Author: Mihir Borkar

Organizations managing large-scale analytical workloads increasingly face challenges with traditional Apache Parquet-based data lakes with Hive-style partitioning, including slow queries, complex file management, and limited consistency guarantees. Apache Iceberg addresses these pain points by providing ACID transactions, seamless schema evolution, and point-in-time data recovery capabilities that transform how enterprises handle their data infrastructure. In this post, we demonstrate how you can achieve migration at scale from existing Parquet tables to Apache Iceberg tables. Using Amazon DynamoDB as a central orchestration mechanism, we show how you can implement in-place migrations that are highly configurable, repeatable, and fault-tolerant.