Building Dynamic Data Lakes with Apache Iceberg and Flink on AWS

The Dynamic Iceberg Sink enables Apache Flink to dynamically route records to multiple Iceberg tables based on user-defined logic. It creates and updates tables on the fly and evolves both table schemas and partition specifications during streaming execution. This eliminates the need for Flink job restarts when requirements change, allowing a single job to handle diverse event types.

The Dynamic Iceberg Sink automatically handles schema evolution without requiring pipeline restarts when new fields are added to streaming sources. It compares the record's schema against the target table and adds new fields as optional columns, committing changes with the next data file without needing a table rewrite. Schema information can be inferred from data or read from a schema registry, and changes are non-destructive by default.

The article addresses the challenge of handling upstream schema changes in streaming data pipelines writing to a data lake. Traditionally, this requires restarting pipelines or performing manual migrations, leading to ingestion pauses, data loss, or engineering overhead. The proposed solution avoids these issues by allowing pipelines to adapt dynamically.