What this book covers

This book consists of seven chapters. The first three chapters help you to get started quickly with Spark and GraphX. Then, the next two chapters teach the core techniques and abstractions to manipulate and aggregate graph data. Finally, the last two chapters of this book cover more advanced topics such as graph clustering, implementing graph-parallel iterative algorithms with Pregel, and learning methods from graph data.

Chapter 1, Getting Started with Spark and GraphX, begins with an introduction to the Spark system, its libraries, and the Scala Build Tool. It explains how to install and leverage Spark on the command line and in a standalone Scala program.

Chapter 2, Building and Exploring Graphs, presents the methods for building Spark graphs using illustrative network datasets.

Chapter 3, Graph Analysis and Visualization, walks you through the process of exploring, visualizing, and analyzing different network characteristics.

Chapter 4, Transforming and Shaping Up Graphs to Your Needs, teaches you how to transform raw datasets into a usable form that is appropriate for later analysis.

Chapter 5, Creating Custom Graph Aggregation Operators, teaches you how to create custom graph operations that are tailored to your specific needs with efficiency in mind, using the powerful message-passing aggregation operator in Spark.

Chapter 6, Iterative Graph-Parallel Processing with Pregel, explains the inner workings of the Pregel computational model and describes some use cases.

Chapter 7, Learning Graph Structures, introduces graph clustering, which is useful for detecting communities in graphs and applies it to a social music database.