Amazon Redshift now supports incremental refresh on Materialized Views (MVs) for data lake tables

Amazon Redshift now supports incremental refresh of Materialized Views (MVs) on data lake tables. This capability helps customers improve query performance for their data lake queries in a cost effective and efficient manner. By enabling incremental refresh for MVs, customers can maintain up-to-date data in a more efficient and affordable way.

Customers leverage data lake tables to achieve cost effective storage and interoperability with other tools. Now with Open Table Formats (OTFs), such as Apache Iceberg, data is continuously being added and updated. Previously the changing data required a full re-compute of materialized views to maintain fresh data. Amazon Redshift now provides the ability to incrementally refresh your MVs on data lake tables by identifying changes in your base data lake tables and only reading the changed portion of the underlying data from Amazon S3 instead of scanning the entire data set, saving cost and time for eligible materialized views.

Support for incremental refresh for materialized views on data lake tables is now available in all commercial regions. To get started and learn more, visit the documentation.