Foundations of dbt for Analytics Engineering

Modern data teams face the challenge of managing complex transformation logic that is often difficult to test and document. By adopting a code-based approach to data modeling, you can bring the rigor of software engineering to your analytics workflow. This course provides a clear path to understanding how to structure, build, and maintain a robust transformation layer within your data warehouse. You will transition from writing fragmented scripts to building a modular, version-controlled data project. Through written explanations and code examples, you will learn how to organize your logic, ensure data quality, and collaborate effectively with other data professionals. What you'll learn: - Understand the core concepts of the dbt workflow and its role in the modern data stack - Build modular data models using SQL to transform raw information into analytics-ready tables - Implement automated tests to validate data integrity and identify issues before they reach stakeholders - Apply version control principles and basic CI/CD patterns to data transformation - Create and maintain project documentation to improve transparency and team collaboration - Practice optimizing performance through materialization strategies and efficient coding patterns The course begins with essential terminology and structural definitions before moving into the practical application of modeling, testing, and documentation techniques. This foundational knowledge ensures you can contribute to data projects with confidence. This course is designed for beginners in data engineering, analytics, or data science who have a basic grasp of SQL and want to modernize their data transformation skills. No prior experience with command-line tools is required. Start building more reliable data pipelines by mastering the fundamentals of dbt today.