[flyingsilverfin]

I think that you're correct in your assessment of relational vs graph-like structures: it's closer to our data domains we model and think of, more flexible, etc. We may be seeing something similar in the ML world where things are moving from tabular-dominant data to being able to process graphs more natively. A table is just a very structured graph after all!

SQL is the standard because, as others have pointed out, it's so entrenched and also builds upon a solid theoretical foundation. And given its dominance, it has been optimised and performed extremely well until recently, where data complexity is catching up again.

Recent noSQL databases won't take over SQL because of the lack of schema/typing. They do scale nicely, but aren't as constrainable as SQL, which is a feature (compare building a large software in Python vs Rust or Java) that enforces safety and good abstractions. There are some newer DBs which are combining strict schemas with NoSQL, which is promising!

Disclaimer: I work on TypeDB (vaticle.com/typedb) which is a native ERA (entity-relation-attribute) model with strict typing via the schema.