Datalog can be very effective for expressing certain kinds of problems and for generating efficient solutions to those problems. Particularly anything that is even mildly recursive, and therefore especially "knowledge graphs" that rely heavily on rules to infer, model and retrieve information. However if your problem domain amounts to CRUD storage without a need for complex recursion then mature SQL systems usually have all the advantages (asides from the syntax!). For a more formal answer:
> The intersection of databases, logic, and artificial intelligence gave raise to deductive databases. Deductive database systems are database management systems built around a logical model of data, and their query languages allow expressing logical queries. A deductive database system includes procedures for defining deductive rules which can infer information (in the so-called intensional database) in addition to the facts loaded in the (so-called extensional) database. The logic model for deductive databases is closely related to the relational model and, in particular, with the domain relational calculus. Datalog is the most known deductive query language (which syntactically is a Prolog subset) where constructed terms are not allowed as other non-declarative constructs such as the cut.
> Also following the relational model, relational database systems are well-known and widespread nowadays. Their formal query languages include relational algebra and relational calculi but, in practical systems, the de-facto and ANSI/ISO standard SQL is the language of choice of every relational database vendor. Whilst SQL and relational formal languages implement a limited form of logic, deductive database languages implement advanced forms of logic.
https://www.fdi.ucm.es/profesor/fernan/des/html/manual/manua...