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Big Data in Stata

Author

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  • Andrew Maurer

    (Behavioral Research Group, Quantitative Risk Management, UK)

Abstract

With more and more data being stored by organizations across industries – from academia, to health care, to banking – along with plummeting storage and RAM costs, there is a growing need for tools to analyze “big data”. The world is moving from needing to analyze megabytes of data to needing to analyze many gigabytes. While Stata is very user-friendly, many of the most basic commands – summarize, sample, collapse, and encode, etc – are not optimized for speed. These commands – as of Stata 14 – all rely on sorting, making them tens, or even hundreds (in the case of sample), of times slower than what is possible with better algorithms. In this presentation I illustrate alternative algorithms along with coded examples in Stata, Mata, and C++ plugins which may be used to more quickly analyze big data. fastsample and fastcollapse are available from the SSC.

Suggested Citation

  • Andrew Maurer, 2015. "Big Data in Stata," United Kingdom Stata Users' Group Meetings 2015 09, Stata Users Group.
    • Handle: RePEc:boc:usug15:09
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