Configuring your dbt™ Project

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.