Multi-Engine Query Federation: Running Spark, Trino, and Flink on One Lakehouse

Multi-engine query federation enables engines such as Spark, Trino, and Flink to access and write the same lakehouse tables directly, allowing organizations to match batch processing, interactive analytics, and streaming workloads to the most suitable compute system without duplicating data. Open table formats such as Apache Iceberg make this possible by separating metadata from data files and using snapshot-based optimistic concurrency to protect tables when multiple writers operate simultaneously. However, shared storage alone does not prevent operational problems: without a common control plane, teams can create duplicate datasets, inconsistent security policies, fragmented lineage, competing schedules, and difficult-to-explain costs. Effective multi-engine lakehouse architectures therefore require a catalog reachable by every engine, coordinated maintenance such as compaction and snapshot expiration, compatible connector versions, engine-neutral governance and identity controls, and runtime lineage collection across all workloads. While federation can introduce integration and performance complexity, it can help organizations support diverse data needs while keeping data, policies, and operational visibility centralized.

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