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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

Product type Paperback

Published in Jun 2017

Publisher Packt

ISBN-13 9781787287945

Length 296 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Big Data

Author (1):

Frank Kane

View More author details

Table of Contents (8) Chapters

Preface

1. Getting Started with Spark

2. Spark Basics and Spark Examples FREE CHAPTER

3. Advanced Examples of Spark Programs

4. Running Spark on a Cluster

5. SparkSQL, DataFrames, and DataSets

6. Other Spark Technologies and Libraries

7. Where to Go From Here? – Learning More About Spark and Data Science

Introducing Elastic MapReduce

The easiest way to actually get up and running on a cluster, if you don't already have a Spark cluster, is using Amazon's Elastic MapReduce service. Even though it says MapReduce in the name, you can actually configure it to set up a Spark cluster for you and run that on top of Hadoop â it sets everything up for you automatically. Let's walk through what Elastic MapReduce is, how it interacts with Spark, and how to decide if it's really something you want to be messing with.

Why use Elastic MapReduce?

Using Amazon's Elastic MapReduce service is an easy way to rent the time you need on a cluster to actually run your Spark job. You don't have to just run MapReduce jobs, you can actually run Spark and use the underlying Hadoop environment to run as your cluster manager. It has something called Hadoop YARN. If you've taken my course on MapReduce and Hadoop you will have heard of this already. Basically, YARN is Hadoop's cluster manager and Spark is able to run on...

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Authors (1)

Frank Kane

Frank Kane

Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.

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