Athena is serverless and built on a decoupled storage and compute architecture that queries data directly in S3, without the need to ingest/copy the data. It runs in multi-tenancy with shared resources. Users do not have control over the compute resources Athena chooses to allocate per query from the shared resource pool. For folks requiring additional or dedicated resources, they can reserve dedicated processing capacity in the form of Data Processing Units (DPU), with each DPU providing 4 vCPU and 16 GB RAM. RPU allocation ranges from 24 - 1000 per region.
Redshift has the oldest architecture, being the first Cloud DW in the group. Its architecture wasn’t designed to separate storage & compute. While it now has RA3 nodes which allow you to scale compute and only cache the data you need locally, all compute still operates together. You cannot separate and isolate different workloads over the same data, which puts it behind other decoupled storage/compute architectures. Redshift runs as an isolated tenant per customer, and unlike other cloud data warehouses, it is deployed in your VPC. Redshift offers a serverless option which is based on an abstracted unit called Redshift Processing Unit (RPU) ranging from 8 to 512 in increments of 8. Each RPU provides 2 vCPU and 16GB RAM. Thus, 8 RPU is equivalent to 16 vCPU / 128GB RAM. The minimum RPU is 8.
Athena is a shared multi-tenant resource, with no guarantees on the amount or availability of the resources allocated for your queries. From a data volume perspective, it can scale to large volumes, but large data volumes can suffer from very long run times and frequent time outs. Query concurrency is maxed at 20. If scalability is a top priority, Athena is probably not the best choice.
Redshift is limited in scale because even with RA3, it cannot distribute different workloads across clusters. While it can scale to up to 10 clusters automatically to support query concurrency, it can only handle a maximum of 50 queued queries across all clusters by default.
Athena, (and Presto) are designed to query data where it is, sacrificing storage-compute optimizations. This makes it very convenient for easy and immediate querying but at the expense of performance. This typically puts Athena behind cloud data warehouses in terms of performance. But Athena still does relatively well in performance benchmarks, especially when external storage is managed by experts. While it supports partitions, there is no support for indexing, and together with the fact that resources are pooled from a shared multi-tenant service, low-latency and consistent performance are not Athena’s sweet spot. A cloud data warehouse be more performant better than Athena in most cases.
Redshift does provide a result cache for accelerating repetitive query workloads and also has more tuning options than some others. But it does not deliver much faster compute performance than other cloud data warehouses in benchmarks. Sort keys can be used to optimize performance, but their contribution is limited. There is no support for indexes, and low-latency analytics at large data volumes is hard to achieve. Because Redshift decoupling of storage & compute is limited compared to other cloud data warehouses, it doesn’t support isolating workloads, which means performance can degrade under pressure and competition for resources
Athena is a great choice for Ad-Hoc analytics. You can keep the data where it is, and start querying without worrying about hardware or pretty much anything else, given that Athena is serverless and takes care of everything behind the scenes. However, it is not a great fit when you need consistent and fast query performance, and/or high concurrency. This is why it is typically not the best choice for operational and customer-facing applications. It can be also easily and flexibly used for batch processing, which is often leveraged for ML use cases.
Redshift was originally designed to support traditional internal BI reporting and dashboard use cases for analysts. As such, it is typically used as a general-purpose Enterprise data warehouse. With deep integrations into the AWS ecosystem, it can also leverage AWS ML service, making it also useful for ML projects. However, given the coupling of storage & compute, and the difficulty in delivering low-latency analytics at scale, it is less suited for operational use cases and customer-facing use cases like Data Apps. The coupling of storage and compute, together with the need to predefine sort & dist keys for optimal performance, make it challenging to use for Ad-Hoc analytics.