Posted On: Jun 19, 2020
Materialized views can significantly boost query performance for repeated and predictable analytical workloads such as dashboarding, queries from business intelligence (BI) tools, and ELT (Extract, Load, Transform) data processing. Data engineers can easily create and maintain efficient data processing pipelines with materialized views while seamlessly extending the performance benefits to data analysts and BI tools.
Now you can extend the benefits of materialized views to external data in your S3 data lake and federated data sources. Using materialized views, you can easily store and manage the pre-computed results of a SELECT statement referencing both external tables and Redshift tables. Subsequent queries referencing the materialized views run much faster as they use the pre-computed results stored in Amazon Redshift, instead of accessing the external tables. This also helps you reduce associated costs of repeatedly accessing the external data sources, because they are accessed only when you explicitly refresh the materialized views.