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How to Successfully Integrate R with Hadoop?

How to Successfully Integrate R with Hadoop?
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Hadoop is the most important framework for working with Big Data. The best part of Hadoop is that it is scalable and can be deployed for any type of data in various varieties like structured, unstructured and semi-structured type.

But in order to derive analytics from it, there is a need to extend its capabilities and hence the integration with R programming is a necessity in order to get Big Data analytics. There are various ways in which R can be integrated with Hadoop.

70% of companies say analytics is integral to making decisions – IBM Study

R is a programming language that is used for statistical and graphical analysis. If you are in need of strong data analytics and visualization features then you will be combining the R programming language along with Hadoop. R is highly extensible object oriented programming language and it has strong graphical capabilities.

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Some of the reasons why R is such a good fit for data analytics are as below:

  • It is an interactive language
  • It can be used for graphic applications
  • Statistical programming features
  • Advanced data visualizations
  • The R data structure
  • The various packages available in R
  • The functions can be used as first class objects.

As the requirement of the data analytics field increases there is a real need to scale the process and this is possible using the integration of R with Hadoop. Hadoop is a big data framework while R is a statistical computing, data analytics and visualization tool. The graphical capabilities of R are commendable and it is also highly extensible along with object-oriented features. In its basic form it comes with a Command Line Interpreter.

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So this integration of R with Hadoop can be extensively used for data visualization, analytics, predictive modeling and statistics. The integration of R and Hadoop comes in naturally thanks to the storage capabilities of Hadoop and the analytics features of R. They are pretty much complementary to each other when it comes to Big Data analytical and visualization capabilities.

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19% is annual growth rate of the Analytics market – Pringle & Company

The R language can be used for the Mapper and Reducer functions since it is much easier to code these steps than in Java and also there are lesser lines of code required. This integration is especially beneficial for the data scientists and in the data analysis process.

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Here are the most common ways in which R and Hadoop can be integrated

RHadoop : this is an Open Source package provided by Revolution Analytics. These come with four packages that can be readily used for R analysis and working with Hadoop framework data.

RHIPE : this is an integrated programming environment that is developed by the Divide and Recombine (D & R) for analyzing large amounts of data. RHIPE stands for R and Hadoop Integrated Programming Environment.

ORCH : this is the Oracle R Connector which can be used to exclusively work with Big Data in Oracle appliance or on non-Oracle framework like Hadoop.

HadoopStreaming : this is the R Script available as part of the R package on CRAN. This intends to make R more accessible to Hadoop streaming applications. Using this you can write MapReduce programs in a language other than Java.

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A detailed understanding of 4 integration methods between R and Hadoop:

The RHadoop can be seen as a 3 package collection. Here are the functionalities of these packages:

  • The rmr package is the one that provides the MapReduce functionality to the Hadoop framework by writing the Mapping and Reducing codes in R language
  • The rhbase package lets you get the R database management capability with integration with HBase
  • The rhdfs package lets you get the file management capabilities by integration with HDFS.

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The Hadoop Streaming lets you write MapReduce codes in R language making it extremely user-friendly. Java might be the native language for MapReduce but it is not suited for high speed data analysis needs of today and hence there is a need for faster mapping and reducing steps with Hadoop and this is where Hadoop Streaming comes into the picture wherein you can write the codes in Python, Perl or even Ruby. The Hadoop Streaming then converts the data to how the users need it. When you are doing data analysis using R there is no need to write codes from the command line interface as the data will be sitting on the background and all you need to do is create data, partition data and compute summaries.

The RHIPE lets you work with R and Hadoop integrated programming environment. You can use Python, Java or Perl to read data sets in RHIPE. There are various functions in RHIPE that lets you interact with HDFS. This way you can read, save that are created using RHIPE MapReduce.

The Oracle R Connector for Hadoop can be used for deploying R on Oracle Big Data Appliance or for non-Oracle frameworks like Hadoop with equal ease. The ORCH lets you access the Hadoop cluster via R and also to write the Mapping and Reducing functions. You can also manipulate the data residing in the Hadoop Distributed File System.

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