The big difference I see at a skim is that, in classical datalog, facts are only allowed to contain domain attributes, and not value attributes. E.g., you can express binary facts like Raining(12:00) (it's raining at 12:00), but not Rain(12:00) = 5 in (At 12:00, 5 inches of rain had accumulated).
Value attributes make it much easier to express most forms of aggregation (sum, min, max), so you'll find very similar patterns in practical datalog variants e.g., RelationalAI's Rel [1], DBToaster's AGCA [2], etc...
Apart from that, and a syntax that seems to resemble map-style collection programming a bit more than datalog, yeah, this basically looks like datalog.
[1] https://docs.relational.ai/getting-started/rel/my-first-rel-...
[2] https://dbtoaster.github.io/
Value attributes make it much easier to express most forms of aggregation (sum, min, max), so you'll find very similar patterns in practical datalog variants e.g., RelationalAI's Rel [1], DBToaster's AGCA [2], etc...
Apart from that, and a syntax that seems to resemble map-style collection programming a bit more than datalog, yeah, this basically looks like datalog.
[1] https://docs.relational.ai/getting-started/rel/my-first-rel-... [2] https://dbtoaster.github.io/