One of the most commonly used cloud-based data warehouse platforms is Amazon Redshift. It is the most prominent platform for data management in the cloud-based ecosystem. It is based on PostgreSQL and is intended mainly for analytical workloads. Redshift is highly scalable and requires significantly less effort relative to on-premises databases with similar characteristics. It can be deployed directly from the AWS console (after signing up for an AWS account). Nodes can be added or removed seamlessly via the AWS Console to increase and/or decrease capacities respectively.

A more recent release of Redshift known as Redshift Spectrum permits the querying of data that has been stored in Amazon S3, the standard storage layer in AWS. This means that users can directly query data stored on disk without having to load it into a Redshift-specific instance. Overall, Redshift is relatively fast, inexpensive, and more importantly easy to use and deploy. Redshift Spectrum uses a pay-per-query model—users pay only for the queries that are executed at a nominal charge for each terabyte of data scanned.

Amazon Athena is in many respects similar to Amazon Redshift Spectrum, in that it is also used to query data that is stored on S3. However, while the features of Amazon Redshift Spectrum cannot be used without first purchasing Amazon Redshift, users can leverage Amazon Athena on-demand and do not need to reserve any additional hardware. On the other hand, because Amazon Redshift Spectrum is closely integrated with the Redshift ecosystem, users can distribute their workload on either of the two solutions. Data that needs faster processing can remain on Amazon Redshift, whereas less frequently used/less critical data can be stored on S3 and queried using Redshift Spectrum: