We’ll go deeper into the Spectrum architecture further down in this post. However, we do recommend using Spectrum from the start as an extension into your S3 data lake. (We’ll explain that part in a bit.) Image 1: Amazon Redshift Architecture We’re excluding Redshift Spectrum in this image as that layer is independent of your Amazon Redshift cluster. This architecture diagram shows how Amazon Redshift processes query across this architecture. We’ll include a few pointers on best practices.Īmazon Redshift Architecture and The Life of a Query It’s what drives the cost, throughput volume, and efficiency of using Amazon Redshift.Īnd so in this blog post, we’re taking a closer look at the Amazon Redshift architecture, its components, and how queries flow through those components. In the case of Amazon Redshift, much of that depends on understanding the underlying architecture and deployment model. Today, data sets have become so large and diverse that data teams have to innovate around how to collect, store, process, analyze and share data. The shift in expectations has implications for the work of the database administrator (“DBA”) or data engineer in charge of running an Amazon Redshift cluster. End-users expect to operate in a self-service model, to spin up new data sources and explore data with the tools of their choice. More choice : We see a constant flux of new data sources and new tools to work with data.End-users expect data platforms to handle that growth. The average intermix.io customer doubles their data volume each year. Growing data volume : On average, data volume grows 10x every 5 years.End users expect service level agreements (SLAs) for their data sets. Mission-critical : Redshift is now at the core of data lake architectures, feeding data into business-critical applications and data services the business depends on. ![]() And that has come with a major shift in end-user expectations: But with rapid adoption, the uses cases for Redshift have evolved beyond reporting. Today, we still, of course, see companies using BI dashboards like Tableau, Looker, and Periscope Data with Redshift. In the early days, business intelligence was the major use case for Redshift. Since its launch, Amazon Redshift has found rapid adoption among SMBs and the enterprise.
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