Graph Analytics at Scale using SparkSQL and GraphFrames

Chris Snyder, the Manager of Data Science at Jornaya, walks us through graph analytics at scale using SparkSQL and GraphFrames. In this talk, you will learn about work that has made graph algorithms in GraphFrames faster and more scalable. We will walk through some of these algorithms, in particular the connected components algorithm implemented in GraphFrames, and demonstrate their usage.

Graph analytics has a wide range of applications, from information propagation and network flow optimization to fraud and anomaly detection. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. GraphFrames is an open source project that leverages SparkSQL to execute graph algorithms at scale.