Data Transformation and Dimensional Modeling in Fabric

In modern data engineering, delivering clean, structured, and consistent data is critical for making accurate business decisions. This course guides you through the core concepts of data transformation and warehousing using Fabric, the unified analytics platform. You will transition from understanding raw data to designing production-ready dimensional models. By reading through clear explanations and practical text-based walkthroughs, you will master the techniques needed to ingest, clean, and structure data for reliable business intelligence. What you'll learn: - Understand the fundamentals of Fabric architecture, including the Lakehouse concept and Medallion architecture - Design effective dimensional models using star schemas, fact tables, and dimension tables - Apply data transformations using low-code Dataflows for quick and efficient ETL processes - Write Spark notebooks to perform scalable, code-first data cleaning and preparation - Utilize T-SQL queries to transform and shape data directly within the SQL analytics endpoint - Implement best practices for maintaining data consistency, performance, and integrity The course begins with foundational data warehousing terminology and Fabric architecture before moving step-by-step through practical transformation methods using Dataflows, Spark, and T-SQL. It is designed for beginner data analysts, aspiring data engineers, and database professionals looking to build a solid foundation in modern data analytics. No prior experience with Fabric is required. Start reading today to build clean, reliable data pipelines for your organization.