dbt-features 0.4.0

Feature catalog for dbt projects, built for ML teams.

dbt-features

Feature catalog for dbt projects, built for ML teams.

Live demo · Docs

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Install

pip install dbt-features

For warehouse enrichment (freshness, row counts, null %), install the extra for your warehouse:

pip install 'dbt-features[duckdb]'       # local / dbt-duckdb
pip install 'dbt-features[postgres]'     # Postgres
pip install 'dbt-features[redshift]'     # Redshift
pip install 'dbt-features[snowflake]'    # Snowflake
pip install 'dbt-features[bigquery]'     # BigQuery

Requires Python 3.10+. The base install does not depend on dbt-core.

Quickstart

Try it with no setup - bundled sample data, served on a free port:

dbt-features demo

On your dbt project:

dbt parse
dbt-features build --connection my_profile --output ./catalog
dbt-features serve --output ./catalog

For production (with type inference and correct schemas):

dbt docs generate                              # populates catalog.json with column types
dbt-features build \
    --manifest target/manifest.json \
    --catalog target/catalog.json \
    --connection my_profile --target prod \
    --output ./catalog

See Production setup for details.

What it does

• Reads your dbt manifest and finds models marked is_feature_table: true.
• Renders a static HTML site: feature groups, features, lineage, ML model consumers.
• With --connection, pulls freshness, row counts, null %, and cardinality from the warehouse.

Read-only. No backend, no database.

Docs

• Metadata schema - how to mark a model as a feature table
• Warehouse enrichment - freshness, row counts, profile examples
• dbt package - compile-time validation
• Deploying the catalog - GitHub Pages, S3, Netlify

Development

git clone https://github.com/gauthierpiarrette/dbt-features
cd dbt-features
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

License

Apache 2.0. Not affiliated with dbt Labs.