![]() ![]() These can be distributed across thousands of nodes to enhance the performance and can be scaled to query exabytes of data. Under the hood, Spectrum is breaking the user queries into filtered subsets that run concurrently. This means there are no new tools to learn and it allows you to leverage your existing skillsets to query Redshift. With Spectrum, you continue to use SQL to connect to and read AWS S3 object stores in addition to Redshift. This also reduces data latency since you aren’t waiting for ETL jobs to be written and processed. Spectrum allows you to access your data lake files from within your Redshift data warehouse, without having to go through an ingestion process. Athena uses pooled resources while Spectrum is based on your Redshift cluster size and is, therefore, a known quantity. When compared to a similar object-store SQL engine available from Amazon such as Athena, Redshift has significantly higher and more consistent performance. To discuss that however, it’s important to know what AWS Redshift is, namely an Amazon data warehouse product that is based on PostgreSQL version 8.0.2. Since there is a shared name with AWS Redshift, there is some confusion as to what AWS Redshift Spectrum is. Spectrum allows you to do federated queries from within the Redshift SQL query editor to data in S3, while also being able to combine it with data in Redshift. Launched in 2017, Redshift Spectrum is a feature within Redshift that enables you to query data stored in AWS S3 using SQL. Redshift and Redshift Spectrum Use Case.AWS Redshift Spectrum Performance & Price. ![]()
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