Today, we’re announcing a simple, cost-effective path for data lake modernization. Amazon S3 Tables now support managed migration of your Apache Hive tables to Apache Iceberg. Migration works with Parquet tables in Amazon S3 that are registered in the AWS Glue Data Catalog and allows you to upgrade your data lake with no pipelines to build or compute to provision, while production workloads keep running. Apache Iceberg has become the open standard for large analytics datasets. It lets you manage petabyte-scale tables with features like schema evolution, hidden partitioning, and time travel queries, while keeping your data open and queryable with any Iceberg-compatible engine. Amazon S3 Tables offer fully managed storage purpose-built to keep Iceberg tables performant and cost-effective as they grow, with features like automatic maintenance, optimization, replication, and S3 Intelligent-Tiering built-in. What is new Previously, migrating Apache Hive tables to Apache Iceberg could take weeks of engineering work to build and manage the required infrastructure. Converting data types and building rollback capabilities added operational complexity. Now, this is available as a managed, API-driven operation. S3 Tables handle the entire migration workflow, including data validation, compatibility checks, type conversions, file recompression, and compaction. Before copying any data, S3 Tables identify every source object in scope and generate a compatibility report. You can sync as many times as you need without stopping writes to your source table, except for a few minutes during cutover. Once you are ready, you can complete the migration, minimizing time that tables are offline. Compared to self-managed approaches, S3 Tables managed migrations are cost-effective because you pay only for the data copied, with no charge for the Hive-to-Iceberg conversion, and source objects in S3 Intelligent-Tiering stay in their current access tier. A first look at the migration To migrate an AWS Glue Hive table, choose your destination table bucket in the S3 console, then choose Create table from migration. Create a namespace and table name for your destination Iceberg table. The destination table is created using recommended default settings tuned for query performance and storage efficiency. When you start the migration, S3 Tables validate the source table, generate a compatibility report, and begin copying data. The destination table is read-only during the migration. Once you complete migration, the destination table is promoted to read-write. You can begin working with your data right away using a direct integration with Amazon Athena and Amazon SageMaker Unified Studio in the AWS console, or use any tool compatible with the Apache Iceberg REST API to directly query the tables. To learn more, visit the S3 Tables migration documentation. You can also use AWS Command Line Interface (AWS CLI), AWS SDKs, or the Agent Toolkit for AWS. For example, set up migration jobs and call the start-table-migration command. cat > /tmp/start-migration.json