Which programming model is used in Big Data processing?

The MapReduce programming model is specifically designed to process and generate large datasets that can be parallelized across a distributed cluster of computers. It works by dividing the data into smaller chunks, processing those chunks in parallel, and combining the results. This model is particularly effective in handling big data because it simplifies the complexity of data processing tasks, making it easier to scale and manage large volumes of data.

MapReduce consists of two primary functions: the Map function, which processes input data and produces intermediate key-value pairs, and the Reduce function, which takes these intermediate key-value pairs and aggregates them to produce the final output. This paradigm allows developers to focus on writing the logic of data processing without worrying about the underlying complexities of distributed computing, such as fault tolerance and task scheduling.

While other options like Grid Computing, Stream Processing, and Event-Driven Programming also have their roles in handling data, they do not specifically describe a programming model designed for processing large datasets in the same structured and efficient way as MapReduce. Each of those methods can be more suitable for specific tasks or types of data handling, but they do not encapsulate the same general-purpose capabilities that make MapReduce a cornerstone in big data processing frameworks, such as Hadoop.