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Incremental refresh for Amazon Redshift materialized views on data lake tables
- Amazon Redshift allows precomputed query results in the form of materialized views for faster query response times from your data warehouse.
- Redshift supports incremental refresh capability for local tables, which is useful for aggregations and multi-table joins specifically.
- Customers use data lake tables for cost-effective storage and interoperability with other tools.
- Amazon Redshift now provides the ability to incrementally refresh installed materialized views on data lake tables.
- Incremental refreshes on standard data lake tables enable building and refreshing materialized views in Amazon Redshift maintaining data freshness with a cost-effective approach.
- Incremental refreshes are also possible for data lake tables using Apache Iceberg.
- Amazon Redshift's introduction of incremental refresh provides substantial performance gains over full recompute.
- Materialized views on data lake tables can be valuable for optimizing SQL queries for faster data analysis.
- For best practices on materialized views on data lake tables in Amazon Redshift Spectrum, check out the AWS documentation.
- Amazon Redshift makes it cost-effective to analyze structured and semi-structured data using standard SQL and business intelligence tools.
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