I don't want to pile on, but I do see startup after startup that is focused on improving the cost/perf payoff of pyspark. Pyspark seems to be basically implemented by having the databricks query planner embedded in it, then it generates multiple queries to evaluate it. I think there's nothing inherent in that process that should make it faster than raw sql queries. There's theoretical advantages of maybe looking at the overall giant pyspark query and decomposing it into efficient pieces. Compare that to someone who wrote a series of separate sql queries to accomplish the same thing, and the qp can't take advantage of any reused computation.

a single node of clickhouse would likely be sufficient for a fraction of the cost and time. use attached nvme disks (ebs on aws) or a physical local disk.

Polars would be worth looking into too. $50 and hours seems orders of magnitude too high vig 10tb