It's a good question. I'm punting for the moment because it isn't clear that there is one true solution that makes everyone happy. So, let's say MPI then. :)

I think the more optimistic version is probably that the signals Timely Dataflow (the model) gives should make tasteful fault-tolerance logic easier to write. Michael Isard did some work here just before leaving MSR. Realistically, errors haven't been a big problem yet (thanks Rust!) and it's a research prototype for understanding efficient data-parallel compute, so not too bothered yet.

The MR/Spark approach comes with lots of hidden costs in the programming model; I'm not clear on when these costs make sense. Using lots more resources so that you survive being killed because you were using too many resources seems like a not-best solution.

Some of the layers on top have better fault-tolerance properties. For example, differential dataflow operators are functional and have append-only logs as their internal state; I could see that stack ending up with more transparent fault-tolerance properties, though not yet.