> They used a c5.4xlarge that has peak 10Gbps bandwidth, which at a constant 100% saturation would take in the ballpark of 9 minutes to load those 650GB from S3, making those 9 minutes your best case scenario for pulling the data (without even considering writing it back!)

The query being tested wouldn't scan the full files and in reality the query in most sane engines would be processing much less than 650GB of data (exploiting S3 byte-range reads): i.e. just 1 column: a timestamp, which is also correlated with the partition keys. Nowadays what I would mostly be worried about the distribution of file size, due to API calls + skew; or if the query is totally different to the common query access patterns that skips the metadata/columnar nature of the underlying parquet (i.e. doing an effective "full scan" over all row groups and/or columns).

> The irony of workloads like this is that it might be cheaper to pay for a gigantic instance to run the query and finish it quicker, than to pay for a cheaper instance taking several times longer.

That's absolutely right.