The problem Datalog tries to solve is complexity: SQL "pulls" data (what's a query after all) to a calling application. Datalog builds up data relationships through declarations. That means that: a) that entities can be inferred from these relationships as opposed to large complex queries, b) that some of these relationships can be built up by code/robots as opposed to humans declaring them.
The end result is (you hope) a very complex database where the smaller blocks/relationships can be audited and verified quickly, and where parallelization more or less comes for free.
The reality is that Datalog systems end up being massive hairballs of declarations that are hard to unravel for mere humans (well, regular developers) and that query-based solutions are 10x faster to develop for 80% of the application use cases.
The closest parallel is functional-vs-procedural programming (don't flame me); it's a niche solution for niche problems.
The end result is (you hope) a very complex database where the smaller blocks/relationships can be audited and verified quickly, and where parallelization more or less comes for free.
The reality is that Datalog systems end up being massive hairballs of declarations that are hard to unravel for mere humans (well, regular developers) and that query-based solutions are 10x faster to develop for 80% of the application use cases.
The closest parallel is functional-vs-procedural programming (don't flame me); it's a niche solution for niche problems.
Source: former Datalog developer for ERP systems.