The Hadoop-based solution processes batches to provide high throughputs but at the cost of high latency and denormalization of data. The Neo4j approach is the perfect candidate for OLTP processing on native graph data, with an added advantage of real-time OLAP operations that provide a modest throughput but speed things up with a quite low latency. So, depending upon the type of data and the requirement of your application, you can select one of the methods for an advanced graph-processing approach. If OLTP is what you need with deep analytical insights into your data in near real-time, then Neo4j is the answer to your prayers. For more relaxed scenarios that can bear the high latencies in order to achieve higher throughput, then you should consider graph-processing platforms such as Hadoop or Pregel (developed at Google).

In fact, there have also been attempts to combine Hadoop's processing capabilities with the native graph storage of Neo4j. You can check...