Hadoop Integration with R Programming Language

What is R Programming?

R is a programming language which used for hadoop technologies like data analytics, statistical analysis and hadoop graph report presentation. R is the most popular language used by data scientist and data researchers. R comes from interpreter commands and also called interpreted language available for MAC and Windows.

Why use R on Hadoop?

R is a popular datascience programming tool used for data analytics. One main disadvantage of R is all data are stored in one main memory, when R programming integrated with hadoop is an best solution for this problem. Integrate R on Hadoop will provide highly scalable data which based on size of hadoop dataset.

How to Integrate Hadoop on R?

There are five ways of using integrate hadoop and R

Rhadoop – Integrate hadoop and R programming language called Rhadoop. It provided by Revolution analytics used for directly insert the data from HDFS systems and Hbase systems. Rhadoop is a collection of five packages for manages the data using R programming language that packages are rhbse, rhdfs, plyrmr, ravro and rmr2

1. rhbase – It provides the database management for hbase stores data and hdfs files. If using rhbase you can read, write and accessing the hbase data from R
2. rhdfs – It is the connectivity package for hdfs so you can read, write and modify the data which stored in hadoop
3. plyrmr – It supports the data manipulation operations managed by hadoop. Plyrmr depends upon MapReduce to perform the manipulation operation
4. ravro – This package used to allow users for read and write avro files from hdfs.
5. Rmr2 – It is used to statistical analysis on hadoop cluster and helps to move and handles the large hadoop datasets

Hadoop Streaming – Hadoop streaming used to runs the MapReduce jobs that give standard output data as mapper or reducer. In this method no need any client side integration because its access data through command line

RHIPE – RHIPE means R and Hadoop Integrated Programming Environment. It allows runs MapReduce jobs within R. In this method programmers write R Maps and R Reduce functions only and RHIPE transfer data to Hadoop MapReduce tasks.

RHIVE(Install R on Workstations and Connect to Data in Hadoop) – Rhive is a statistical libraries which available in R programming. It is used to extending the HiveQl and query language.

ORCH (Oracle Connector for Hadoop) – It can be used to non-oracle hadoop clusters. Mappers and Reducer jobs written in R programming and MapReduce job executed from R. This connector also used to tests the MapReduce jobs.

Conclusion:

Hadoop and R working together is a best tool for big data professionals with high performance and scalability. Hadoop integration with R built for overcome limitations of R programming but if we just ignore it, then R and Hadoop together can make big data analytics an ecstasy!

Reference - Hadoop Integration