Debatable. Java's dominance in the data engineering space rests a bit on clay feet: it's unsuitable for scientific computing in a number of ways: for example yo

Debatable. Java's dominance in the data engineering space rests a bit on clay feet: it's unsuitable for scientific computing in a number of ways: for example you can neither write fast nor clean numerical code in java and it does not interface naturally with the languages that people do write numerical code in. This is not an absolute deal breaker, but it adds friction. Also, serveral big data tools like cassandra (scylladb), kafka (redpanda) etc. have basically already been rewritten in C++ not least because they suffer from java-typical performance problems (even if those problems are not necessarily inherent in the choice of java). But C++ is not a very attractive language to write code in if there are viable alternatives, because it is objectively unpleasant and expensive to use (long compile times, terrible build and packaging eco-system, high cognitive load, difficulty of avoiding undefined behavior, etc.). Rust shares some of these defects and has some of its own, but it also offers substantial benefits for some types of problems that seem relevant to data engineering and crucially it interfaces more naturally with Fortran, C++ and Python than Java does. So I think it has a chance to gradually make inroads.