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R is great for machine learning, data visualization and analysis, and some areas of scientific computing.
The June update to Apache Spark brought support for R, a significant enhancement that opens the big data platform to a large audience of new potential users. Support for R in Spark 1.4 also gives users an alternative to Python. But which language will emerge as the winner for doing data science in Spark? We spoke to Databricks Ali Ghodsi for answers.
Dataframes today supports Spark’s machine learning and SQL libraries, and will support the graph database and Spark Streaming libraries in the future. Eventually, Dataframes will be the main way that people interact with Spark, Ghodsi says. “One of the main ways you talk to Spark is Dataframes,” he says. “If you’re using R or if you’re using Python or even if you’re using Scala, there’s a Dataframe way you can speak to Spark.”
The Spark framework is evolving at a fast pace, and one of the most important enhancements was the version 1.3 release of Dataframes, which is essentially a “smashup” of different statistical vectors, according to Ghodsi. It’s interesting that the Dataframes concept was originally developed within R, and the folks behind Spark saw how powerful that approach could be, so they copied it. The Python community also has its version of a Dataframe, which is embodied in the Pandas project. Spark today support both flavors of Dataframes, in R and Python Pandas, as well as Dataframes for Scala.
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