Don’t know by looking at it if it is the related to or the same as my link below but had a class where the professor liked Datalog for some reason. Entire class found it hard to learn and not really useful, and not much more work to do similar things in other languages. Very niche but I know HN loves niche-y languages.
Compared to SQL, Datalog lets you break down queries into smaller queries that are reusable.
I think Datalog got some attention in the 1980s as a logically pure dialect of Prolog, then it practically disappeared from the literature for a while. I remember it being really hard to find anything about it around 2008, then when people got really tired of OWL and other semantic web tools Datalog came roaring back in the 2010s.
Like the semantic web tools people have a hard time recognizing bog-standard database technology when they have been slightly reskinned and Datalog has long had that problem. One issue is that SQL has this crazy idiosyncratic syntax that hides the fact that it has a simple set of operators behind it, in the case of Datalog the operators are quite directly exposed which people seem to find too simple to understand.
In some ways, but it is more dynamic, that is, you can write recursive queries in a natural syntax like the way you write recursive functions in a normal programming language. You don't have awful hacks like
http://www.learndatalogtoday.org/
Maybe some useful scientific applications I am not aware of?