Big Data analytics. Three use cases with R, Python and Spark
This provides practical guidance for statisticians on tool selection in big data analytics, but it is incremental as it benchmarks existing methods without introducing new techniques.
The article compares the performance of R, Python Scikit-learn, and Spark MLlib on three big data use cases, finding that Spark excels in data munging and collaborative filtering but underperforms in conventional learning methods like logistic regression and random forests compared to non-distributed environments.
Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments: R, Python Scikit-learn, Spark MLlib on three public use cases: character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture