Book Image

Big Data Analytics with R and Hadoop

By : Vignesh Prajapati
Book Image

Big Data Analytics with R and Hadoop

By: Vignesh Prajapati

Overview of this book

<p>Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing. <br /><br />Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.<br /><br />You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.</p>
Table of Contents (16 chapters)
Big Data Analytics with R and Hadoop
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Preface
Index

Understanding Hive


Hive is a Hadoop-based data warehousing-like framework developed by Facebook. It allows users to fire queries in SQL, with languages like HiveQL, which are highly abstracted to Hadoop MapReduce. This allows SQL programmers with no MapReduce experience to use the warehouse and makes it easier to integrate with business intelligence and visualization tools for real-time query processing.

Understanding features of Hive

The following are the features of Hive:

  • Hibernate Query Language (HQL)

  • Supports UDF

  • Metadata storage

  • Data indexing

  • Different storage type

  • Hadoop integration

Prerequisites for RHive are as follows:

  • Hadoop

  • Hive

We assume here that our readers have already configured Hadoop; else they can learn Hadoop installation from Chapter 1, Getting Ready to Use R and Hadoop. As Hive will be required for running RHive, we will first see how Hive can be installed.

Installing Hive

The commands to install Hive are as follows:

// Downloading the hive source from apache mirror
wget http://www...