For years, dimensional modeling has been the foundation of enterprise analytics platforms. However, with the rise of Lakehouses, OneLake, Direct Lake, and AI-driven analytics, many organizations have started to assume that concepts such as Star Schemas, conformed dimensions, and clearly defined grain are no longer necessary.

The reality is very different.

Microsoft Fabric modernizes the data platform, but it does not eliminate the need for solid analytical architecture. In fact, the emergence of Copilot, semantic models, and data agents makes consistency, business context, and proper data modeling more important than ever.

In this session, we will explore which Ralph Kimball principles remain critical in modern Microsoft Fabric architectures, which practices are evolving, and how to combine Lakehouse, Warehouse, Semantic Models, and AI into a coherent and scalable analytical platform.

Through practical examples and real-world scenarios, we will discuss why:

Medallion Architecture does not replace dimensional modeling.
Direct Lake improves performance, but it does not fix poor data models.
Semantic Models are becoming the true contract between business, analytics, and AI.
Enterprise AI requires structured business context to generate reliable answers.

This session is aimed at architects, data engineers, BI professionals, and analysts who want to build modern analytics platforms without abandoning the principles that still make them successful today.