Business Big Data Analytics With R And Hadoop Download When people talk about big data analytics and Hadoop, they think about using technologies like Pig, Hive, and Impala as the core tools for data analysis.
However, if you discuss these tools with data scientists or data analysts, they say that their primary and favourite tool when working with big data sources and
Hadoop, is the open source statistical modelling language – R. R programming language is the preferred choice amongst data analysts and data scientists because of its rich ecosystem catering to the essential ingredients of a big data project- data preparation, analysis and correlation tasks.
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R and Hadoop were not natural friends but with the advent of novel packages like Rhadoop, RHIVE, and RHIPE- the two seemingly different technologies, complement each other for big data analytics and visualization.
Hadoop is the go-to big data technology for storing large quantities of data at economical costs and R programming language is the go-to data science tool for statistical data analysis and visualization.
R and Hadoop combined together prove to be an incomparable data crunching tool for some serious big data analytics for business.
Most Hadoop users, often pose this question – “What is the best way to integrate R and Hadoop together for big data analytics.” The answer to this depends on various factors like size of the dataset, skills, budget, governance limitations, etc.
This post summarizes the various ways to use R and Hadoop together to perform big data analytics for achieving scalability, stability and speed.
Why use R on Hadoop?
R is an amazing data science programming tool to run statistical data analysis on models and translating the results of analysis into colourful graphics.
There is no doubt that R is the most preferred programming tool for statisticians, data scientists, data analysts and data architects but it falls short when working with large datasets.
Analytical Power of R + Storage and Processing Power of Hadoop =Ideal Solution for Big Data Analytics
One major drawback with R programming language is that all objects are loaded into the main memory of a single machine. Large datasets of size petabytes cannot be loaded into the RAM memory; this is when Hadoop integrated with R language, is an ideal solution.
To adapt to the in-memory, single machine limitation of R programming language, data scientists have to limit their data analysis to a sample of data from the large data set.
This limitation of R programming language comes as a major hindrance when dealing with big data. Since, R is not very scalable, the core R engine can process only limited amount of data.
To the contrary, distributed processing frameworks like Hadoop are scalable for complex operations and tasks on large datasets (petabyte range) but do not have strong statistical analytical capabilities.
As Hadoop is a popular framework for big data processing, integrating R with Hadoop is the next logical step. Using R on Hadoop will provide highly scalable data analytics platform which can be scaled depending on the size of the dataset.
Integrating Hadoop with R lets data scientists run R in parallel on large dataset as none of the data science libraries in R language will work on a dataset that is larger than its memory.
Big Data analytics with R and Hadoop competes with the cost value return offered by commodity hardware cluster for vertical scaling.
R For Big Data
R finds the following applications in the field of big data:
- R can be used for the purpose of exploratory data analysis. The term exploratory data analysis was minted in the field of data analysis using R. Exploratory data analysis is an approach that involves several techniques such as the identification and extraction of important variables from data, testing of underlying assumptions, and drawing insights from the datasets. R may be used to perform both simple and complex mathematical calculations and statistical analysis on various data objects.
- Data visualization is made simple with R since it provides several inbuilt plotting commands that help create simple and complex graphs. The package ggplot2 allows users to add, remove or alter components to a plot and provides a coherent system for building graphs. R makes data visualization and data representation very easy and attractive with its graphic libraries. R provides support for many forms of graphic representations varying from concise charts to interactive graphic capabilities. It is said to be one of the most versatile data visualization packages.
- In the finance and banking sectors, R is used for fraud detection. It is also used to help in reducing customer churn rates based on customer data analysis. Future business decisions can be made using the results of data analysis performed using R.
- In the field of bioinformatics, R is used to analyze strands of genetic sequences and identify patterns in genomes. R is used in performing drug discovery and also finds applications in the field of computational neuroscience.
- Analysts in social media companies use R to identify potential customers through targeted online advertising. Developers in social media companies use R to perform behavior and sentiment analysis to generate recommendation engines and keep customers engaged.
