Discover how Cosmos DB in Microsoft Fabric transforms the traditional separation between analytics and operations into a unified, actionable intelligence platform. In this demo-rich session, you'll learn how to build next-generation applications that don't just analyze data—they act on it in real-time. Through three hands-on demonstrations, we'll showcase Cosmos DB's full potential within the Fabric ecosystem: 1. AI-Powered Fraud Detection with Vector Search See how vector indexing in Cosmos DB enables real-time fraud detection by transforming transaction patterns into semantic embeddings. Learn to implement DiskANN-powered similarity searches that detect anomalies within milliseconds, automatically locking credit cards when suspicious activity is identified—all while processing live transaction streams through change feed. 2. Translytical Task Flows: Write-Back That Closes the Loop Experience the power of translytical workflows where insights drive immediate action. Using Fabric's User Data Functions with Cosmos DB, we'll demonstrate how business users can update operational data directly from Power BI reports—turning static dashboards into interactive command centers that respond to analytical insights in real-time. 3. Reverse ETL: Scaling Analytics to Production Discover how to serve analytics-derived insights to thousands of concurrent users by leveraging Reverse ETL patterns. We'll show you how to use CopyJobs to move enriched data from your Medallion Architecture's Gold layer back into Cosmos DB, creating a high-performance serving layer that makes analytics data immediately operational in your applications. Why This Matters: Traditional architectures force you to choose between analytical scale and operational speed. Cosmos DB in Fabric eliminates this trade-off, enabling you to build AI-powered features with millisecond latency at any scale, act on analytical insights without leaving your reporting interface, serve personalized experiences to millions of users, and create truly intelligent applications that learn and adapt in real-time. You'll leave with practical, production-ready patterns and working code to implement these capabilities in your own architectures.