Apache Iceberg V3 brings powerful capabilities, but running it reliably in production takes more than a version upgrade. In this session, we share our real-world adoption journey with Iceberg 1.10: table design, write/read path tuning, compaction strategy, and metadata lifecycle management. We’ll unpack the key bottlenecks we faced—small-file growth, snapshot sprawl, merge/update pressure, and query latency variance—and the concrete optimizations that improved both stability and cost efficiency. You’ll leave with practical architecture patterns, anti-patterns, and a battle-tested checklist to move from pilot to production with confidence.

Speakers:

Yuming Wang: eBay, Compute, Lakehouse

Yuming Wang is a member of the Apache Spark Project Management Committee (PMC) and currently leads the build-out and evolution of his company’s compute platform. He focuses on architecting, optimizing, and operating Spark at production scale, while also driving the implementation and operation of a Lakehouse architecture based on Apache Iceberg. He is committed to turning cutting-edge open-source data technologies into reliable and efficient enterprise-grade production systems.