Federated data: How does a data lakehouse help?

Data lakehouses and data federation are converging to address significant data architecture challenges by enhancing data access and governance beyond the capabilities of traditional data warehouses or lakes. In modern enterprise environments where data is dispersed across multiple cloud and on-premises systems, centralized data storage is impractical due to cost, regulatory issues, and slower data pipelines. Data federation enables access and querying across diverse data sources without moving data, while data lakehouses provide the necessary storage, metadata, and governance foundation. Technologies like Apache Iceberg enhance transactional support and governance, making federated architectures viable at scale. By separating storage and compute, lakehouses allow for independent scaling of resources, optimizing performance, cost, and governance needs. Federated architectures offer a unified governance layer, enabling consistent policy enforcement across data. Modern data lakehouses also improve federated query performance through features like metadata caching and adaptive query planning. For AI workloads, federated architectures facilitate access to diverse training data while maintaining governance, supporting the concept of Lakeside AI, which integrates analytics and AI through federated access. The Starburst Icehouse Architecture exemplifies the integration of data federation and lakehouse principles, enabling federated queries without sacrificing performance or governance.