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Setting up a well-structured dbt™ project is essential for scalability, maintainability, and efficiency. This section covers the core configurations needed to define sources, manage transformations, and ensure data quality in your dbt™ workflows. Whether you’re starting from scratch or optimizing an existing setup, these guides will help you configure your dbt™ project effectively.

Key Areas of Configuration

Setting Up Your dbt_project.yml

📄 Define project-wide configurations, including materializations, model directories, and environment settings.

Defining Your Sources

📊 Use sources.yml to document and reference external data sources in your transformations.

Testing Source Freshness

🔄 Ensure your raw data is up to date with automatic freshness checks in dbt™.

Working with Tags in Your dbt™ Project

🏷️ Organize and selectively run models using tags for better project structure and workflow control.

🧪 Unit Testing

Validate your SQL transformation logic with controlled input data before deploying models.
Prefer hands-on learning? Check out our Paradime 101 Guide for a step-by-step, interactive way to learn dbt™ and analytics engineering best practices—all for free.