Using materialized views, you can store the pre-computed results of queries and efficiently maintain them by incrementally processing the latest changes made to the source tables. Subsequent queries referencing the materialized views use the pre-computed results to run much faster. Materialized views can be created based on one or more source tables using filters, projections, inner joins, aggregations, grouping, functions and other SQL constructs.
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. Furthermore, materialized views make it easier to migrate to Redshift, and allow secure access to the pre-computed results.