Integration of R Programming with Hadoop

Posted by Ash Steinfeld on July 7th, 2022

What is R Programming?

R has open-source programming tools for data science. It works best for statistical and graphical analysis. Additionally, we must mix R and Hadoop if we need robust data analytics and visualization tools.

What is Hadoop?

The ASF, or Apache Software Foundation, founded Hadoop as an open-source application. It is also an open-source project, meaning anyone can use it for free and modify its source code to suit their needs. However, if a particular function doesn't suit your demands, you can also change it. It also offers a powerful foundation for managing jobs.

The purpose behind R and Hadoop Integration

The programming language, R is one of the most popular choices for statistical computing and data analysis. However, without supplementary packages, it falls short a little in terms of managing memory and handling big amounts of data.

On the other hand, Hadoop, with its distributed file system HDFS and the map-reduce processing method, is a potent tool for processing and analyzing enormous amounts of data. At the same time, Hadoop and R both make sophisticated statistical calculations simple.

Using these two technologies, R's statistical computing power can be merged with effective distributed computing. This implies that we can: Run R code using Hadoop.

To access the data kept in Hadoop, use R.

R and Hadoop Integration Methods

Four different techniques exist for integrating R programming with Hadoop:


  1. R Hadoop

The R Hadoop approach consists of three packages. Here, we'll talk about the three packages' features.


  • The rmr package

It gives the Hadoop framework access to the MapReduce capability. Additionally, it offers functionality via running R's mapping and reducing codes.

  • The rhbase package

It will give you access to R database administration functionality with HBase integration.

  • The rhdfs package

It is the HDFS integration's file management capabilities.


  1.  Hadoop Streaming

Its HBase integration and R database management capabilities. The R script known as Hadoop streaming is a component of the R package on CRAN. Additionally, this aims to increase R's usability for Hadoop streaming applications. Additionally, this enables the creation of MapReduce programmes in languages other than Java.

It is particularly user-friendly because MapReduce instructions are written in the R programming language. The native language of MapReduce is Java. However, it isn't ideal for high-speed data analysis given the demands of the modern world. We, therefore, require faster mapping and reduction procedures with Hadoop the present.

Due to the fact that we can create the scripts in Python, Perl, or even Ruby, Hadoop streaming has seen tremendous growth.


  1.  RHIPE

R and Hadoop Integrated Programming Environment is known as RHIPE. This integrated programming environment was created by Divide and Recombine to facilitate the effective analysis of massive amounts of data.

Working with the Hadoop and R integrated programming environment is required. RHIPE data sets can be accessed using Python, Java, or Perl. RHIPE has a number of features that help you communicate with HDFS. Consequently, employing this method allows you to read and save all of the data produced by RHIPE MapReduce.

  1. ORCH

Oracle R Connector is the name of it. It may be used to specifically work with big data on non-Oracle frameworks like Hadoop and in Oracle appliances.

ORCH makes it easier to build mapping and reduction functions and access the Hadoop cluster using R. The data that is stored in the Hadoop Distributed File System can also be modified.

  1. IBM's BigR

IBM's BigR offers complete integration between R and BigInsights, the company's Hadoop package. Users can concentrate on the R application rather than MapReduce tasks while using BigR to evaluate data stored in the HDFS. The concurrent execution of R code throughout the Hadoop cluster is made possible by combining the BugInsights and BigR technologies.

Summary

In this blog, we have carefully examined the interaction of R with Hadoop. We also discussed the various approaches for integrating R programming with Hadoop.

To learn more about Hadoop or R for data science, visit the data science course in Mumbai. Co-powered by IBM, the data science courses are designed for working professionals of all domains. 

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Ash Steinfeld

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Ash Steinfeld
Joined: July 7th, 2022
Articles Posted: 3

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