain authoritative. Canvas defaults are project-wide settings stored in version-controlled trellis.yml, not personal preferences. They are applied once to an otherwise unfiltered Canvas session. Users can clear or change them while working; those session changes are reversible and are not written back to trellis.yml.

From Entity List, Open filtered on Canvas opens the complete current filtered result set on Canvas. An explicit Canvas URL entity filter takes precedence over project-wide domain and tag defaults.

Vision

trellis is built and tested around dbt-core today. The longer-term idea is to stay tool-agnostic—concepts outlive any one framework. Possible directions include dbt Fusion, Pydantic-flavored exports, or adapters for tools like SQLMesh or Bruin where it makes sense. For now, the focus is a great experience with dbt-core.

Contributing

Contributions welcome. Workflow, local dev, tests, and packaging: CONTRIBUTING.md. All contributors sign the CLA once per GitHub account—see CLA.md and the bot on your PR.

Acknowledgments

• dbt-colibri for lineage-related capabilities that support trellis visualization.

License

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