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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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Concepts
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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

Understanding the core concepts of Docker

So now it's time to further unfold the topic by introducing Docker. Docker basically makes use of LXC but adds support for building, shipping, and running operation system images. So there exists a layered image format, which makes it possible to pack the filesystem components necessary for running a specific application into a Docker images file.

Although not necessary for this chapter because it is already provided by the following minikube package we are using, Docker can be easily installed on different operating systems. Since Docker uses functionality only present in the Linux kernel, it can be run natively only on Linux (and only there you will see the performance benefits over using virtual machines). But you still can use it on macOS and Windows, where a separate hypervisor is running Docker on Linux in the background. So...
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