Amazon Redshift adds support for incremental refresh for materialized views on data lake tables (preview)
Amazon Redshift now supports incremental refresh for materialized views on Apache Iceberg and standard AWS Glue tables, eliminating the need for full-refreshes which require the re-execution of the underlying select statements and re-writing the data in the materialized view.
Amazon Redshift now supports incremental refresh for materialized views on Apache Iceberg and standard AWS Glue tables, eliminating the need for full-refreshes which require the re-execution of the underlying select statements and re-writing the data in the materialized view.
Materialized views in Redshift provides a way to speed up running queries on large tables, especially with aggregations and multi-table joins, by storing a precomputed result set of these queries, and it already supports incremental refresh capability for local tables to identify changes on the base tables and efficiently updating the data in the materialized view. Now data lake queries can also benefit from incremental refreshes, which can prevent unnecessary data scans on data lake during a refresh, and reduce the time and the costs it would take to refresh materialized views for eligible queries.
Amazon Redshift’s support for incremental materialized views on data lake tables is available as a preview using the PREVIEW_2023 track in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Europe (Ireland) and Europe (Stockholm).
To get started with this feature, visit the preview documentation and the Amazon Redshift Management Guide. For preview terms and conditions, see Beta Service Participation in AWS Service Terms.