This system provides a collection of sources that you can directly query with no copy required.
![athena vs redshift athena vs redshift](https://sidress.pl/img/3956ffa97fc527b1d58b771139a046c7.jpg)
When connecting to data sources other than S3, Athena has a connector ecosystem to work with.
![athena vs redshift athena vs redshift](https://blog.panoply.io/hubfs/Wait%20for%20the%20cluster%20to%20be%20ready%20(2)%20-%20Redshift.png)
If you are working with files with high-cardinality and trying to join them, you will likely have very poor performance. Keep in mind that when working with S3 objects, these are not traditional databases, which means there are no indexes to be scanned or used for joins. However if you are using Redshift, it would likely make more sense to use Spectrum in this case. Athena also has a Redshift connector to allow for similar joins. If you are working with Redshift, then Spectrum can join information in S3 with tables stored in Redshift directly. The functionality of each is very similar, namely using standard SQL to query the S3 object store.
![athena vs redshift athena vs redshift](https://img.stackshare.io/stackup/6327928/amazon-athena-vs-amazon-dynamodb.png)
![athena vs redshift athena vs redshift](https://blog.panoply.io/hubfs/Add%20a%20partition%20-%20Athena.png)
Redshift Spectrum, on the other hand, is an extension to Redshift that is a query engine. Very briefly, Redshift is the storage layer/data warehouse. While the thrust of this article is an AWS Redshift Spectrum vs Athena comparison, there can be some confusion with the difference between AWS Redshift Spectrum and AWS Redshift.