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Big Data Analytics with R

You're reading from  Big Data Analytics with R

Product type Book
Published in Jul 2016
Publisher Packt
ISBN-13 9781786466457
Pages 506 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Simon Walkowiak Simon Walkowiak
Profile icon Simon Walkowiak
LinkedIn
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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Table of Contents (16) Chapters

Big Data Analytics with R
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
The Era of Big Data Introduction to R Programming Language and Statistical Environment Unleashing the Power of R from Within Hadoop and MapReduce Framework for R R with Relational Database Management Systems (RDBMSs) R with Non-Relational (NoSQL) Databases Faster than Hadoop - Spark with R Machine Learning Methods for Big Data in R The Future of R - Big, Fast, and Smart Data

Introduction to NoSQL databases

We already know basic features and characteristics of the traditional Relational Database Management Systems (RDBMSs), which we presented in Chapter 5, R with Relational Database Management Systems (RDBMSs). We are also well aware of their limitations and specific requirements, for example, that they contain predefined schema, are vertically scalable which results in constant hardware upgrades to catch up with data growth, and they generally do not support unstructured or hierarchical data.

Non-relational or NoSQL databases attempt to fill these gaps and some of them are specialized in certain aspects more than others. In the next section, we will briefly present several NoSQL databases and their particular use cases.

Review of leading non-relational databases

To say that NoSQL databases are what SQL databases are not, may be a bit of an over-simplification. However, this statement is true to some extent and the following characteristics can shed some light on...

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Authors (1)

Profile icon Simon Walkowiak
LinkedIn
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).
Read more
See other products by Simon Walkowiak

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