1. R and Hadoop Integration
In this blog, we will do a study R and Hadoop Integration. Also, will learn when to use R and Hadoop combination. Moreover, will study the implementation of R integration with Hadoop. I recommend you to go through Hadoop and R Programming So lets start with integrating r and hadoop for big data analysis.
R and Hadoop Integration | R Integration with Hadoop
2. Introduction to R With Hadoop Integration
a. Introduction to R Programming Language
R is an open source programming language. It is best suitable for statistical and graphical analysis. Also, if we are in need of strong data analytics and visualization features. Then its need to combine R with Hadoop.
b. Introduction to Hadoop
Hadoop is an open-source tool. It is provided by the ASF – Apache Software Foundation. Also, it’s Open source project. That means it is freely available and one can change its source code as per the requirements. Although, if the certain functionality does not fulfill your need. Then you can change it according to your need. Moreover, it provides an efficient framework for running jobs.
3. R and Hadoop Integration Purpose
- Use Hadoop to execute R code
- Use R to access data stored in Hadoop
4. R and Hadoop Integration Methods
There are 4 types of methods for Integrating R with Hadoop
R Hadoop Integration – Methods
a. R Hadoop
The R Hadoop is a collection of 3 packages. Here, we will discuss functionalities of packages.
i. The rmr package
It provides the MapReduce functionality to the Hadoop framework. Also, it provides functionalities by writing the Mapping and Reducing codes in R.
ii. The rhbase package
It will give you the R database management capability with integration with HBase.
iii. The rhdfs package
It’s the file management capabilities by integration with HDFS
b. Hadoop Streaming
It’s R database management capability with integration with HBase
. Hadoop Streaming is the R Script available as part of the R package
on CRAN. Also, this intends to make R more accessible to Hadoop streaming applications. Moreover, using this you can write MapReduce programs in a language other than Java
It involves writing MapReduce codes in R Language
. That makes it extremely user-friendly
. As JAVA
is the native language for MapReduce. But according to today’s need, it doesn’t suit high-speed data analysis. Thus, in toady’s we need faster mapping and reducing steps with Hadoop. Hence, Hadoop streaming in demand and use. As we can write the codes in Python
, Perl or even Ruby.
This is an integrated programming environment which was developed by the Divide and Recombine (D & R) for analyzing large amounts of data. As RHIPE stands for R and Hadoop Integrated Programming Environment.
It involves working with R and Hadoop integrated programming environment. Also, one can use Python
, Java or Perl to read data sets in RHIPE. Moreover, there are various functions in RHIPE that lets you interact with HDFS
. Thus, this way you can read, save that are created
using RHIPE MapReduce.
It is called as Oracle R Connector. Also, it can be used to exclusively work with Big Data in Oracle appliance. Also, on a non-Oracle framework like Hadoop.
It helps in accessing the Hadoop cluster via R and also to write the Mapping and Reducing functions. Also, one can manipulate the data residing in the Hadoop Distributed File System
5. Conclusion: R Integration with Hadoop
As a result, we have studied R and Hadoop integration. Also, learned different ways of integration of R with Hadoop. Thus, this will help you to understand how R is used and integrated Hadoop and with other. Furthermore, if you have any query, you can ask in a comment section.