Using data to answer interesting questions is what researchers are busy doing in today’s data driven world. Given huge volumes of data, the challenge of processing and analyzing it is a big one; particularly for statisticians or data analysts who do not have the time to invest in learning business intelligence platforms or technologies provided by Hadoop eco-system, Spark, or NoSQL databases that would help them to analyze terabytes of data in minutes.
The norm today is for researchers or statisticians to build their models on subsets of data in analytics packages like R, MATLAB, or Octave, and then give the formulas and data processing steps to IT teams who then build production analytics solutions.
One problem with this approach is that if the researcher realizes something new after running his model on all of the data in production, the process has to be repeated all over again.
What if the researcher could work with a MongoDB developer and run his analysis on all of the production data and use it as his exploratory dataset, without having to learn any new technology or complex programming languages, or even SQL?
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