R Hadoop

R Hadoop, also known as RHadoop, is an integration that allows users to combine the power of the R programming language with the capabilities of the Hadoop ecosystem for processing large-scale data. It provides a bridge between R and Hadoop, enabling data scientists and analysts to work with big data using familiar R tools and libraries. Here are some key aspects of R Hadoop:

1. R Programming Language:

   • R is a popular programming language and environment for statistical computing and data analysis.
   • R provides a wide range of statistical and data manipulation functions, making it a favorite among data scientists and statisticians.

2. Hadoop Ecosystem:

   • The Hadoop ecosystem includes distributed storage (HDFS) and processing (MapReduce, Spark, etc.) components for big data.
   • Hadoop is designed for handling large volumes of data in a distributed and fault-tolerant manner.

3. Integration with R Hadoop:

   • RHadoop is not a single tool but a collection of R packages that interface with different Hadoop components.
   • The primary packages in the RHadoop ecosystem include:
     • rhdfs: Allows R to interact with HDFS (Hadoop Distributed File System), enabling file operations.
     • rmr2 (Revolution R): Provides an interface for writing MapReduce jobs in R.
     • plyrmr: Simplifies the writing of MapReduce jobs by providing high-level abstractions.

4. Benefits of R Hadoop:

   • Allows R users to leverage Hadoop’s scalability and distributed processing capabilities for analyzing large datasets.
   • Users can write R code to perform data manipulations and statistical analysis on data stored in HDFS.
   • RHadoop facilitates the integration of R-based analytics into Hadoop workflows.

5. MapReduce with R:

   • RHadoop’s rmr2 package enables R users to write MapReduce jobs in R, which can be executed on a Hadoop cluster.
   • This allows you to distribute R computations across the cluster, making it feasible to process big data at scale.

6. Hive and Pig Integration:

   • RHadoop also integrates with Hive and Pig, two query languages commonly used in the Hadoop ecosystem.
   • Users can run HiveQL or Pig Latin scripts from R and import the results back into R for further analysis.

7. Limitations:

   • While RHadoop provides R users with the benefits of Hadoop, it may not always be as efficient as writing MapReduce code in native Java or using other Hadoop ecosystem tools directly.
   • RHadoop may have a steeper learning curve for those not familiar with Hadoop.

8. Alternatives:

   • In addition to RHadoop, other options for R users in the big data space include Apache Spark with SparkR and R packages for interfacing with databases and distributed computing platforms.