Much of it was writing code akin to recursive SQL (which was touted as one of the things it was way better than SQL at). Just never could understand it, and it took hours for most to get simple recursive models like connecting family trees. That language (like many) has a lot of work to do in terms of support and tutorials in order to build reasonable interest for it.

I think the problem here is is institutional--both our teaching institutions and normal industrial practices. Objective studies have shown repeatedly that functional programming models (which emphasize recursion, a stumbling block you mention) are easier for students with no prior programming knowledge to pick up and use effectively. It also tends to make better programmers, more quickly. There's a reason MIT taught Lisp/Scheme in its introductory computer science course for so many decades.

However (1) once you've ingrained all the counter-proactive habits of thinking and worked past the stumbling blocks that imperative and object-oriented programming models present, the conceptual jump from that to SQL is smaller; and (2) good, in-depth introductory materials don't materialize out of nowhere without demand. Your instructor was probably trying to do something good, both by introducing you to data processing in a language with fewer syntactical hurdles and in year-by-year incrementally improving the quality of introductory material by using it in instruction.

Datalog allows you to express data relationships in a more straight forward, more compact, and easier to refactor way free of most boilerplate. It does, however, require you think about what you want to accomplish abstractly, rather than as an imperative process, which is difficult merely because of the years of experience the typical student already has in unergonomic languages like C++, Java, etc.