Build a dynamic streaming data lake with Apache Iceberg and Apache Flink

This article demonstrates how to build a dynamic streaming data lake using Apache Iceberg and Apache Flink that handles schema changes and multiple event types without stopping the pipeline.

• Use DynamicIcebergSink to route records to multiple Iceberg tables based on per-record logic
• Automatically evolve table schemas when new columns appear in streaming data
• Infer schemas from JSON records or read from AWS Glue Schema Registry for precise types
• Partition routed tables dynamically based on candidate fields present in each table's schema
• Deploy with Managed Service for Apache Flink using CDK with support for AWS Glue Data Catalog or S3 Tables
• Handle multiple event types in a single Flink job without manual schema updates or restarts

The solution enables data lakes that adapt to upstream schema changes and new event types automatically, reducing operational overhead and keeping table definitions synchronized with evolving source data.