#1 thing you should know, RDBMS can solve pretty much every data storage/retrieval problem you have. If you're choosing something other than an RDBMS - you should rethink why. Because unless you're at massive scale (which still doesn't justify it), choosing something else is rarely the right decision.
I'm with you on using an RDBMS for almost everything, but worked on quite a few projects where alternatives were needed.
One involved a lot of analytics queries (aggregations, filters, grouping etc.) on ~100-200GB of data. No matter what we tried, we couldn't get enough performance from Postgres (column-based DBs / Parquet alternatives gave us 100x speedups for many queries).
Another was for storing ~100M rows of data in a table with ~70 columns or so of largely text based data. Workload was predominantly random reads of subsets of 1M rows and ~20 columns at a time. Performance was also very poor in Postgres/MySQL. We ended up using a key/value store, heavily compressing everything before storing, and got a 30x speedup compared to using an RDBMS using a far smaller instance host size.
I wouldn't call either of them massive scale, more just data with very specific query needs.