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Learning Apache Spark 2

You're reading from   Learning Apache Spark 2 A beginner's guide to real-time Big Data processing using the Apache Spark framework

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785885136
Length 356 pages
Edition 1st Edition
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Author (1):
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Muhammad Asif Abbasi Muhammad Asif Abbasi
Author Profile Icon Muhammad Asif Abbasi
Muhammad Asif Abbasi
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Table of Contents (12) Chapters Close

Preface 1. Architecture and Installation 2. Transformations and Actions with Spark RDDs FREE CHAPTER 3. ETL with Spark 4. Spark SQL 5. Spark Streaming 6. Machine Learning with Spark 7. GraphX 8. Operating in Clustered Mode 9. Building a Recommendation System 10. Customer Churn Prediction Theres More with Spark

What is GraphX?


Spark GraphX is not the first system to bring graph parallel computation to the mainstream, as even before this project, people used to perform graph computation on Hadoop, although they had to spend considerable time to build a graph on Hadoop. This resulted in creation of specialized systems such as Apache Giraph (an open source version of Google's Pregel), which ensured that the graph processing times come down to a fraction of what they were on Hadoop. However, graph processing is not isolated, and is very similar to MLLib where you have to spend time to load the data and pre-process it before running a machine learning pipeline. Similarly, the full data processing pipeline isn't just about running a graph algorithm, and graph creation is an important aspect of the problem, including performing post-processing, that is, what to do with the result. This was beautifully presented in a UC Berkley AmpLab talk in 2013 by Joseph Gonzalez and Reynold Xin.

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