The Real-Time Lakehouse with Streaming and Iceberg

This article provides a technical deep dive into the real-time lakehouse design pattern, emphasizing that it is a contract between streams, table commits, query paths, and freshness expectations. It distinguishes between the stream clock (when events arrive) and the table clock (when committed snapshots are visible for analytical queries). The discussion covers Apache Iceberg, Flink, and Kafka documentation to ground the architecture, and addresses commit frequency, snapshot visibility, small-file accumulation, and failure modes. It includes operational checklists for engineers, data owners, and executives, as well as guidance for realistic pilot rollouts and metrics. The article is a practical guide for data engineers building streaming lakehouse systems.