AWS details Iceberg materialized views in Amazon Redshift for data lake interoperability

This blog post explains the functionality of Iceberg materialized views within Amazon Redshift, allowing users to pre-compute complex queries and store the results as open Apache Iceberg tables in Amazon S3. It details how these views foster cross-engine interoperability, enabling sharing of expensive analytical outputs with services like Amazon Athena, Apache Spark, and Amazon SageMaker AI without data duplication. The article highlights how this capability simplifies data transformation pipelines and provides a consistent source of truth across diverse analytics workloads. It also contrasts Iceberg MVs with native Redshift MVs, noting support on Serverless and Graviton-powered RG instances.

• →Add lakeformation.amazonaws.com to the IAM role trust policy (in addition to redshift.amazonaws.com and glue.amazonaws.com)
• →Add lakeformation:GetDataAccess to the role’s inline policy