As GPT-powered applications and copilot tools become integral to modern solutions, the creation of semantic layers over databases has become essential. This session explores how semantic layers can enhance GPT models' understanding of complex datasets by incorporating business context using Aggregator pattern, thereby improving the accuracy and relevance of generated responses.

We will demonstrate how to mirror databases into Microsoft Fabric's analytics zones, enabling seamless integration of diverse data sources for advanced analytics and insights. In microservice-based architectures, teams often leverage varied databases such as Azure SQL and Azure Cosmos DB. While the copilot application might primarily interact with Cosmos DB, we will showcase how Microsoft Fabric’s OneLake can unify these data silos by creating combined views across disparate sources, facilitating real-time analytics and robust BI reporting.

Attendees will gain practical insights into building real-time semantic layers on Azure Cosmos DB using the change feed, ensuring GPT models remain context-aware and updated. Additionally, we will illustrate methods to integrate supplementary data sources into Microsoft Fabric, creating enriched datasets tailored for copilot applications and business intelligence needs.