Amazon Redshift will no longer support the creation of new Python UDFs starting November 1, 2025.
If you would like to use Python UDFs, create the UDFs prior to that date.
Existing Python UDFs will continue to function as normal. For more information, see the
blog post
SUPER data type and materialized views
With Amazon Redshift, you can use materialized views to enhance the performance and flexibility of queries run against the SUPER data type. The SUPER data type lets you store a superset of columns from the base tables in a materialized view, letting you query the materialized view directly without joining the base tables. The following sections show you how to create and use materialized views with the SUPER data type in Amazon Redshift.
Amazon Redshift supports materialized views that incorporate SUPER data type columns and PartiQL queries. Materialized views can incrementally refresh, whereas Amazon Redshift only updates data that has changed in the base tables since the last refresh operation. This selective update approach makes the refresh process more efficient than full recalculations. For more information about materialized views, see Materialized views in Amazon Redshift.
Accelerating PartiQL queries
You can use materialized views to accelerate PartiQL queries that navigate and/or unnest hierarchical data in SUPER columns. By creating one or more materialized views to shred the SUPER values into multiple columns and utilize the columnar organization of Amazon Redshift analytical queries, you can essentially extract and normalize nested data. The level of normalization depends on how much effort you put into turning the SUPER data into conventional columnar data.
The following topics showcase examples of breaking down, or shredding, complex data into smaller columns, as well as creating scalar columns out of shredded data to improve performance.