Analysis on Sales Data Using Hadoop and MapReduce Algorithm

Abstract

For this project, we developed an extremely flexible and efficient MapReduce method to analyze a big sales dataset. The program's objectives were to find interesting correlations plus tendencies within the sales statistics and provide a strong substitute for in-depth analysis of the data. We created Map and Reduce algorithms that processed the information in a networked & simultaneous manner using the MapReduce framework's distributed processing capabilities. We extended the application to efficiently process enormous datasets by deploying it on the Hadoop platform. The results of the investigation demonstrate the MapReduce framework's effectiveness at managing enormous volumes of information through a dispersed, simultaneous way. By employing the networked computing features of the framework, we were able to swiftly and efficiently analyze the sales data and get meaningful insights about it. The program's great scalability allowed us to quickly and effectively examine large amounts of data, making it a powerful tool enabling big information research.