With the drastic development of computing technologies, there is an ever-increasing trend in the growth of data. Data scientists are overwhelmed with such a large and ever-increasing amount of data, as this now requires more processing channels. The big concern arising here for large-scale data is to provide support for the decision making process. Here in this study, the MapReduce programming model is applied, an associated implementation introduced by Google. This programming model involves the computation of two functions; Map and Reduce. The MapReduce libraries automatically parallelize the computation and handle complex tasks including big data distribution, loads and fault tolerance. This MapReduce implementation with the source formation of Google and the open-source mechanism, Hadoop has an objective of handling computation of large clusters of commodities. Our implication of MapReduce and Hadoop framework is aimed at discussing terabytes and petabytes of storage with thousands of machines parallel to every machine and process at identical times. This way, large processing and manipulation of big data are maintained with effective result orientations. This study will show up the basics of MapReduce programming and open-source Hadoop structure application. The Hadoop system can speed up the handling of big data and respond very fast.