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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 2. Apache Spark SQL FREE CHAPTER 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

Apache Spark with Jupyter Notebooks on IBM DataScience Experience

When talking about the cloud, nearly everybody has a different view. Some think of virtual machines, which you can start and stop instantaneously, while some others think of file storage. But the cloud is more than that. In this chapter, we will talk about using Apache Spark in the cloud. But this is, of course, more than provisioning some virtual machines and installing an Apache Spark cluster on it.

We are talking about the so-called Platform as a service clouds (PaaS), where everything is a service, ready to be consumed, and the operation of components is done by the cloud provider. Therefore, on PaaS clouds, Apache Spark is pre-installed. This is done in a way that workers can be added to and removed from the cluster on-the-fly. But not only is Apache Spark pre-installed, the required tooling around it is also...

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