AWS Glue Data Catalog now supports materialized views for Apache Iceberg tables. This lets data teams store pre-computed query results as managed Iceberg tables. These tables automatically update when the source data changes. High-volume pipelines that needed complex orchestration—like joins, aggregations, and scheduling—can now be handled with simple SQL for creating materialized views. These managed views work well with analytics engines such as Amazon Athena, Amazon EMR, and AWS-optimized Spark. They automatically rewrite queries to use the pre-computed results. This boosts performance and cuts compute costs.
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Organizations can set up automatic refreshes or manually trigger updates, either full or incremental, to keep data fresh. By removing manual transformation jobs and maintenance overhead, this enhancement simplifies data‑lake workflows, makes high-performance querying more accessible, and enables faster analytics and AI/ML workloads without deep operational burden. The new feature underscores AWS’s push to make data lakes more performant, efficient and developer‑friendly.