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Athena Vs Spectrum. It also integrates with AWS Glue so you can identify the schema of your data sources as well. One significant difference is that Spectrum requires Redshift which must be factored into your total cost. Athena and Redshift integrate data in. Redshift Spectrum runs in tandem with Amazon Redshift while Athena is a standalone query engine for querying data stored in Amazon S3.
Athena supports it for both JSON and Parquet file formats while Redshift Spectrum only accepts flat data. Spinning up Redshift clusters. If Athena or Spectrum are candidates for your workflows then you are likely structuring your data in a manner that could support either tool. Athena and Redshift integrate data in. Being a part of the Redshift family Redshift Spectrum natively supports connection to Redshift clusters. Athena is serverless so there is no infrastructure to manage and you pay only for the queries that you run.
2172020 Spectrum is a feature of Redshift whereas Athena is a standalone service.
3182017 Assuming you have objects on S3 that Athena can consume then you might start with Athena vs. Athena supports it for both JSON and Parquet file formats while Redshift Spectrum only accepts flat data. Being a part of the Redshift family Redshift Spectrum natively supports connection to Redshift clusters. In AWS there is a suite of tools that make analyzing and processing large amounts of data in the cloud faster including ways to optimize and integrate existing workflows with Amazon S3. The products sound similar by their description but one main difference is that Athena allows you to query data in S3 but thats all you can do while Redshift Spectrum allows you to query data in S3 but also join this data to data in your existing Redshift Cluster. Being a part of the Redshift family Redshift Spectrum natively supports connection to Redshift clusters.