Google didn’t stop with MapReduce, but they developed other approaches for applications where MapReduce wasn’t a good fit, and I think this is an important message for the whole Big Data landscape. You cannot solve everything with MapReduce. You can make it faster by getting rid of the disks and moving all the data to in-memory, but there are tasks whose inherent structure makes it hard for MapReduce to scale.
Open source projects have picked up on the more recent ideas and papers by Google. For example, ApacheDrill is reimplementing the Dremel framework, while projects like Apache Giraph and Stanford’s GPS are inspired by Pregel.
See on architects.dzone.com