We study the two approaches, rHadoop and H2O, to intergate R, a popular statistical programming environment, into the Hadoop Big Data ecosystem. Using these approaches and the vanilla implementation of MapReduce to implement the solution to an analytic question for the on-time airline performance data set, we evaluate the differences in runtime performance and elaborate on the causes of these differences based on rHadoop and H2O's design principles.