Amazon Redshift Adds Support for Apache Iceberg Materialized Views
Amazon Redshift now supports creating and refreshing materialized views stored as Apache Iceberg tables in Amazon S3 and registered in the AWS Glue Data Catalog. This enhancement allows Redshift to pre-compute expensive joins and aggregations once, improving data pipeline efficiency and reducing redundant processing. The resulting Iceberg tables are instantly queryable by any Iceberg-compatible engine, including Amazon Athena, Apache Spark, Trino, and Snowflake. This capability aims to streamline analytics workflows and ensure consistent data across different engines without requiring data copies or conversions.
Features (1) ›
- Redshift supports Apache Iceberg materialized views
Amazon Redshift now allows users to create and refresh materialized views that store their results as Apache Iceberg tables in Amazon S3, registered within the AWS Glue Data Catalog. These views pre-compute complex queries and can be incrementally refreshed, ensuring data consistency and efficiency across an ecosystem of Iceberg-compatible engines like Athena, Spark, Trino, and Snowflake.
https://aws.amazon.com/about-aws/whats-new/2026/10/redshift-iceberg-materialized-views
Related releases
- AWS details Iceberg materialized views in Amazon Redshift for data lake interoperability AWS Big Data Blog ·
- Amazon Bedrock AgentCore Gateway supports private TLS certificates for VPC endpoints AWS What's New ·
- Amazon Redshift now supports cross-Region S3 data lake queries AWS What's New ·
- AWS Glue now offers system-managed write protection for Apache Iceberg materialized views AWS What's New ·
- AWS launches system-managed write protection for Apache Iceberg materialized views AWS What's New ·
- Amazon DynamoDB adds filtered export to Amazon S3 AWS What's New ·