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Mastering Apache Spark 2.x

You're reading from Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Length 354 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Author (1):

Romeo Kienzler

View More author details

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 FREE CHAPTER

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

We could have provided streaming examples for other systems as well, but there was no room in this chapter. Twitter streaming has been examined by example in the Checkpointing section. This chapter has provided practical examples of data recovery via checkpointing in Spark Streaming. It has also touched on the performance limitations of checkpointing and shown that the checkpointing interval should be set at five to ten times the Spark stream batch interval.

Checkpointing provides a stream-based recovery mechanism in the case of Spark application failure. This chapter has provided some stream-based worked examples for TCP, File, Flume, and Kafka-based Spark stream coding. All the examples here are based on Scala and compiled with sbt. In case you are more familiar with Maven the following tutorial explains how to set up a Maven based Scala project: http://www.scala-lang.org/old/node/345.

All of the code will be released with this book. Where the example architecture has become overcomplicated...

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Authors (1)

Romeo Kienzler

Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.

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