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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. A First Taste and What’s New in Apache Spark V2

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

Summary

This chapter started with an architectural overview on GraphFrames. We saw how GraphFrames can make use of the Catalyst and Tungsten optimizers by running on top of DataFrames.

Additional optimizations on top of these were explained. Finally, we showed, by example, how Scala-based code can be used to call GraphFrames algorithms in Apache Spark. Scala has been used because it requires less code to develop the examples, which saves time; a Scala-based shell can be used and the code can be compiled into Spark applications.

The configuration and code examples from this chapter are also available for download with the book. If you want to learn more on GraphFrames please refer to this link https://databricks.com/blog/2016/03/03/introducing-graphframes.html.

Now let's have a look how Apache Spark can be used in the Cloud in conjunction with Jupyter in the next chapter. This is a very convenient user interface from which to do data science.

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