I think it's one of those situations where for most vendor's customers, buying more hardware is far cheaper than hiring smart programmers. But for academic situations, there's a surplus of clever programmers with low wages, and not nearly enough money for hardware. So in "enterprise" the solution is to shove everything into SQL databases and just buy a ton more compute and disk to manage the extra inefficiencies, whereas academic situations have not had that luxury.

As data science progresses, the amount of enterprisey large data situations will also decrease, I think.