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Your approach is not uncommon, and I have seen many organization first leveraging Athena to meet their query response times. Having an S3 data lake future proofs your architecture and allows you the flexibility to switch compute in the future. Redshift can be leveraged once you encounter that Athena is unable to provide you the performance you need. With Redshift Serverless you can get more powerful compute for those queries that need better performance than what Athena can offer. You pay for use and if queries execute only 3 hours a day then that is your compute cost with Redshift Serverless as there are no charges for idle times. Also, Redshift Serverless is fully integrated with S3 data lake and you can query data in-place without needing to copy data as local Redshift tables. However, I have also seen customers will create aggregated, and pre-joined data sets as Redshift local tables to meet tighter query SLA's.
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