FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -GraphX. GraphX is a distributed graph-processing framework on top of Spark. It provides an API for expressing graph computation that can model the user-defined graphs by using Pregel abstraction API. It also provides an optimized runtime for this abstraction.

Detailed explanation-2: -The Pregel operator terminates iteration and returns the final graph when there are no messages remaining. Note, unlike more standard Pregel implementations, vertices in GraphX can only send messages to neighboring vertices and the message construction is done in parallel using a user defined messaging function.

Detailed explanation-3: -GraphX is Apache Spark’s API for graphs and graph-parallel computation. GraphX unifies ETL (Extract, Transform & Load) process, exploratory analysis and iterative graph computation within a single system.

Detailed explanation-4: -GraphX is a graph processing framework for big data. It is used for analyzing and processing large graphs in a distributed fashion. GraphX can solve various data science problems, including machine learning, social network analysis, recommendation systems, and bioinformatics.

Detailed explanation-5: -Spark GraphX Features It includes exploratory analysis, ETL (Extract, Transform & Load), the iterative graph in 1 system. It is possible to view the same data as both graphs, collections, transform and join graphs with RDDs. Also using the Pregel API it is possible to write custom iterative graph algorithms.