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Key benefits

Perform computational analyses on Big Data to generate meaningful results

Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,

Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market

Description

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.

Who is this book for?

This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.

What you will learn

Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities

Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner

Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage

Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Amazon verified reviews

Sungho Hwang Feb 24, 2019

Big data analysis has been a trend in Korea too! This book solved my curiosity of big data analysis and the practice with R.

Amazon Verified review

f.janvier Feb 14, 2017

It was exactly what I was looking for: a pedagogical read guiding me towards the next step with R. I'm an intermediate-level R user (not my first programming language), working mainly with relational databases and non-big data volumes. This book gave me a clear view of what is possible with R once the data become larger, analysis is moved to the cloud and a big-data environment (Hadoop & co) comes into play.

sbeltran Dec 03, 2017

I really enjoyed this book. Every concept is thoroughly explained, and the comparisons with other programs and platforms are really helpful. I don’t think there’s a better resource to learn R for data analytics (I’ve definitely looked and have been disappointed many, many times), so I can absolutely recommend this book.

Z.V. Aug 14, 2016

Great book for anyone wishing to hone their data engineering skills, having the R platform as their basis. Although this book is intended for the intermediate data science professional, some of the concepts it covers are fairly advanced, yet explained in such a way that they seem elementary. Some experience with the Linux OS (particularly the CLI aspect of it) would be very useful as the author goes into the nitty-gritty often, in order to perform low-level operations that enable the data engineering tasks he describes. He also provides a plethora of meticulously illustrated examples that make all the concepts he introduces hands-on and comprehensible.R is not my platform of choice (in fact I rarely use it nowadays), but I still enjoyed reading this book at least twice and I would recommend it to anyone who wishes to advance their data science expertise, using this particular platform. This is not a book you would read on your commute though. For best results I would recommend you treat it like a textbook and follow all of the examples on your computer.Disclaimer: I have been given a copy of this book for free in order to provide the Amazon community with an unbiased review of it. So, even though I'm not a verified purchaser, trust me when I say it, I know this book inside-out!

Wolf Aug 02, 2016

This is an excellent book. It is so from the purely technical point of view but it is also full of interesting facts and historical notes that make it entertaining as well. Well written and pedagogical, I definitely recommend it for anyone also looking for the R- Big Data connection.

Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data

What do you get with eBook?