AWS has announced a new materialized view capability for Apache Iceberg tables in the AWS Glue Data Catalog that will make it easier to simplify data pipelines and increase data lake query performance by solving painful, long-standing challenges that data engineers face transforming raw data into actionable insights. Traditionally, setting up and maintaining multistage data transformations, such as joining clickstream logs with orders data in e-commerce use cases, required complex change detection logic, custom-coded joins and aggregations, and extensive orchestration, which translates into high engineering effort with slow turnarounds. The new feature creates managed tables that store in their body the precomputed results of queries in Iceberg format and automatically refresh them as the underlying datasets change, eliminating the need for a custom-built pipeline while intelligently accelerating performance and reducing compute costs.
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Apache Spark engines running across Amazon Athena, Amazon EMR, and AWS Glue now support these materialized views and can transparently rewrite queries to leverage the precomputed results. Data transformations make use of familiar SQL syntax, operate on managed infrastructure, and handle change detection, incremental updates, and refresh scheduling automatically. Materialized views store results as Iceberg tables on Amazon S3, making results accessible to multiple query engines, including Athena and Amazon Redshift. Users can either define automatic refresh intervals or manually trigger full or incremental refreshes. This tight integration simplifies data engineering workflows, reduces operational overhead, and enables teams to spend more time delivering analytics, not babysitting ETL pipelines, setting a significant advancement in AWS’s data lake ecosystem.