Structuring dbt Models: Folders, Tags, and Smart Selection

As data projects grow, managing hundreds of SQL models can quickly become chaotic and computationally expensive. Knowing how to logically group your models and execute only what you need is essential for maintaining a clean, high-performing data platform. This text-based course guides you through organizing your dbt projects for maximum efficiency, helping you transition from running flat, unorganized directories to executing highly targeted, modular data pipelines. What you'll learn: - Understand foundational dbt project structures, directory layouts, and model naming conventions - Configure and apply tags within schema files and model configurations to group related resources - Master node selection syntax to run, test, and build specific subsets of your data pipeline - Implement folder-level configurations to apply settings across multiple models simultaneously - Apply advanced selection methods, including intersection, exclusion, and state-based selection for modern CI/CD workflows - Practice writing clean, maintainable YAML configuration files that scale with your team The course begins with core terminology and the fundamental concepts of dbt project architecture and model hierarchies. From there, you will progress through step-by-step written explanations and practical configuration examples, learning how to apply tags, write selection commands, and optimize execution times. This course is designed for beginner-to-intermediate data analysts and analytics engineers who understand basic SQL and want to organize their dbt workflows. No advanced developer experience is required. Start reading today to build cleaner, faster, and more maintainable data pipelines.