1) 10 years ago, having access to 300tb of data that could sustain 10gigabytes/s of throughput would require something like two racks of disks with some SSD cache and junk.
2) people thought hadoop was a good idea
3) People assumed that everything could be solved with map:reduce
3) machine learning was much less of a thing.
4) people realised that postgres does virtually everything that mongo claimed it could.
5) people realised that cassandra was a very expensive way to make a write only database.
I gave a talk about using big data, and basically at the time the best definition I could come up with was "anything that's too big to reasonably fit in one computer. so think 4, 60 disk direct attached SAS boxes".
Most of the time people were chasing the stuff for the CV, rather than actually stopping to think if it was a good idea. (think k8s two years ago, chatGPT now, chat bots in 2020). Most buisnesses just wanted metrics, and instead of building metrics into the app, they decided to boil the ocean by parsing unstructured logs.
Not surprisingly it turned to shit pretty quick. Nowadays people are much better at building metrics generation directly into apps, so its much easier to easily plot and correlate stuff.