I share your sentiment here. Having a common base to work from also has the benefit that end-users, the BI or Data Science practitioner (whom we empower as data engineers) can expect a stable SQL dialect that they'll be able to use everywhere*.

I haven't followed Spark in recent months, but what I recall was that the SQL DSL had some caveats at first, because certain things weren't yet implemented.

My anecdote has been that projects that implement SQL after a while, typically don't deliver the whole thing on initial release. I imagine it's often quite a lot of work.

The more projects that use Calcite, the more upstream contributions there would be ...