Everything You Need to Know About R, Hadoop, and How They Work Together
When individuals talk about big data analysis and Hadoop, the names that pop up in their minds are Pig, Impala, and Hive as the core data analysis tools. However, if you examine these tools with experts or data scientists, they state that their main and favorite tool when dealing with Hadoop and big data sources is the statistical modeling language – R. Talking about R, it is one of the preferred programming languages among data scientists and data analysts catering to the basic elements of a big data project-data training, correlation and analysis tasks.
R and Hadoop, as we all know, were not born as friends but rather with the appearance of packages like Rhadoop, RHIPE, and RHIVE, the two apparently different technologies, supplement each other for visualization and big data analysis. Hadoop is the ultimate big data technology for storing large amounts of data at prudent expenses and R programming language is the ultimate data science tool for statistical modeling, data analysis, and visualization. Combined together, Hadoop and R prove to be an incomparable data-crunching tool for big data analysis for business.
A vast majority of Hadoop users, regularly offer this question as a conversation starter – "What is the most ideal approach to integrate Hadoop and R together for big data analysis." The answer to this relies upon various factors like the size of the dataset, abilities, governance limitations, your budget, and so on. In this blog, we summarize the various approaches to integrate Hadoop and R together to perform big data analysis for accomplishing adaptability, dependability, and speed.
As the demand for the data analysis field rises, it is extremely necessary to scale this integration process. R is data analysis, statistical computing, and visualization tool while Hadoop is a big data framework. The graphical abilities of R language are praiseworthy and it is additionally profoundly extensible alongside object-oriented features. Integrating R with Hadoop can be broadly used for data analysis, visualization, statistics, and predictive modeling.
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R and Hadoop Integration Methods:
The four different methods of integrating R and Hadoop are:
1. RHadoop
RHadoop is an assortment of three R packages: rhdfs, rmr, and rhbase. The rmr package is responsible for providing Hadoop MapReduce functionality, rhdfs is responsible for providing HDFS file management while rhbase is responsible for providing HBase database management in R. Every one of these basic packages can be utilized to manage and analyze Hadoop framework data in a better way.
2. ORCH
The term ORCH represents Oracle R Connector for Hadoop. ORCH is an assortment of R packages that provide the relevant interfaces to work with the Apache Hadoop process infrastructure, Hive tables, Oracle database tables, and the local R environment. Furthermore, ORCH likewise provides predictive analytic methods that can be applied to data in HDFS documents.
3. RHIPE
The term RHIPE represents R and Hadoop Integrated Programming Environment and is basically RHadoop with a different API. RHIPE is an R package that is responsible for providing an API to use Hadoop.
4. Hadoop streaming
Hadoop Streaming permits users for creating and running tasks with any executables as the mapper and/or as the reducer. Utilizing the streaming framework, one can create a working Hadoop task with simply enough information to write two shell scripts on Java that work as the couple.
As we have looked at the integration strategies, let’s dive deep into each strategy to take a closer look.
The RHadoop is a 3 package collection – Compact and powerful. Let’s have a look at the functionalities of this package collection:
- rmr package is responsible for providing MapReduce functionality to the Hadoop framework, this is done by writing codes for mapping and reducing
- rhbase package is responsible for database management functionality by integrating it with HBase
- rhdfs package is responsible for the file management functionality by integrating it with HDFS.
The Oracle R Connector for Hadoop is utilized for deploying R on Oracle Big Data Appliance or for non-Oracle frameworks like Hadoop without any difficulty. The ORCH allows you to get to the Hadoop cluster using R and furthermore to write code for the Mapping and Reducing functions. ORCH also allows you to control the data residing in the HDFS.
The RHIPE allows you to work with Hadoop and R integrated programming environment. Programming languages like Java, Python, or Perl can be used to read data sets in RHIPE. Several functionalities in RHIPE lets you to interact with the Hadoop Distributed File System (HDFS). In this manner, you can read, save that are created utilizing RHIPE MapReduce.
The Hadoop Streaming allows the user to write MapReduce codes in the R language. Java may be the local language for MapReduce however it isn't appropriate for rapid data analysis needs for the modern era and henceforth there is a requirement for quicker mapping and reducing ventures with Hadoop and that’s where Hadoop streaming takes over, you can write the codes in Perl, Python, or even Ruby.
This unique yet powerful combo of R and Hadoop is emerging as an absolute necessity to have a toolbox for individuals working with statistics and big data sets. Although, certain Hadoop lovers have raised a red banner while managing extremely large Big Data fragments. Hadoop lovers guarantee that the upside of R isn't its syntactical structure however the comprehensive library of primitives for statistics and visualization. These libraries make data retrieval a tedious affair. This is an inherent blemish with R, and if you decide to overlook it, R and Hadoop combination can, in any case, work wonders. The number of open-source choices for performing big data analysis with R and Hadoop is persistently growing yet for basic Hadoop MapReduce jobs, R and Hadoop Streaming despite everything proves to be the best solution. The blend of R and Hadoop together is an unquestionable requirement to have a toolbox for professionals working with big data to create quick, predictive analysis joined with performance, adaptability, and speed you need.
Are you thinking to get your hands on Hadoop and R? Well we’ve got you covered here: You can enroll yourself in programs that offer R and Hadoop training online to never look back.
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