dbt: Dependency Management During dbt Project Migration

✅ Concept: Dependency Management During dbt Project Migration

When migrating a dbt project across environments (e.g., from dev to test to prod, or between machines/teams), managing package dependencies is critical. You want to ensure that:

Everyone uses the same version of external packages
The behavior is predictable across environments
Issues are reproducible and easy to debug

The best practice is to combine:

packages.yml → for dbt packages
requirements.txt → for Python dependencies (like dbt-core, dbt-bigquery, etc.)

📦 Example: `packages.yml`

This file defines the dbt-specific packages your project depends on, with version pins:

# packages.yml
packages:
  - package: dbt-labs/dbt_utils
    version: [">=1.1.0", "<1.2.0"]

  - package: calogica/dbt_expectations
    version: [">=0.8.0", "<0.9.0"]

💡 You can pin to exact versions if you want maximum reproducibility:

version: "1.1.1"

Then run:

dbt deps

🐍 Example: `requirements.txt`

This file manages Python-level dependencies, including dbt itself and plugins for specific adapters:

# requirements.txt
dbt-core==1.7.5
dbt-bigquery==1.7.5
dbt-expectations==0.8.3

Then install with:

pip install -r requirements.txt

🚀 How it works in practice

Let’s say you’re migrating a dbt project to a CI/CD environment or a new teammate joins your team:

1. Clone the repo

git clone https://github.com/your-org/dbt-project.git
cd dbt-project

2. Set up the Python environment

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Install dbt packages

dbt deps

Now you’re guaranteed to be using:

The exact same Python environment
The exact same dbt packages
The exact same macros and behavior

✅ Benefits Recap

Feature	Benefit
🔒 Version pinning	Avoids unexpected breaking changes
💻 Cross-environment consistency	Ensures dev, test, and prod behave the same
🐞 Easier debugging	You know exactly which version caused the issue
🔁 Reproducibility	Anyone can recreate the environment 1:1
🔄 CI/CD friendly	Automates clean environment setup

🧠 Optional: Freeze Python versions

After installing everything and validating it works, you can freeze your full dependency tree for audit/debugging:

pip freeze > full_requirements.lock.txt