Amazon Redshift now supports writing to Apache Iceberg tables

Amazon Redshift today announces the general availability of write capability to Apache Iceberg tables, enabling users to run analytics read and write queries for append-only workloads on Apache Iceberg tables within Amazon Redshift. Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service used by tens of thousands of customers. Whether your data is stored in operational data stores, data lakes, streaming engines or within your data warehouse, Amazon Redshift helps you quickly ingest, securely share data, and achieve the best performance at the best price. The Apache Iceberg open table format has been used by many customers to simplify data processing on rapidly expanding and evolving tables stored in data lakes.

Customers have been using Amazon Redshift to run queries on data lake tables in various file and table formats, achieving a wide range of scalability across data warehouse and data lake workloads. Data lake use cases continue to evolve and become increasingly sophisticated, and require capabilities like transactional consistency for record-level updates and deletes while having seamless schema and partition evolution support. With this milestone Amazon Redshift now supports SQL DDL (data definition language) operations to CREATE an Apache Iceberg table, SHOW the table definition SQL, DROP the table and perform DML (data manipulation language) operations such as INSERT. You can continue to use Amazon Redshift to read from your Apache Iceberg tables in AWS Glue Data Catalog and perform write operations on those Apache Iceberg tables while other users or applications can safely run DML operations on your tables.

Apache Iceberg support in Amazon Redshift is available in all AWS regions where Amazon Redshift is available. To get started, visit the documentation page for Amazon Redshift Management Guide.