While both Spectrum and Athena are serverless, they differ in that Athena relies on pooled resources provided by AWS to return query results, whereas Spectrum resources are allocated according to your Redshift cluster size.
If you are already a Redshift customer, the use Spectrum can help you balance the need for adding capacity to the system. This can save you a $$$ since you can lifecycle data out of Redshift to an S3 data lake.
If you are not a Redshift user, then it becomes more interesting. Assuming you have objects on S3 that Athena can consume, then you might start with Athena vs spinning up Redshift. This might help you balance investments for storage and compute resources that might go underutilized in Redshift.
It might be the case that your analytic tool of choice does not support Athena, but it does support Redshift. For example, Tableau 10.3 officially released support for Athena. Looker also released support for Athena not long after its release. However, there are many tools that don’t support Athena.
If you are using Athena or Spectrum, then you are structuring your workflow and data in a manner that could support either tool. Why? Athena and Spectrum can both access the same object on S3. I can query a 1 TB Parquet file on S3 in Athena the same as Spectrum.
Originally Athena was JDBC centric. However, Amazon recently released a REST API for Athena. The might open some interesting new use cases for Athena.
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