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Spark GraphX In Action

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 technologyUsing the GraphX APIDeveloping algorithms for big graphsMachine learning with graphsGraph 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 GRAPHSTwo important technologies: Spark and graphsGraphX quick startSome fundamentalsPART 2 CONNECTING VERTICESGraphX BasicsBuilt-in algorithmsOther useful graph algorithmsMachine learningPART 3 OVER THE ARCThe missing algorithmsPerformance and monitoringOther languages and tools

Paperback: 280 pages

Publisher: Manning Publications; 1 edition (July 3, 2016)

Language: English

ISBN-10: 1617292524

ISBN-13: 978-1617292521

Product Dimensions: 7.3 x 0.5 x 9.2 inches

Shipping Weight: 15.2 ounces (View shipping rates and policies)

Average Customer Review: 5.0 out of 5 stars  See all reviews (6 customer reviews)

Best Sellers Rank: #258,141 in Books (See Top 100 in Books) #56 in Books > Computers & Technology > Web Development & Design > Web Services #60 in Books > Textbooks > Computer Science > Algorithms #126 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design

Our team has worked with Spark since 1.0 and I needed to get up to speed. My team recommended this book. The team is made up of experienced people; some have advanced degrees and all have over 20 years experience each. The point is not to brag, but to suggest we have had to evaluate multiple books in the past. But, note this book is accessible to anyone with some programming experience and curiosity.The authors are clearly knowledgeable and presentation and flow are clear and concise. Chapter 3, in particular, which covers fundamentals of Spark and Scala, was very useful. Compared to the large tomes out there on these subjects, this chapter quickly covered what I needed to move forward. The same applied to Chapter 4, which introduces GraphX fundamentals.I have prior experience with machine learning and I appreciated Chapter 7 covering Spark capabilities couched in supervised, unsupervised, and semi-supervised terms. This partitioned GraphX (and some MLlib) capabilities into manageable chunks by application. These concepts are introduced as well, but those familiar with machine learning may feel at home too.Monitoring Spark job progress and performance is not always the easiest. In Chapter 9, the authors’ real-world experience shines with tips and tricks that would only come with actual hands-on trial and error. This is important. When using a new technology, things may not work as expected and learning how to debug at the same time is twice as brutal. So, having real-world operational debugging approaches really helped.I do wish some of the examples were a little more in-depth. The authors appear to have the experience to have done this.

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