Real-Time Lakehouse Patterns with Apache Flink and Iceberg

This article provides a detailed walkthrough for implementing real-time lakehouse patterns with Apache Flink and Iceberg. It explains why traditional Kafka-to-lakehouse pipelines fail due to schema drift, small file proliferation, and operational rigidity. The post covers setting up a pipeline from Kafka to Iceberg, configuring checkpointing for exactly-once delivery, enabling schema evolution without restarts, and managing small files from streaming writes. It also compares static vs. dynamic Iceberg sinks and discusses when Flink is the right choice. Aimed at data engineers and tech professionals, it offers practical configuration details and failure modes often undocumented.