Since both services operate in a decoupled manner that separates between storage and compute, you can leverage inexpensive S3 to work with petabyte or exabyte-scale data without racking up massive cloud bills. Īdditional costs to take into account would be storage on S3, which is relatively much less costly than a database. The price is the same across both services – $5 per compressed terabyte scanned. Spectrum and Athena are both charged based on the amount of data scanned when running a query – although there is 10MB minimum per query and AWS rounds up to the next megabyte. so choosing one over the other will produce different results in many cases. However, there are differences in how they work under the hood. We’ve written quite a lot about Athena in the last few years, so you can check out our Amazon Athena resources here.īoth Spectrum and Athena are similar in that they both enable you to query data stored on S3. It is fully managed so there is no infrastructure to maintain – simply define and run your query. Spectrum enables you to query data stored on Amazon S3 using SQL, and to run the same queries on tabular data stored in your Redshift cluster and data stored in S3 – all using the Redshift SQL query editor.Īmazon Athena was introduced in 2016 as a standalone, serverless SQL query engine used to query data stored on Amazon S3. The ContendersĪmazon Redshift Spectrum was launched in April 2017 as a feature within Amazon Redshift. Let’s proceed to take a closer look at Athena and Spectrum, with the aim of understanding when you should choose each tool for a specific analytical workload. However, a closer look will reveal key differences between these two services – which could manifest in cost, performance and functionality. Ostensibly, both of these services are used to query data from Amazon S3 using SQL, without managing infrastructure. It’s easy to get confused when comparing Amazon Athena and Amazon Redshift Spectrum.
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The following article is part of our free Amazon Athena resource bundle.
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