Distributed In-memory Cluster Computing Approach in Scala for Solving Graph Data Applications

Large graph analysis is one of the significant applications of distributed computing frameworks. The distributed computing applications are solved by developing programs over different types of established distributed computing frameworks. Since graph analysis and prediction is one of the new trend in data analytics, designing the problems on an in-memory cluster framework which consumes graph data-sets have a significant role in distributed computing. Traditional disk-based distributed computing framework like hadoop will confine only to a specific group of problems in data analytics. The importance of utilizing the memory of the cluster apart from the disk-based storage space contributes a significant role in reducing the latency and increasing the speedup. The whole work describes the significance of spark-framework in solving graph related problems in a distributed approach using page ranking algorithm and proteome-protein annotation method in Scala.