Amazon Redshift adds support for creating and refreshing Apache Iceberg materialized views

Amazon Redshift now supports creating and updating Apache Iceberg materialized views. These views precompute complex joins and aggregations, storing results in Amazon S3 or S3 table buckets. The results are registered in the AWS Glue Data Catalog, enabling immediate querying with Amazon Athena, Apache Spark on Amazon EMR, AWS Glue, and other Iceberg-compatible engines. Redshift keeps results up-to-date with manual incremental updates, managing them as Iceberg tables in the Glue Data Catalog, detectable and manageable like other catalog tables. This reduces pipeline orchestration overhead for data cleaning and transformation across different engines, allowing all downstream consumers to share the same open results. The results are provided as open Iceberg tables, readable by any engine without copying or transformation. These tables can also be loaded into Redshift Managed Storage (RMS) for use as native RMS materialized views in performance-critical dashboards. Iceberg materialized views can be created in all regions supporting Redshift Serverless and provisioned Graviton instances.

Why it matters

This update benefits data teams aiming to streamline and ensure consistency in data analytics pipelines by reducing overhead from multiple engine conversions and orchestration, providing consistent results.