Semantic Modeling in Fabric for Enterprise Analytics

As organizations scale, managing complex data sources and delivering consistent business logic becomes a major challenge. Designing robust semantic models is the key to turning raw data into trusted, enterprise-wide insights. This text-based course guides you from foundational data modeling concepts to advanced deployment strategies in Fabric. You will learn how to structure relationships, optimize performance, and manage model lifecycles using modern analytics engineering workflows. What you will learn: Understand core semantic modeling concepts and terminology to establish a solid data foundation; Design scalable relationships and star schemas to support complex enterprise reporting; Implement calculations and business logic using DAX and model optimization techniques; Configure lifecycle management and version control using Git integration in Fabric; Practice optimizing model performance for large datasets and enterprise-scale queries; Explore modern analytics workflows, including connecting Python notebooks to semantic models. You will start with essential terminology and structural design before moving into performance tuning and collaborative lifecycle workflows. Through clear written explanations and structured design scenarios, you will gain practical skills to manage models at scale. This course is designed for beginner-to-intermediate data analysts, aspiring analytics engineers, and business intelligence professionals. No prior experience with Fabric is required, though a basic understanding of data concepts is helpful. Start building scalable, reliable semantic models for your enterprise today.