Python Certification Training for Data Scienc ...
- 60k Enrolled Learners
- Live Class
Hadoop is a disruptive Java-based programming framework that supports the processing of large data sets in a distributed computing environment, while R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and performing data analysis. In the areas of interactive data analysis, general purpose statistics and predictive modelling, R has gained massive popularity due to its classification, clustering and ranking capabilities.
Hadoop and R complement each other quite well in terms of visualization and analytics of big data.
There are four different ways of using Hadoop and R together:
RHadoop is a collection of three R packages: rmr, rhdfs and rhbase. rmr package provides Hadoop MapReduce functionality in R, rhdfs provides HDFS file management in R and rhbase provides HBase database management from within R. Each of these primary packages can be used to analyze and manage Hadoop framework data better.
ORCH stands for Oracle R Connector for Hadoop. It is a collection of R packages that provide the relevant interfaces to work with Hive tables, the Apache Hadoop compute infrastructure, the local R environment, and Oracle database tables. Additionally, ORCH also provides predictive analytic techniques that can be applied to data in HDFS files.
RHIPE is a R package which provides an API to use Hadoop. RHIPE stands for R and Hadoop Integrated Programming Environment, and is essentially RHadoop with a different API.
4. Hadoop streaming
Hadoop Streaming is a utility which allows users to create and run jobs with any executables as the mapper and/or the reducer. Using the streaming system, one can develop working Hadoop jobs with just enough knowledge of Java to write two shell scripts that work in tandem.
The combination of R and Hadoop is emerging as a must-have toolkit for people working with statistics and large data sets. However, certain Hadoop enthusiasts have raised a red flag while dealing with extremely large Big Data fragments. They claim that the advantage of R is not its syntax but the exhaustive library of primitives for visualization and statistics. These libraries are fundamentally non-distributed, making data retrieval a time-consuming affair. This is an inherent flaw with R, and if you choose to overlook it, R and Hadoop in tandem can still work wonders.
Now, let’s see a demo:
Got a question for us? Please mention them in the comments section and we will get back to you.