Designing Scalable Semantic Models in Fabric

As data volumes grow, standard reporting models often slow down, frustrating business users and increasing costs. Learning how to architect high-performance semantic models in Fabric ensures your enterprise analytics remain fast, responsive, and reliable at any scale. This text-based course guides you from foundational data modeling concepts to advanced optimization strategies. You will understand how to structure your data, choose the right storage configurations, and implement efficient calculation patterns that handle heavy concurrent user traffic without compromising performance. What you'll learn: Understand foundational semantic modeling concepts and the core architecture of Fabric; Choose the optimal storage mode, including Import, DirectQuery, and modern Direct Lake configurations; Design robust star schema relationships to optimize query paths and reduce memory overhead; Write clean, scalable calculation patterns using DAX optimized for large datasets; Configure settings to handle high concurrency and large data volumes efficiently; Implement row-level security to protect enterprise data at scale. You will start with key terminology and foundational data architecture principles before exploring storage mechanics, relationship design, and performance tuning. Through clear written explanations and structured DAX examples, you will learn to construct models capable of supporting enterprise-level analytics. This course is designed for beginner to intermediate data analysts, BI developers, and database professionals looking to transition to modern cloud-scale data modeling. No advanced prerequisites are required, though a basic familiarity with database concepts is helpful. Start building faster, more reliable data models in Fabric today.