Book Image

Apache Spark 2 for Beginners

By : Rajanarayanan Thottuvaikkatumana
Book Image

Apache Spark 2 for Beginners

By: Rajanarayanan Thottuvaikkatumana

Overview of this book

<p>Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.</p> <p>This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.</p> <p>By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.</p>
Table of Contents (15 chapters)
Apache Spark 2 for Beginners
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Chapter 4. Spark Programming with R

R is a popular statistical computing programming language used by many and freely available under the General Public License (GNU). R originated from the programming language S, created by John Chambers. R was developed by Ross Ihaka and Robert Gentleman. Many data scientists use R for their computing needs. R has inherent support for many statistical functions and many scalar data types, and has composite data structures for vectors, matrices, data frames, and more, for statistical computation. R is highly extensible and for that, external packages can be created. Once an external package is created, it has to be installed and loaded for any program to use it. A collection of such packages under a directory forms an R library. In other words, R comes with a set of base packages and additional packages that can be installed on top of it to form the required library for the desired computing needs. In addition to functions, datasets can also be packaged...