dbt-bigquery-information-schema

dbt project to make it easier to work with BigQuery INFORMATION_SCHEMA views. Treats these views as data sources, and builds them into a Distributed Acyclic Graph (a.k.a. DAG) via dbt, that can be easily browsed using the dbt docs command, to see which views logically connect, and to give some examples of how to use them in your BigQuery project

Prerequisites

• uv - Python package manager

How To Use (assuming running locally)

1. In dbt_bigquery_info_schema/profiles.yml, Edit project: & dataset:
2. In dbt_bigquery_info_schema/dbt_project.yml, Edit vars to fit your use case
3. Create gitignored .env file: cp -n .env.template .env
4. Load environment variables: source .env
5. Install dependencies: uv sync
6. Test the setup: uv run dbt debug
7. Go do dbt stuff! (use uv run dbt <command>)

Contributing

For master list of GBQ stuff and PM approach, see Issue #1 - and otherwise, see Issues

For now, please fork this repo, make your changes there, and submit a PR. If you want less hassle and plan on working on this a bunch, contact @Brent Brewington in dbt Slack

Try to follow GitHub Flow - basically, small set of changes on fork/branch that shouldn't live too long before getting merged (Merge conflicts, while manageable, add chaos)

Note on the goofy clickbait commit messages from 2022...it was a different time in AI. Now in 2026 the hype has died down and we're back to regular ole' commit messages. Here's original inspiration: Tweet by @shaundai