Hadoop is beatable
The problem with simple batch processing tools like MapReduce and Hadoop is that they are just not powerful enough in any one of the dimensions of the big data space that really matters. If you need complex joins or ACID requirements, SQL beats Hadoop easily. If you have realtime requirements, Cloudscale beats Hadoop by three or four orders of magnitude. If you have supercomputing requirements, MPI or BSP beat Hadoop easily. If you have graph computing requirements, Google's Pregel beats Hadoop by orders of magnitude. If you need interactive analysis of web-scale data sets, then Google's Dremel architecture beats Hadoop by orders of magnitude. If you need to incrementally update the analytics on a massive data set continuously, as Google now have to do on their index of the web, then an architecture like Percolator beats Hadoop easily.
via sys-con.com
