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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

Hadoop architecture

Apache Hadoop is an open source, integrated framework for Big Data processing and management, which can be relatively easy to deploy on commodity hardware. Hadoop can also be defined as an ecosystem of tools and methods that allow distributed storage and analytics of massive amounts of structured and unstructured data. In this section, we will present an array of tools, frameworks, and applications that come as integral parts of the Hadoop ecosystem and are responsible for a variety of data management and processing purposes.

Hadoop Distributed File System

As explained in Chapter 1, The Era of Big Data, Hadoop Distributed File System (HDFS) derives from the original Google File System presented in 2003 in a paper titled The Google file system authored by Ghemawat, Gobioff, and Leung. The architecture and design of current HDFS (based on the Apache Hadoop 2.7.2 release) are explained thoroughly in the HDFS Architecture Guide available at the Apache website at http://hadoop...

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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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