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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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

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

Example--Apache Spark on Kubernetes

This example is taken directly from the Kubernetes GitHub page, which can be found at https://github.com/Kubernetes/Kubernetes/tree/master/examples/spark. We've done some modification to that example, since we are using a very specific Kubernetes deployment called Minikube. But we still want it to be based on the original example, since when you are using this link, you are guaranteed to always obtain an updated version compatible with the latest Kubernetes version in place. So these are the required steps, which are explained in detail in the next sections:

  1. Install Minikube local Kubernetes to your machine.
  2. Deploy the Apache Spark master node.
  3. Deploy the Apache Spark worker nodes.
  4. Deploy the Apache Kubernetes notebook application and test the whole cluster (optional).

The following section describes the prerequisites to run the example...

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