Graph Processing with Spark GraphX

In many use cases, the relationship between data points provides as much value or more than the data points themselves. Discovering data relationships and interdependencies is critical to applications ranging from fraud detection to better understanding customer relationships to ranking web pages or people in social networks.
Graph analytics is a powerful tool in understanding and exploiting the connections in data. To enable graph analytics, graph databases utilize graph structures with nodes, edges and properties to represent and store data. In graph databases, the data is stored linked together for simple quick retrieval and exploration of complex relationships. Popularized by early adopters such as Facebook and LinkedIn, graph applications are everywhere today and are a critical component of many next generation applications.
GraphX is Apache Spark’s API for graph computation. GraphX unifies ETL, exploratory analysis, and iterative graph computation within a single framework. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks. Because GraphX builds on Spark’s core, it benefits from the performance, flexibility, fault tolerance and ease of use of Spark.
In this session, we will explore GraphX and walk through a detailed demo of GraphX running in a Spark notebook. Pizza and drinks will be served.