Analytics Engineering with dbt: Build, Test, and Document Data Models

Modern data teams need clean, reliable, and well-documented datasets to make critical business decisions. This text-based course introduces you to dbt, the industry standard for transforming raw data in your warehouse into production-ready analytical tables. Through written explanations, structured SQL code examples, and practical exercises, you will transition from writing manual, disconnected queries to building scalable, automated data pipelines. You will master the fundamentals of analytics engineering, including modular data modeling, version control, and automated data quality testing. What you'll learn: - Understand foundational analytics engineering concepts and the dbt workflow - Configure a dbt project and connect it to a modern data warehouse - Build modular data models using SQL, ref functions, and Jinja templating - Apply automated testing to validate data quality and ensure pipeline reliability - Generate and publish interactive documentation for your data models - Configure version control with Git to manage and track pipeline changes safely You will start by mastering core terminology and database concepts before setting up your first dbt environment. From there, you will progress through building modular SQL models, writing data tests, and documenting your pipeline step-by-step. This course is designed for beginners, data analysts, and aspiring data engineers who have a basic understanding of SQL. No prior experience with dbt or data pipeline tools is required. Start reading today to build reliable, production-grade data pipelines with confidence.