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Learning Apache Spark 2

You're reading from Learning Apache Spark 2

Product type Book

Published in Mar 2017

Publisher Packt

ISBN-13 9781785885136

Pages 356 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Table of Contents (18) Chapters

Learning Apache Spark 2

Credits

About the Author

About the Reviewers

www.packtpub.com

Customer Feedback

Preface

1. Architecture and Installation

2. Transformations and Actions with Spark RDDs

3. ETL with Spark

4. Spark SQL

5. Spark Streaming

6. Machine Learning with Spark

7. GraphX

8. Operating in Clustered Mode

9. Building a Recommendation System

10. Customer Churn Prediction

1. Theres More with Spark

Commonly supported file systems

Until now we have mostly focused on the functional aspects of Spark and hence tried to move away from the discussion of filesystems supported by Spark. You might have seen a couple of examples around HDFS, but the primary focus has been local file systems. However, in production environments, it will be extremely rare that you will be working on a local filesystem and chances are that you will be working with distributed file systems such as HDFS and Amazon S3.

Working with HDFS

Hadoop Distributed File System (HDFS) is a distributed, scalable, and portable filesystem written in Java for the Hadoop framework. HDFS provides the ability to store large amounts of data across commodity hardware and companies are already storing massive amounts of data on HDFS by moving it off their traditional database systems and creating data lakes on Hadoop. Spark allows you to read data from HDFS in a very similar way that you would read from a typical filesystem, with the only...

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