Process “Big Data” in MATLAB using MapReduce

This step-by-step MATLAB example shows you how to process  #bigdata in  using #MapReduce!

  • The reducer function finishes the computation begun by the mapper function, and outputs the final answer.
  • The “mapper” function computes the maximum of each chunk from the datastore .
  • If the mapper function adds values to multiple keys, this leads to multiple calls to the reducer function, with each call working on only one key’s intermediate values.
  • A “reducer” function that is given the aggregate outputs from the mapper function.
  • Once the mapper and reducer functions are written and saved in your current folder, you can call mapreduce using the datastore , mapper function, and reducer function.

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@MATLAB: “This step-by-step MATLAB example shows you how to process #bigdata in using #MapReduce!”


This example shows how to use the datastore and mapreduce functions to process a large amount of file-based data.


Process “Big Data” in MATLAB using MapReduce

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