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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

Summary


We saw that containerization is the new way to go and that managing an abundance of containers requires orchestrations. We learned that Kubernetes is such an orchestrator and we saw how it can be used to create a simple Apache Spark cluster within minutes. We used a local test installation for playing around with Kubernetes, but the example shown can be used out-of-the-box on any Kubernetes installation.

We also saw that using Kubernetes as a service in the cloud can be very beneficial when used in conjunction with Apache Spark, since in the cloud the underlying Docker containers are charged on an hourly basis, therefore making it possible to elastically grow and shrink the Apache Spark cluster on the fly, as needed.

Finally, using Kubernetes in the cloud and in the local data center as well allows a broader usage scenario in a so-called hybrid cloud approach. In such an approach, depending on constraints such as data protection laws and resource requirements, an Apache Spark cluster...

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