Analytics Engineering and Data Visualization with dbt

Raw data is rarely ready for immediate analysis, often requiring extensive cleaning, structuring, and validation before it can deliver real business value. This course introduces you to the essentials of analytics engineering, bridging the gap between data engineering and business analysis. You will transition from writing complex, unorganized SQL queries to building structured, testable, and documented data pipelines. By reading through step-by-step explanations and studying clear code snippets, you will learn how to transform raw datasets into clean, reliable data models ready for modern business intelligence and visualization tools. You will understand how to apply software engineering best practices to your SQL workflows, ensuring your data pipelines are robust and scalable. What you'll learn: - Understand foundational analytics engineering concepts and the role of dbt in the modern data stack. - Configure dbt projects and organize your data transformation layers effectively. - Transform raw data into modular, reusable SQL models using best practices. - Apply automated data testing and documentation to ensure data quality and trust. - Implement version control and continuous integration concepts for analytics pipelines. - Integrate clean dbt models with modern visualization and business intelligence platforms. The course begins with key terminology and architectural fundamentals before guiding you through the practical mechanics of writing, testing, and documenting your data models. You will work through written exercises that simulate real-world data transformation challenges. This course is designed for beginners, aspiring analytics engineers, and data analysts who want to modernize their data workflows. No prior experience with dbt is required, though a basic familiarity with SQL concepts is helpful. Start reading today to build reliable, production-grade data pipelines that power business decisions.