As I assume, we more often have datasets with (A) very many cases (objects) and not so many variables (properties) (= large data) than the other way around: (B) just a few cases and many more variables (= high dimensionality).
There are other conceivable cases: (C) few cases, few variables, (D) very many cases, equally many variables.
I assume, that for these different cases, different statistical methods will be applied. Is this true, and how can these be characterized?
Further, I wonder, how often each of these cases does occur in practice (proportionally, estimated) - and from which disciplines they typically stem.