You can also join external Amazon S3 tables with tables that reside locally on Amazon Redshift. You can query vast amounts of data in your Amazon S3 data lake without having to go through a tedious and time-consuming extract, transfer, and load (ETL) process. Such platforms include Amazon Athena, Amazon EMR with Apache Spark, Amazon EMR with Apache Hive, Presto, and any other compute platform that can access Amazon S3. For example, it expands the data size accessible to Amazon Redshift and enables you to separate compute from storage to enhance processing for mixed-workload use cases.Īmazon Redshift Spectrum also increases the interoperability of your data, because you can access the same S3 object from multiple compute platforms beyond Amazon Redshift.
With Amazon Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond the data that is stored natively in Amazon Redshift.Īmazon Redshift Spectrum offers several capabilities that widen your possible implementation strategies.
November 2022: This post was reviewed and updated for accuracy.Īmazon Redshift Spectrum enables you to run Amazon Redshift SQL queries on data that is stored in Amazon Simple Storage Service (Amazon S3).
