It fits a pretty nice niche between small data (32GB in-memory, pandas level) and truly huge data where the disk files can't even fit on one machine (2TB+). Mos

It fits a pretty nice niche between small data (32GB in-memory, pandas level) and truly huge data where the disk files can't even fit on one machine (2TB+). Most data that people want to process on a daily cadence fit somewhere in between those ranges. As for whether to use a database or not, I think that's a more fundamental question about where your different read and write loads are coming from. Scaling a database to support billions of rows and have high query performance is not trivial; if your use-case fits more in a write-infrequently, read-often then OLAP is a pretty good choice of index structure.