- R makes data visualization and data representation very easy and attractive with its graphic libraries. R provides support for many forms of graphic representations varying from concise charts to interactive graphic capabilities. It is said to be one of the most versatile data visualization packages.
- R has the ability to handle structured and unstructured data and can be integrated with multiple formats of data storage. R provides a variety of tools, including Oracle, Open Database Connectivity Protocol, and RmySQL, which allow it to interface with databases. There is also an extensive library of tools that can be utilized for database manipulation and wrangling.
- R is able to seamlessly integrate with some data processing technologies such as Apache Hadoop and Apache Spark. Spark clusters can be used to remotely process large datasets using R. R and Hadoop work well together where Hadoop’s large scale data processing ability along with its distributed computing capabilities go well with R’s statistical computing abilities.
Methods of Integrating R and Hadoop Together
Data analysts or data scientists working with Hadoop might have R packages or R scripts that they use for data processing.
To use these R scripts or R packages with Hadoop, they need to rewrite these R scripts in Java programming language or any other language that implements Hadoop MapReduce.
This is a burdensome process and could lead to unwanted errors. To integrate Hadoop with R programming language, we need to use a software that already is written for R language with the data being stored on the distributed storage Hadoop.
There are many solutions for using R language to perform large computations but all these solutions require that the data be loaded into the memory before it is distributed to the computing nodes.
This is not an ideal solution for large datasets. Here are some commonly used methods to integrate Hadoop with R to make the best use of the analytical capabilities of R for large datasets.
R And Hadoop Streaming
Hadoop Streaming API allows users to run Hadoop MapReduce jobs with any executable script that reads data from standard input and writes data to standard output as mapper or reducer.
Thus, Hadoop Streaming API can be used along R programming scripts in the map or reduce phases. This method to integrate R, Hadoop does not require any client side integration because streaming jobs are launched through
Hadoop command line MapReduce jobs submitted undergo data transformation through UNIX standard streams and serialization to ensure Java complaint input to Hadoop, irrespective of the language of the input script provided by the programmer.
The below syntax can be used to run MapReduce codes written in R for data processing using the Hadoop MapReduce framework.
$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar -input InputDirLocation \ -output OutputDirLocation \ -mapper /bin/cat \ -reducer /usr/bin/wc
- InputDirLocation – location of input directory for map function
- OutputDirLocation – location of output directory for reduce function
- /bin/cat \ – the R executable script for map function
- /usr/bin/wc – the R executable script for reduce function
Hadoop Streaming works in the following manner:
- The executables to the mapper and reducer functions are scripts that read the input from stdin line-by-line and generate the output to stdout.
- Hadoop Streaming creates a Map/Reduce job and submits it to a cluster, meanwhile monitoring the job progress until it gets completed.
- Each mapper task launches the R script specified for the mappers as a separate process when the mapper is initialized.
- The mapper task takes the input as key-value pairs and converts it into lines, and then pushes these transformed lines as the standard input to the process. The mapper collects the outputs from the standard output, which are now line-oriented and converts them to key-value pairs. The key-value pairs are collected as the result of the mapper.
- Each reducer task launches the R reducer script specified as a separate process when the reducer gets initialized.
- The reducer runs, taking the input key-value pairs and converting them into lines. The lines then get fed to the standard input of the process.
The number of open source options for performing big data analytics with R and Hadoop is continuously expanding but for simple Hadoop MapReduce jobs, R and Hadoop Streaming still proves to be the best solution.
The combination of R and Hadoop together is a must have toolkit for professionals working with big data to create fast, predictive analytics combined with performance, scalability and flexibility you need.
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Most Hadoop users claim that the advantage of using R programming language is its exhaustive list of data science libraries for statistics and data visualization.
However, the data science libraries in R language are non-distributed in nature which makes data retrieval a time consuming affair.
However, this is an in-built limitation of R programming language, but if we just ignore it, then R and Hadoop together can make big data analytics an ecstasy!
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