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R Hadoop Training Hyderabad

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The volume of data that enterprises acquire every day is increasing exponentially. It is now possible to store these vast amounts of information on low cost platforms such as Hadoop.

R hadoop training Hyderabad
The conundrum these organizations now face is what to do with all this data and how to glean key insights from this data. Thus R comes into picture. R is a very amazing tool that makes it a snap to run advanced statistical models on data, translate the derived models into colorful graphs and visualizations, and do a lot more functions related to data science.R Hadoop Training Hyderabad
One key drawback of R, though, is that it is not very scalable. The core R engine can process and work on very limited amount of data. As Hadoop is very popular for Big Data processing, corresponding R with Hadoop for scalability is the next logical step.
Using R with Hadoop will provide an elastic data analytics platform that will scale depending on the size of the dataset to be analyzed. R Hadoop training HyderabadExperienced programmers can then write Map/Reduce modules in R and run it using Hadoop’s parallel processing Map/Reduce mechanism to identify patterns in the dataset.
Until now, R has been used mainly for statistical analysis, but due to the increasing number of functions and packages, it has become popular in several fields, such as machine learning, visualization, and data operations. R will not load all data (Big Data) into machine memory. So, Hadoop can be chosen to load the data as Big Data. Not all algorithms work across Hadoop, and the algorithms are, in general, not Ralgorithms. Despite this, analytics with R have several issues related to large data. In order to analyze the dataset, R Hadoop training HyderabadR loads it into the memory, and if the dataset is large, it will fail with exceptions such as “cannot allocate vector of size x”. Hence, in order to process large datasets, the processing power of R can be vastly magnified by combining it with the power of a Hadoop cluster. Hadoop is very a popular framework that provides such parallel processing capabilities. So, we can use R algorithms or analysis processing over Hadoop clusters to get the work done.

R Hadoop Training Hyderabad

Three ways to link R and Hadoop are as follows:
  • RHadoop
  • Hadoop streaming
Introducing RHIPE
RHIPE stands for R and Hadoop Integrated Programming Environment. As mentioned on, it means “in a moment” in Greek and is a merger of R and Hadoop. It was first developed by Saptarshi Guha for his PhD thesis in the Department of Statistics at Purdue University in 2012. Currently this is carried out by the Department of Statistics team at Purdue University and other active Google discussion groups.
The RHIPE package uses the Divide and Recombine technique to perform dataanalytics over Big Data. In this technique, data is divided into subsets, computation is performed over those subsets by specific R analytics operations, and the output is combined. RHIPE has mainly been designed to accomplish two goals that are as follows:
  • Allowing you to perform in-depth analysis of large as well as small data.R Hadoop Training Hyderabad
  • Allowing users to perform the analytics operations within R using a lower-level language. RHIPE is designed R Hadoop Training Hyderabadwith several functions that help perform Hadoop Distribute File System (HDFS) as well as MapReduce operations using a simple R console.
Understanding the architecture of RHIPE
Let’s understand the working of the RHIPE library package developed to integrate R and Hadoop for effective Big Data analytics.
R Hadoop Training Hyderabad
Therefore, the integration of such data-driven tools and technologies can build a powerful scalable system that has features of both of them.
Components of RHIPE
There are a number of Hadoop components that will be used for data analytics operations with R and Hadoop.
The components of RHIPE are as follows:
  • RClient: RClient is an R application that calls the JobTracker to execute the job with an indication of several MapReduce job resources such as Mapper, Reducer, input format, output format, input file, output file, and other several parameters that can handle the MapReduce jobs with RClient.
  • JobTracker: A JobTracker is the master node of the Hadoop MapReduce operations for initializing and monitoring the MapReduce jobs over the Hadoop cluster.
  • TaskTracker: TaskTracker is a slave node in the Hadoop cluster. It executes the MapReduce jobs as per the orders given by JobTracker, retrieve the input data chunks, and run R-specific Mapper and Reducer over it. Finally, the output will be written on the HDFS directory.
  • HDFS: HDFS is a filesystem distributed over Hadoop clusters with several data nodes. It provides data services for various data operations.

R Hadoop training Hyderabad


If we think about a combined RHadoop system, R will take care of data analysis operations with the preliminary functions, such as data loading, exploration, analysis, and visualization, and Hadoop will take care of parallel data storage as well as computation power against distributed data.

Prior to the advent of affordable Big Data technologies, analysis used to be run on limited datasets on a single machine. Advanced machine learning algorithms are very effective when applied to large datasets, and this is possible only with R Hadoop Training Hyderabadlarge clusters where data can be stored and processed with distributed datastorage systems. In the next section, we will see how R and Hadoop can be installed on different operating systems and the possible ways to link R and Hadoop.
 Let’s see what are the advantages of R and Hadoop integration within an organization. Since statisticians and dataanalysts frequently use the R tool for data exploration as well as data analytics, Hadoop integration is a big boon for processing large-size data. Similarly, dataengineers who use Hadoop tools, such as system, to organize the datawarehouse can perform such logical analytical operations to get informative insights that are actionable by integrating with R tool.
Introducing RHadoop
RHadoop is a collection of three R packages for providing large data operations with an R environment. It was developed by Revolution Analytics, which is the leading commercial provider of software based on R. RHadoop is available with three main R packages: rhdfs, rmr, and rhbase. Each of them offers different Hadoop features.
  • rhdfs is an R interface for providing the HDFS usability from the R console. As Hadoop MapReduce programs write their output on HDFS, it is very easy to access them by calling the rhdfs methods. The R programmer can easily perform read and write operations on distributed data files. Basically, rhdfs package calls the HDFS API in backend to operate data sources stored on HDFS.
  • rmr is an R interface for providing Hadoop MapReduce facility inside the Renvironment. So, the R programmer needs to just divide their application logic into the map and reduce phases and submit it with the rmr methods. After that, rmr calls the Hadoop streaming MapReduce API with several job parameters as input directory, output directory, mapper, reducer, and so on, to perform the R MapReduce job over Hadoop cluster.
  • rhbase is an R interface for operating the Hadoop HBase data source stored at the distributed network via a Thrift server. The rhbase package is designed with several methods for initialization and read/write and table manipulation operations.
Here it’s not necessary to install all of the three RHadoop packages to run the Hadoop MapReduce operations with R and Hadoop. If we have stored our input data source at the HBase data source, we need to install rhbase; else we require rhdfs and rmr packages. As Hadoop is most popular for its two main features, Hadoop MapReduce and HDFS, both of these features will be used within the R console with the help of RHadoop rhdfs and rmr packages. These packages are enough to run Hadoop MapReduce from R. Basically, rhdfsprovides HDFS data operations while rmr provides MapReduce execution operations.
RHadoop also includes another package called quick check, which is designed for debugging the developed MapReduce job defined by the rmrpackage.
In the next section, we will see their architectural relationships as well as their installation steps.
Understanding the architecture of RHadoop
Since Hadoop is highly popular because of HDFS and MapReduce, Revolution Analytics has developed separate R packages, namely, rhdfs, rmr, and rhbase. The architecture of RHadoop is shown in the following diagram:
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