In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully managed, petabyte-scale enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. This launch was a significant leap from the traditional on-premise data warehousing solutions which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift in AWS’s global infrastructure to process exabytes of data daily.

In the last few years, the use cases for Amazon Redshift have evolved and in response, Amazon Redshift has delivered and continues to deliver a series of innovations that delight customers. We take a peek under the hood of Amazon Redshift and give a detailed view of its architecture, explaining how Amazon Redshift maintains its industry-leading performance. We present storage and compute scalability discussing innovations such as Managed Storage, Concurrency Scaling, Data Sharing and AQUA. We then talk about autonomics that make Amazon Redshift easy to use. Finally, we discuss how Amazon Redshift extends beyond traditional data warehousing workloads, by integrating with the broad AWS ecosystem with features such as querying the data lake with Spectrum, semistructured data ingestion and querying with PartiQL, Redshift ML, Federated Query and cross-service materialized views with Glue Elastic Views.