Very good points about some problem areas where Hadoop is not that useful:
Map-reduce performs poorly on algorithms that rely on intra-data relationships. For example, clustering algorithms are supposed to find geometric regions of data. To pull this off, the algorithm must effectively compare every data point with every other data point. These intra-data relationships are the death nail for Hadoop. Map-reduce fundamentally struggles to compare datapoints with other datapoints.
Consider another example: recommendations. A recommendation engine is an implementation of a clustering algorithm. Although it’s possible to run this on Hadoop, our experience has shown that it takes six times longer than a non-Hadoop implementation.
Original title and link: Hadoop Doesn’t Solve All Problems ( ©myNoSQL)