redshift spectrum vs athena vs s3 select

This model leads to storage inefficiencies because the data must be stored twice, and can also mean that results from one form of processing may not align with those from another due to delays introduced by the loading process.

Others will issue queries to the data warehouse. Some applications will process the data in its original form, outside of the data warehouse. While these steps can be automated and scaled, the loading process introduces overhead and complexity, and also gets in the way of those all-important actionable results.ĭata formats present another interesting challenge. In order to provide great query performance, loading data into a data warehouse includes compression, normalization, and optimization steps.

One of the most challenging aspects of running a data warehouse involves loading data that is continuously changing and/or arriving at a rapid pace. Once the data is loaded, our customers can make use of a plethora of enterprise reporting and business intelligence tools provided by the Redshift Partners. Because Redshift is optimized for complex queries (often involving multiple joins) across large tables, it can handle large volumes of retail, inventory, and financial data without breaking a sweat.

Now that we can launch cloud-based compute and storage resources with a couple of clicks, the challenge is to use these resources to go from raw data to actionable results as quickly and efficiently as possible.Īmazon Redshift allows AWS customers to build petabyte-scale data warehouses that unify data from a variety of internal and external sources.

Redshift spectrum vs athena vs s3 select