Chapter 10 – Using Graph Analytics for Big Data

This chapter looks at business problems suited for graph analytics, what differentiates the problems from traditional approaches, and considerations for discovery vs. search analyses. We discuss the graph model (vertices and edges), as well as graph analytics. The representation of a graph model using triple notation makes it particularly suited to a big data application model. In addition, we discuss the difference between the use of a traditional data warehouse model for reporting and querying, versus the use of a graph model for discovery analytics. We look at some use cases for graph analytics (health care quality, cybersecurity, correlation analysis), and provide some solution approaches. We then look at the characteristics of a platform to be used for graph analytics, including seamless data intake, high-speed I/O, standards-based representations, and inferencing, multithreading, large memory, among others.