Amazon Redshift now supports Iceberg materialized views, letting enterprises store precomputed query results in open Iceberg tables and reuse them across Redshift, Spark and Athena.
That interoperability cuts repeated execution of the same analytics workloads, lowering compute spending in multi-engine environments where teams and AI agents often recalculate identical business metrics.
Data teams can also avoid copying data and building separate ETL pipelines for dashboards, data science and other workloads, reducing integration complexity and metric-version confusion.
For CIOs, the feature adds architectural flexibility because performance optimizations are no longer tied to one engine, making it easier to mix, switch or add analytics platforms.