Apache Spark is one of the most compelling technologies in the big data space and for good reason. It allows data scientists and data engineers alike to work in their language of choice (Java, Scala, Python, SQL, and R as of this writing) to make sense of their data. As ReynoldXin noted, Apache Spark is the Swiss Army Knife of big data analytics tools. It allows you to use one tool to do many things from real-time streaming to advanced analytics. And in no small part, the versatility and power of GraphX has helped Spark propel forward.

Apache Spark Graph Processing follows Rindra's journey into solving complex analytics problems. As a PhD graduate in electrical engineering from the University of British Columbia, he focused on applying learning and optimization algorithms to achieve energy-efficient wireless networks. As he dove further into these problems, he realized the ease of which he could solve graph-processing problems by using Apache Spark GraphX. With a tutorial style and hands-on projects with interesting datasets, this book is a reflection of his path from getting started with Apache Spark GraphX to iterative graph parallel processing to learning graph structures.

This book is a great jump-start into GraphX, a practical guide for large-scale graph processing, and a testament to the author's enthusiasm for the Spark community (and the community as a whole).

Denny Lee

Technology Evangelist, Databricks

Advisor, WearHacks