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Integrating R and Hadoop for Big Data Analysis

Author

Listed:
  • Bogdan Oancea

    (“Nicolae Titulescu” University of Bucharest)

  • Raluca Mariana Dragoescu

    (The Bucharest University of Economic Studies)

Abstract

Analyzing and working with big data could be very difficult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with hundreds or even thousands of computing nodes. Official statistics is increasingly considering big data for deriving new statistics because big data sources could produce more relevant and timely statistics than traditional sources. One of the software tools successfully and wide spread used for storage and processing of big data sets on clusters of commodity hardware is Hadoop. Hadoop framework contains libraries, a distributed file-system (HDFS), a resource-management platform and implements a version of the MapReduce programming model for large scale data processing. In this paper we investigate the possibilities of integrating Hadoop with R which is a popular software used for statistical computing and data visualization. We present three ways of integrating them: R with Streaming, Rhipe and RHadoop and we emphasize the advantages and disadvantages of each solution.

Suggested Citation

  • Bogdan Oancea & Raluca Mariana Dragoescu, 2014. "Integrating R and Hadoop for Big Data Analysis," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 83-94, June.
    • Handle: RePEc:rsr:journl:v:62:y:2014:i:2:p:83-94
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    More about this item

    Keywords

    big data; Hadoop; R; RHadoop; Rhipe; Streaming;
    All these keywords.

    JEL classification:

    • L8 - Industrial Organization - - Industry Studies: Services
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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