International Research Journal of Education and Technology

In the current digital age, data serves as the cornerstone around which businesses develop their plans, streamline processes, and spur innovation. Big Data is the term used to describe the explosion of data brought about by the internet, social media, connected devices, and other digital activities growing at an exponential rate. It is difficult for conventional databases and processing methods to handle this data since it is produced at a rapid rate, in a variety of formats, and on an unprecedented scale. Effectively storing, processing, and analysing this enormous volume of data gives businesses a major competitive edge since data-driven insights facilitate better consumer experiences, predictive analytics, and more informed decision-making.

Nevertheless, utilising traditional processing techniques to handle such massive datasets is impractical and ineffective. The demands of real-time data analysis are too great for sequential processing, and traditional databases have trouble scaling. The creation of distributed computing models—in which tasks are broken down into smaller subtasks and carried out concurrently across several machines—was prompted by this difficulty. One of the most influential solutions in this area is the MapReduce programming model from Google. MapReduce divides work into two main stages: Map and Reduce. This method offers a scalable, fault-tolerant, and effective way to process large datasets. Smaller pieces of data are separated during the Map phase and processed concurrently by various nodes in a distributed network.