As analytics ecosystems grow more diverse, organisations increasingly need to query data across warehouses, data lakes, and operational systems without excessive movement or duplication. Query federation has become essential by enabling unified SQL access and intelligent pushdown into heterogeneous sources. This talk introduces the core principles of federation and why it matters for modern OLAP workloads and how it is different to Trino.

Using StarRocks as a model system, we highlight its vectorized execution engine, native connectors, and deep Apache Iceberg integration that together deliver high-performance lakehouse querying. We examine common lakehouse challenges—schema evolution, file fragmentation, and object-storage latency—and show how federation and hot/cold data separation help address them.

Finally, we explore federating additional sources such as Elasticsearch, PostgreSQL, and Apache Paimon to build a unified analytical architecture.