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Apache Spark for Data Science Cookbook

You're reading from Apache Spark for Data Science Cookbook

Product type Book

Published in Dec 2016

Publisher

ISBN-13 9781785880100

Pages 392 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Padma Priya Chitturi

Table of Contents (17) Chapters

Apache Spark for Data Science Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

1. Big Data Analytics with Spark

2. Tricky Statistics with Spark

3. Data Analysis with Spark

4. Clustering, Classification, and Regression

5. Working with Spark MLlib

6. NLP with Spark

7. Working with Sparkling Water - H2O

8. Data Visualization with Spark

9. Deep Learning on Spark

10. Working with SparkR

Working with H2O on Spark

Sparkling Water is executed as a regular Spark application. It provides a way to initialize H2O services on each node in the Spark Cluster and to access data stored in the data structures of Spark and H2O. The Sparkling Water application is launched inside a spark executor created after submitting the application. At this point, H2O starts the services, including the distributed key value (KV) storage and the memory manager.

Getting ready

To step through this recipe, you will need a running Spark Cluster in any one of the following modes: Local, standalone, YARN, Mesos. You must also include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java.

How to do it…

In this recipe, we'll learn how to download and install H2O services in a Spark Cluster. We'll also use the H2O API in Spark.

The list of sub-recipes in this section is as follows:

Downloading and installing H2O...

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Authors (1)

Padma Priya Chitturi

Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.

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