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Big Data Analytics with R

You're reading from Big Data Analytics with R Leverage R Programming to uncover hidden patterns in your Big Data

Product type Paperback

Published in Jul 2016

Publisher Packt

ISBN-13 9781786466457

Length 506 pages

Edition 1st Edition

Languages

R

Tools

Hadoop

Concepts

Big Data

Author (1):

Simon Walkowiak

View More author details

Table of Contents (10) Chapters

Preface

1. The Era of Big Data FREE CHAPTER

2. Introduction to R Programming Language and Statistical Environment

3. Unleashing the Power of R from Within

4. Hadoop and MapReduce Framework for R

5. R with Relational Database Management Systems (RDBMSs)

6. R with Non-Relational (NoSQL) Databases

7. Faster than Hadoop - Spark with R

8. Machine Learning Methods for Big Data in R

9. The Future of R - Big, Fast, and Smart Data

Chapter 7. Faster than Hadoop - Spark with R

In Chapter 4, Hadoop and MapReduce Framework for R, you learned about Hadoop and MapReduce frameworks that enable users to process and analyze massive datasets stored in the Hadoop Distributed File System (HDFS). We launched a multi-node Hadoop cluster to run some heavy data crunching jobs using R language which would not be otherwise achievable on an average personal computer with any of the R distributions installed. We also said that although Hadoop is extremely powerful, it is generally recommended for data that greatly exceeds the memory limitations due to its rather slow processing. In this chapter we would like to present Apache Spark engine–a faster way to process and analyze Big Data. After reading this chapter, you should be able to:

Understand and appreciate Spark characteristics and functionalities
Deploy a fully-operational, multi-node Microsoft Azure HDInsight cluster with Hadoop, Spark, and Hive fully-configured...

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Authors (1)

Walkowiak

Walkowiak

Simon Walkowiak is a cognitive neuroscientist and a managing director of Mind Project Ltd a Big Data and Predictive Analytics consultancy based in London, United Kingdom. As a former data curator at the UK Data Service (UKDS, University of Essex) European largest socio-economic data repository, Simon has an extensive experience in processing and managing large-scale datasets such as censuses, sensor and smart meter data, telecommunication data and well-known governmental and social surveys such as the British Social Attitudes survey, Labour Force surveys, Understanding Society, National Travel survey, and many other socio-economic datasets collected and deposited by Eurostat, World Bank, Office for National Statistics, Department of Transport, NatCen and International Energy Agency, to mention just a few. Simon has delivered numerous data science and R training courses at public institutions and international companies. He has also taught a course in Big Data Methods in R at major UK universities and at the prestigious Big Data and Analytics Summer School organized by the Institute of Analytics and Data Science (IADS).

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