Federated data model enables real-time access to distributed data sources without physically moving or duplicating data. This approach eliminates silos, reduces operational overhead, and provides instant insights while maintaining compliance concerns. Key characteristics of a federated data model include virtualized access, federated queries, and no data duplication. It processes queries without moving data, allowing businesses to analyze information stored across multiple systems in real-time. Federated data models offer significant advantages, including real-time access, cost efficiency, seamless interoperability, and stronger security and compliance. However, they come with challenges such as performance bottlenecks, data consistency issues, and governance complexities. Organizations can address these obstacles by implementing the right strategies. Federated data models are being adopted across industries for applications like financial services, e-commerce, healthcare, and more. They enable real-time access to data without centralizing it, improving efficiency, reducing costs, and ensuring data compliance. As the field evolves, AI-powered query optimization, hybrid data federations, and Federation-as-a-Service solutions are emerging trends that enhance data accessibility, security, and integration across diverse platforms. To fully leverage federated architecture, businesses need intelligent, automated solutions that optimize query execution, ensure data reliability, and enhance security across multi-cloud environments, where companies like Acceldata provide end-to-end data observability, performance optimization, and governance.