The choice of LANGUAGE for a particular Big Data project depends on the kind of SOLUTION we want to develop. For example, if we want to do data MANIPULATION, certain languages are good at the manipulation of data.

If we are looking for Big Data ANALYTICS, we see another set of languages that should be preferred.

As far as R and Python are concerned, both of these languages are preferred choices for Big Data. When we are looking into the visualization aspect of Big Data, R language is preferred as it is rich in tools and libraries related to graphics capabilities.

When we are into Big Data development, Model BUILDING, and testing, we choose Python.

R is more favourite among statisticians whereas developers prefer Python.

Next, we have Java as a popular language in the Big Data environment as the most preferred Big Data platform ‘Hadoop’ itself is written in java. There are other languages also popular such as Scala, SAS, and MATLAB.

There is also a community of Big Data people who prefer to use both R and Python. So we see that there are ways we can use a combination of both of these languages such as PypeR, PyRserve, rPython, rJython, PythonInR etc.

Thus, it is up to you to decide which one or a combination will be the best choice for your Big Data project.