One of Iceberg's promises is engine independence, but making it work in production requires careful planning. This session shares battle-tested patterns for running multiple query engines against the same Iceberg tables.
Topics include: managing concurrent writes across engines, optimizing file layouts for different query patterns, handling schema evolution across Spark/Athena/Redshift, and monitoring cross-engine performance. Includes a live demo showing the same dataset queried by three engines simultaneously.