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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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Length 354 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Author (1):

Romeo Kienzler

View More author details

Table of Contents (21) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

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

Chapter 9. Apache SystemML

So far, we have only covered components that came along with the standard distribution of Apache Spark (except HDFS, Kafka and Flume, of course). However, Apache Spark can also serve as runtime for third-party components, making it as some sort of operating system for big data applications. In this chapter, we want to introduce Apache SystemML, an amazing piece of technology initially developed by the IBM Almaden Research Lab in California. Apache SystemML went through many transformation stages and has now become an Apache top level project.

In this chapter, we will cover the following topics to get a greater insight into the subject:

Using SystemML for your own machine learning applications on top of Apache Spark
Learning the fundamental differences between SystemML and other machine learning libraries for Apache Spark
Discovering the reason why another machine library exists for Apache Spark

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Authors (1)

Romeo Kienzler

Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.

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