Ingesting thousands of evolving Kafka topics into a lakehouse often creates complex, brittle pipelines that require constant manual intervention as its write patterns change. But what if your ingestion pipeline could adapt on its own, with zero downtime?
Enter the Flink Dynamic Iceberg Sink, a powerful pattern for Apache Flink that allows users to write streaming data into multiple Iceberg tables—dynamically, efficiently, and with full schema evolution support. The sink can create and write to new tables based on instructions within the records themselves.

Flink is an open-source stream processing framework for building real-time data applications, offering powerful features for data processing, analytics, and event-driven architectures. Developers can learn about stream processing concepts, event-driven architectures, and real-time analytics techniques to build scalable and fault-tolerant data pipelines for processing large volumes of data in real-time.

Flink

Apache Flink's Dynamic Iceberg Sink enables real-time ingestion of thousands of evolving Kafka topics into a lakehouse without manual intervention or job restarts. The pattern automatically handles schema evolution, creates new tables dynamically based on record metadata, and adapts to partitioning changes by leveraging schema registries and late binding. This eliminates operational overhead compared to traditional static pipelines that require restarts for every schema change or new topic, making it ideal for high-velocity data environments with frequently changing schemas.

From Stream to Lakehouse: Kafka Ingestion with the Flink Dynamic Iceberg Sink

The Building Block: A Simple Kafka-to-Iceberg Pipeline #

Project Details: Availability, Credits, and Development #