I think the problem is that most "big data" problems aren't really that big, or the latency requirements aren't that stringent. So people are OK with much slower solutions they can throw midlevel python devs & parallelize AWS compute at. With true time series problems you do get many "big data" problems that you can't actually parallelize (think sequential event based operations where the previous results are needed for deciding what to do with the next event).

With data frames you get some of the "close to the data" interactivity you get with a APL/J/K/Q stack. Of course, K is ~100x faster than Polars which is what.. ~10x faster than Pandas. Meanwhile most people are still using Pandas in a Jupyter notebook despite Polars being right there.

You'd think as people look at their AWS bills they'd be considering solutions that use 1/10th, 1/100th and even 1/1000th the compute time.. and yet.