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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

Our continuing theme when examining both Apache Hadoop and Spark is that none of these systems stand alone. They need to be integrated to form ETL-based processing systems. Data needs to be sourced and processed in Spark and then passed to the next link in the ETL chain or stored. We hope that this chapter showed you that Spark functionality can be extended with extra libraries and systems such as H2O, DeepLearning4j. Even Apache SystemML supports DeepLearning now and TensorFlow can be run within Apache Spark using TensorFrames and TensorSpark.

Although Apache Spark MLlib and SparkML has a lot of functionality, the combination of H2O Sparkling Water and the Flow web interface provides an extra wealth of data analysis modeling options. Using Flow, you can also visually and interactively process your data. We hope that this chapter shows you, even though it cannot cover all that all these libraries offer, that the combination of Spark and third-party libraries widens your data processing...

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