The act of processing data, which requires a lot of memory consumption, needs many physical machines to enable it. That is not always possible in the real world. Even the largest data centers in the early 2000s, despite having huge machine setups, still had the question of how the data could be split across these machines to conduct computation.

In today’s data-driven world, algorithms and applications are constantly collecting data, resulting in huge volumes of data. The big challenge is how to process this massive amount of data with speed and efficiency, and without sacrificing meaningful insights. To facilitate this, a programming model named MapReduce was initiated.