Data Modeling with dbt: Foundations and Practice

Are you looking to transition from writing messy, monolithic SQL scripts to building clean, reusable data pipelines? Understanding how to structure and validate models in dbt is the foundational skill every modern data analyst and analytics engineer needs. This text-based course guides you from dbt basics to building robust data transformations. You will learn how to design modular models, document your schema, and implement automated tests to ensure data quality at every stage. What you'll learn: - Understand the core architecture of dbt and how it manages SQL transformations; - Build modular data models using staging, intermediate, and mart layers; - Configure materializations like views, tables, and incremental models; - Implement schema tests and custom data tests to validate your pipeline's integrity; - Document your models and generate interactive project documentation; - Apply modern version control and dry-run practices to ensure reliable deployments. You will begin with fundamental concepts, learning key terminology and data warehouse setups, before moving into step-by-step written exercises and code examples. This course is designed for beginners with a basic knowledge of SQL, requiring no prior experience with data engineering tools. Take your first step toward building professional, reliable data pipelines today.