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Apache Spark 2.x Machine Learning Cookbook

You're reading from   Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781783551606
Length 666 pages
Edition 1st Edition
Languages
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Authors (5):
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Broderick Hall Broderick Hall
Author Profile Icon Broderick Hall
Broderick Hall
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
Mohammed Guller Mohammed Guller
Author Profile Icon Mohammed Guller
Mohammed Guller
Siamak Amirghodsi Siamak Amirghodsi
Author Profile Icon Siamak Amirghodsi
Siamak Amirghodsi
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Table of Contents (14) Chapters Close

Preface 1. Practical Machine Learning with Spark Using Scala FREE CHAPTER 2. Just Enough Linear Algebra for Machine Learning with Spark 3. Spark's Three Data Musketeers for Machine Learning - Perfect Together 4. Common Recipes for Implementing a Robust Machine Learning System 5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I 6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II 7. Recommendation Engine that Scales with Spark 8. Unsupervised Clustering with Apache Spark 2.0 9. Optimization - Going Down the Hill with Gradient Descent 10. Building Machine Learning Systems with Decision Tree and Ensemble Models 11. Curse of High-Dimensionality in Big Data 12. Implementing Text Analytics with Spark 2.0 ML Library 13. Spark Streaming and Machine Learning Library

Streaming DataFrames for real-time machine learning

In this recipe, we explore the concept of a streaming DataFrame. We create a DataFrame consisting of the name and age of individuals, which we will be streaming across a wire. A streaming DataFrame is a popular technique to use with Spark ML since we do not have a full integration between Spark structured ML at the time of writing.

We limit this recipe to only the extent of demonstrating a streaming DataFrame and leave it up to the reader to adapt this to their own custom ML pipelines. While streaming DataFrame is not available out of the box in Spark 2.1.0, it will be a natural evolution to see it in later versions of Spark.

How to do it...

  1. Start a new project in IntelliJ...
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