The GHz aspect of Moore's law died over a decade ago, and I suppose it's fair to say most other stuff has also slowed down, but if you've got a job that is embarrassingly parallel, which a lot of these "big data" jobs are, people badly underestimate how much progress there has been in the server space even so in the last 10 years if they're not paying attention. What was "big data" in 2011 can easily be "spin up a single 32-core instance with 1TB RAM for a couple of hours" in 2021. Even beyond the "big data" that my laptop comfortably handles.

I'm slowly wandering into a data science role lately, and I've been dealing with teams who are all kinds of concerned about whether or not we can handle the sheer, overwhelming volume of their (summary) data. "Well, let's see, how much data are we talking about?" "Oh, gosh, we could generate 200 or 300 megabytes a day." (Of uncompressed JSON.) Well, you know, if I have to bust out a second Raspberry Pi I'll be sure to charge it to your team.

The funny thing is that some of these teams have the experience that they ought to know better. They are legitimately running cloud services with dozens of large nodes continually running at high utilization and chewing through gigabytes of whatever per second. In their own worlds they would absolutely know that a couple hundred megabytes is nothing. They'll often have known places in their stack where they burn through a few hundred megabytes in internal API calls or something unnecessarily, and it will barely rise to the level of a P3 bug, quite legitimately so. But when they start thinking in terms of (someone else's) databases it's like they haven't updated their sense of size since 2005.

You are not thinking enterprisey enough. Everything is Big Scale if you add enough layers, because all these overheads add up, to which the solution is of course more layers.