E-Commerce Data Modeling with dbt: A Practical Case Study

Raw, unstructured data often holds back businesses from making timely, data-driven decisions. Learning how to transform this chaotic information into clean, reliable data models is the core superpower of modern analytics engineers. In this text-based course, you will step into the shoes of an analytics engineer to build a robust data pipeline for a growing e-commerce business. Through written explanations, structured SQL code snippets, and practical walk-throughs, you will master the foundational concepts of dbt (Data Build Tool) and learn how to structure data models that scale. What you'll learn: - Understand the core concepts of analytics engineering and how dbt fits into the modern data stack - Configure a dbt project from scratch and establish a clean directory structure for staging and mart layers - Build modular SQL models to transform raw e-commerce transaction and customer data into clean datasets - Apply data quality tests and schema validations to ensure your data models remain accurate and trustworthy - Write reusable and maintainable code using Jinja templating, loops, and variables - Document your data models to create a clear, searchable data catalog for business stakeholders The course starts with the absolute basics of data modeling and project configuration before guiding you step-by-step through staging raw data, building intermediate business dimensions, and automating your SQL workflows with Jinja. This course is designed for beginner data analysts, aspiring analytics engineers, and SQL users who want to transition into modern data modeling. No prior experience with dbt is required, though a basic familiarity with SQL queries is helpful. Start reading today to build your first production-grade data pipeline.