Your Enterprise Data Wasn’t Built for AI: Data Federation Can Help

Enterprise data architectures built around centralized warehouses, batch ETL, and static permissions may struggle to support AI agents that require real-time, cross-system information, clear business context, and tightly controlled access. Data federation is presented as an approach that queries distributed data in place rather than copying it into a central repository, allowing systems to combine sources such as operational databases, cloud storage, and warehouses while reducing replication and improving freshness. The approach also centralizes governance through server-side controls including read-only restrictions, row-level filtering, and column masking, which can help limit risks from unpredictable AI-generated queries. Effective implementation requires certified data products with semantic metadata and documented definitions to reduce ambiguity, alongside a hybrid architecture that uses lakehouse formats such as Apache Iceberg for high-volume analytics while federating live operational data when needed. Starburst positions its Trino-based platform as a tool for providing this federated querying, governance, and semantic access layer for enterprise AI workloads.

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