In today’s data-driven market, algorithms and applications are collecting data 24/7 about people, processes, systems, and organizations, resulting in huge volumes of data. The challenge, though, is how to process this massive amount of data with speed and efficiency, and without sacrificing meaningful insights.
This is where the MapReduce programming model comes to rescue. Initially used by Google for analyzing its search results, MapReduce gained massive popularity due to its ability to split and process terabytes of data in parallel, achieving quicker results.