usually you use specific frameworks for that, not pure Python.

I suppose the Python side is a strawman then - who would do that for a small dataset that fits on a machine? Or have I been using shell for too long :-)

I thought the above comment was about datasets that do not fit on ones machine?

That's a hardware management question. The optimized binary used in my shell script still runs orders of magnitude faster and cheaper if you orchestrate 100 machines for it than any Hadoop, Spark, Beam, Snowflake, Redshift, Bigquery or what have you.

That's not to say I'd do everything in shell. Most stuff fits well into SQl, but when it comes to optimizing processing over TB or PB scale, you won't beat shell+massive hw orchestration.

I often find that downloading lots of data from s3 using `xargs aws sync`, and then xargs on some crunching pipeline, is much faster than a 100 core spark cluster