Data Federation
Data federation is an architecture where a virtual compute layer queries data across many source systems in place, rather than copying everything into a single centralized warehouse or lake first.
Also known as: federated query, query federation, federated data access
Large enterprises commonly run dozens to hundreds of data sources — relational databases, object stores, SaaS applications — and the number keeps growing. Centralization tries to solve this by moving everything into one warehouse or lake via ETL, but the pipelines built to do that constantly break as source schemas change, and the effort to move data compounds faster than teams can keep up. Federation instead leaves data where it lives and puts a query engine on top that can join across sources on demand.
For AI agents, federation matters because an agent that can only see one data source will still confidently answer questions it doesn’t have the data to answer, firing query after query while missing context it was never given. A federation layer gives agents a single queryable surface over fragmented systems without requiring a slow, brittle centralization project first.
