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A complete data analysis pipeline, modeled with dbt, orchestrated with Prefect and automatically provisioned with Terraform on GCP

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Summers-Team/summer-gcp-data-platform

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Projet M2 BI

This is a project for M2 BI, featuring infrastructure as code using Terraform/OpenTofu for GCP resources and dbt for data transformation.

Infrastructure deployment

Detailed prerequisites and step-by-step deployment instructions now live in infrastructure/README.md.

Usage

  • dbt: Use the generated dbt/profiles.yml for development and production environments.
  • BigQuery: Access the dev and prod datasets as specified in the Terraform outputs.

Local environment with uv

  • Install uv to manage the Python environment described by pyproject.toml and uv.lock.

  • Run commands inside the managed environment with uv run, which keeps the virtual environment and lockfile in sync automatically. For example:

     uv run scripts/generate_profiles.py
     uv run -- dbt debug
  • If you prefer to activate the virtual environment manually, first synchronize dependencies and then source .venv:

     uv sync
     source .venv/bin/activate
     dbt debug

    See the uv running commands guide for more patterns.

    Lock requirements with uv pip compile pyproject.toml -o requirements.txt.

Notes

  • Service account keys are not committed to version control (ignored in .gitignore).
  • For production deployments, consider using CI/CD pipelines with secure credential management.

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A complete data analysis pipeline, modeled with dbt, orchestrated with Prefect and automatically provisioned with Terraform on GCP

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