Graph data analysis is a prime technique to extract information from very large datasets by assessing the similarity or associativity of data points. The need for such techniques arose when social networks started gaining popularity and expanded their user base rapidly, but today, graph analysis has a much broader scope of application.

Since graph processing has caught up in the race for crunching data, big data platforms and communities have been innovatively adapting themselves to the needs for solving graph problems with frameworks, such as Apache Giraph (http://giraph.apache.org/) and MapReduce extensions such as Pregel (goo.gl/hW3L40), Surfer, and GBASE (http://goo.gl/3QkB46); it is becoming simpler to address graph-processing issues.

Hadoop is a large-scale distributed batch-processing framework that operates at high latencies unlike graph databases. So, if you implement graph processing on a Hadoop-based system, data locality will lead to a more efficient batch...