Spark GraphX in Action (Edition 1) (Paperback)

Summary

Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets. GraphX gives you unprecedented speed and capacity for running massively parallel and machine learning algorithms.

About the Book

Spark GraphX in Action begins with the big picture of what graphs can be used for. This example-based tutorial teaches you how to use GraphX interactively. You'll start with a crystal-clear introduction to building big data graphs from regular data, and then explore the problems and possibilities of implementing graph algorithms and architecting graph processing pipelines. Along the way, you'll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data.

What's Inside

• Understanding graph technology
• Using the GraphX API
• Developing algorithms for big graphs
• Machine learning with graphs
• Graph visualization

About the Reader

Readers should be comfortable writing code. Experience with Apache Spark and Scala is not required.

About the Authors

Michael Malak has worked on Spark applications for Fortune 500 companies since early 2013. Robin East has worked as a consultant to large organizations for over 15 years and is a data scientist at Worldpay.

Table of Contents

PART 1 SPARK AND GRAPHS

1. Two important technologies: Spark and graphs
2. GraphX quick start
3. Some fundamentals

PART 2 CONNECTING VERTICES

4. GraphX Basics
5. Built-in algorithms
6. Other useful graph algorithms
7. Machine learning

PART 3 OVER THE ARC

8. The missing algorithms
9. Performance and monitoring
10. Other languages and tools