Graph processing is an exciting development for those in the graph database space, since the utility of graph databases has been reinforced as a storage system as well as a computational model. However, the processing of graph-like data can be confused with graph databases due to the common data models they share, although each technique operates on fundamentally different scenarios. Some graph-processing platforms such as Pregel, developed by Google, are capable of achieving high-computational throughput, since it adopts the Bulk Synchronous Processing (BSP) model from the domain of parallel computing. This model supports the partition of the graph into multiple machines and uses the localized data from the vertices for computation. Exchange of local information takes place during the synchronization process. This model is used to process large interconnected datasets for business insights compared to traditional map-reduce operations, although high latency is a concern...