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dbt Setup

We use dbt to build our data warehouse. You can view every model on OSO here: https://models.opensource.observer.

This guide walks you through setting up dbt (Data Build Tool) for OSO development.

Prerequisites​

  • Python >=3.11
  • Python uv >= 0.6
  • git
  • A GitHub account
  • BigQuery access
  • gcloud CLI

Installing gcloud CLI​

For macOS users:

brew install --cask google-cloud-sdk

For other platforms, follow the official instructions.

Installation​

  1. Follow the installation instructions in our monorepo README.

  2. Activate the virtual environment:

source .venv/bin/activate
  1. Verify dbt is installed:
which dbt
  1. Authenticate with gcloud:
gcloud auth application-default login
  1. Run the setup wizard:
uv sync && uv run oso_lets_go
tip

The wizard will create a GCP project and BigQuery dataset if needed, copy a subset of OSO data for development, and configure your dbt profile.

Configuration​

dbt Profile Setup​

Create or edit ~/.dbt/profiles.yml:

opensource_observer:
  outputs:
    production:
      type: bigquery
      dataset: oso
      job_execution_time_seconds: 300
      job_retries: 1
      location: US
      method: oauth
      project: opensource-observer
      threads: 32
    playground:
      type: bigquery
      dataset: oso_playground
      job_execution_time_seconds: 300
      job_retries: 1
      location: US
      method: oauth
      project: opensource-observer
      threads: 32
  target: playground

VS Code Setup​

  1. Install the Power User for dbt core extension

  2. Get your virtual environment path:

echo 'import sys; print(sys.prefix)' | uv run -
  1. In VS Code:
    • Open command palette
    • Select "Python: select interpreter"
    • Choose "Enter interpreter path..."
    • Enter the virtual path

Running dbt​

Basic usage:

dbt run

Target specific model:

dbt run --select {model_name}
tip

By default, this writes to the opensource-observer.oso_playground dataset.

For more details on working with dbt models, see our Data Models Guide.