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Mastering Apache Spark 2.x - Second Edition

You're reading from Mastering Apache Spark 2.x - Second Edition

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Pages 354 pages

Edition 2nd Edition

Languages

Concepts

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

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

Why do we need just another library?

In order to answer this question, we have to know something about SystemML's history, which began ten years ago in 2007 as a research project in the IBM Almaden Research Lab in California. The project was driven by the intention to improve the workflow of data scientists, especially those who want to improve and add functionality to existing machine learning algorithms.

Note

So, SystemML is a declarative markup language that can transparently distribute work on Apache Spark. It supports Scale-up using multithreading and SIMD instructions on CPUs as well as GPUs and also Scale-out using a cluster, and of course, both together.

Finally, there is a cost-based optimizer in place to generate low-level execution plans taking statistics about the Dataset sizes into account. In other words, Apache SystemML is for machine learning, what Catalyst and Tungsten are for DataFrames.

Why on Apache Spark?

Apache Spark solves a lot of common issues in data processing and machine...

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