47 GitHub Issues That Might Explain Your Iceberg Latency
Most Kafka to Iceberg pipelines look straightforward in design documents, but become expensive and fragile once they reach production scale. Teams encounter silent connector failures, small file growth, metadata inconsistencies, and rising end to end latency, often alongside unexpected storage and data transfer costs driven by duplicated data movement. This article shows what actually breaks in Kafka Connect, Flink, and Hudi based on real production issues, then explains how teams reduce both operational risk and cost by simplifying data flows, removing unnecessary connector layers, and adopting storage native architectures that separate streaming from analytics while keeping Iceberg tables fully accessible across platforms.
