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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

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781786466457
Length 506 pages
Edition 1st Edition
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Author (1):
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Simon Walkowiak Simon Walkowiak
Author Profile Icon Simon Walkowiak
Simon Walkowiak
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Table of Contents (10) Chapters Close

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

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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