dbt has become the standard tool for data transformation. SQL-based transformations run within warehouses leveraging their compute. Project organization, testing, and documentation practices determine long-term maintainability.
Project Structure
Organize models in layers—staging for source cleaning, intermediate for business logic, marts for final presentation. Use consistent naming conventions. Separate concerns enabling independent development.
- Stage sources cleaning and renaming columns consistently
- Build intermediate models containing reusable business logic
- Create mart models serving specific analytical needs
- Implement tests validating data quality at each layer
- Document models enabling self-service analytics
Testing Strategies
Schema tests validate column properties—uniqueness, not null, referential integrity. Data tests implement custom validation logic. Run tests in CI preventing broken models from deploying.