Now you rarely use basic MapReduce primitives, you have another layer of abstraction that can run on infrastructure that was running MR jobs before. This infrastructure allows to efficiently allocate some compute resources for "long" running tasks in a large cluster with respect to memory/cpu/network and other constraints. So basically schedulers of MapReduce jobs and cluster management tools became that good, because MR methodology had trivial abstractions, but required efficient implementation to make it work seamlessly.
Abstraction layers on top of this infrastructure now can optimize pipeline as a whole by merging several steps into one when possible, add combiners(partial reduce before shuffle). It requires whole processing pipeline to be defined in more specific operations. Some of them propose to use SQL to formulate task, but it can be done using other primitives. And given this pipeline it is easy to implement optimizations making whole system much more user-friendly and efficient compared to MapReduce, when user has to think about all the optimizations and implement them inside single map/reduce/(combine) operations.