In this advanced live workshop of Semantic Modelling in Power BI, you'll master building enterprise-scale, performant semantic layers that support self-service analytics, AI features like Copilot, and governance across Power BI and Microsoft Fabric.

Core Concepts: Understand what defines a mature semantic model—business-friendly, scalable, reusable, secure, and AI-ready—going beyond basic relationships and measures.

Data Modeling Patterns: Implement star schemas with surrogate keys, role-playing dimensions, fact table designs (transaction, snapshot, accumulating), and proper many-to-many handling via bridge tables.

Relationship Management: Optimize single vs. bi-directional filtering, inactive relationships for role-playing, and avoid performance pitfalls like overuse of bi-directional filters.

Advanced DAX Techniques: Create calculation groups for time intelligence, currency, and scenarios (reducing measures by 60-90%); design base/derived measures; use context controls like TREATAS, USERELATIONSHIP, and CROSSFILTER.

Performance Optimization: Apply VertiPaq tweaks (reduce cardinality, integers over text), aggregations, incremental refresh, hybrid tables, and composite/Direct Lake models for large datasets (>100M rows).

Security Features: Build static/dynamic row-level security (RLS) with USERPRINCIPALNAME() and bridge tables; apply object-level security (OLS) for sensitive data.

Governance and Reusability: Use shared/certified datasets, naming standards, display folders, deployment pipelines, ALM with Git/XMLA, and tools like Tabular Editor, DAX Studio, and VertiPaq Analyzer.

AI and Fabric Readiness: Prepare models with descriptions, synonyms, hidden technical columns, and custom calendars for Copilot, Q&A, and Fabric integration.

Best Practices and Pitfalls: Avoid common mistakes like fact-as-dimension, excessive calculated columns, or logic in visuals; follow a structured learning path from fundamentals to real-world case studies.