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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

Cloud

Although parts of this book will concentrate on examples of Apache Spark installed on physically server-based clusters, we want to make a point that there are multiple cloud-based options out there that imply many benefits. There are cloud-based systems that use Apache Spark as an integrated component and cloud-based systems that offer Spark as a service. Even though this book cannot cover all of them in depth, it would be useful to mention some of them:

  • Chapter 13, Apache Spark with Jupyter Notebooks on IBM DataScience Experience, is an example of a completely open source based offering from IBM
  • Chapter 14, Apache Spark on Kubernetes, covers the Kubernetes Cloud Orchestrator, which is available as offering of on many cloud services (including IBM) and also rapidly becoming state-of-the-art in enterprise data centers
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