Introduction:

More and more companies are acknowledging the importance of Big Data as a source to gain insights and make informed decisions in the company. Data Analytic specialists who can define Big Data, uncover hidden patterns, spot opportunities, and create insights for the betterment of a business are more likely needed by the companies.

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First of all, One who is willing to start a career in Big Data Analytics must have a clear idea in some terminologies such as Big Data, Hadoop and R Programming.

Big Data:

Big Data is a collection of large amounts of both structured and unstructured data that is generated from a variety of sources. Currently, the size of generated data per day on the Internet has already exceeded two exabytes, That’s why Big Data became one of the most currently demanded technology in the development and supplement of enterprise software in the IT industry.

Hadoop:

Hadoop is the most popular Big Data framework used nowadays. It is an open source code technology managed by the Apache Software Foundation. Hadoop is used for reliable, scalable, distributed calculations, but it can also be exploited as common-purpose file storage that can store petabytes of data which is generated in the businesses. Generally speaking, Hadoop can store and process many petabytes of information.

Hadoop Consists of Four Main Modules:

• Distributed File System – Distributed File System also known as Hadoop Distributed File System, it enables storing data across a network of linked storage devices
• MapReduce – MapReduce task is to read, transform and analyse data from the database
• Hadoop Common – Hadoop Common is a set of tools and libraries which complement other modules and ensure compatibility with user’s computer systems
• YARN – YARNis a clusters system manager.

R- Programming:

R is a very popular alternative for the domain of data science technology. R is a programming language and software environment used for statistical analysis, graphics representation and reporting in the industry. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently  further developed by the R Development Core Team. R is freely available under the GNU (General Public License), and pre-compiled binary versions are provided for various OS like Linux, Windows and Mac. Though R Programming is a tool inclined more towards data visualization rather than towards the aspect of deployment of datasets for machine learning models. R Programming is still one of the most actively used powerful languages and it offers powerful model interpretability and reliable community support in the IT industry.

Big Data Analytics With R And Hadoop:

Hadoop is a  Java-based programming framework that supports the processing of large data sets in a distributed computing environment, whereas R is a programming language and software environment for statistical computing and graphics. The R  Programming language is widely used by statisticians and data miners for developing statistical software and performing data analysis in the industry. In the areas of interactive data analysis, general purpose statistics and predictive modelling, R programming has gained massive popularity due to its classification, clustering and ranking capabilities in the IT industry. Hadoop and R complement each other quite well in terms of visualization and analytics of big data where both are considered to be important.

Using R and Hadoop :

There are four ways of using Hadoop and R together and they are as follows:

1. R Hadoop:

RHadoop is a collection of three R packages that includes rmr, rhdfs and rhbase. rmr package provides Hadoop MapReduce functionality in R programming, rhdfs provides HDFS file management in R programming and rhbase provides HBase database management from within R programming Each of these primary packages can be used to analyze and manage Hadoop framework data better in the industry.

2. ORCH:

ORCH stands for Oracle R Connector for Hadoop which is a collection of R packages that provide the relevant interfaces to work with Hive tables. It also helps to work with the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables. ORCH also provides predictive analytic techniques that can be applied to data in Hadoop Distributed File System.

3. RHIPE:

RHIPE is one of the R packages which provides an API to use Hadoop. RHIPE stands for R and Hadoop Integrated Programming Environment which is essentially RHadoop with a different API.

4. Hadoop Streaming:

Hadoop Streaming is an utility which allows users to create and run jobs with any executables as the mapper or as the reducer. Using the  Hadoop streaming system, it is easy to develop working Hadoop jobs with just enough knowledge of Java to write two shell scripts. The combination of R programming and Hadoop file system is emerging as a must-have toolkit for people working with statistics and large data sets in the industry. However, certain Hadoop system enthusiasts have raised a red flag while dealing with extremely large Big Data fragments that generated in their business. They claim that the advantage of R is not its syntax but the exhaustive library of primitives available for visualization and statistics. The libraries in hadoop streaming are fundamentally non-distributed, making data retrieval a time-consuming affair. This is an inherent flaw with R programming, and if you choose to overlook it, R programming and Hadoop in tandem can still work better.

Conclusion:

Since Data Analytics is growing rapidly Hadoop and R programming language will be the key factor of growing. If you check some companies or industries that you would like to work for and then see how much they are looking for well versed candidates in R for Hadoop .You should prioritize your learning to upgrade your skills so join big data analytics courses in chennai. Big data analytics training and placement in chennai will make your career bright and teach you the skills which are required in the industry.