Which process helps in analyzing large datasets by distributing the workload among multiple systems?

Grid computing is the correct choice for analyzing large datasets by distributing the workload among multiple systems. This computing model allows for the pooling of resources from various locations to work on a single problem. Each participating computer contributes its processing power, storage, or data, effectively allowing tasks to be handled simultaneously across different machines.

This approach enhances the efficiency of data processing, particularly useful in scenarios such as scientific research, data analysis, and complex simulations where large datasets can be processed faster when the workload is divided among several systems.

Cloud computing, while it can also distribute tasks, primarily focuses on providing scalable resources and services over the Internet. It allows users to access computing resources on-demand, but it does not specifically target the distribution of workloads across multiple systems for data analysis in the same way grid computing does.

Quantum computing represents a different paradigm altogether, utilizing the principles of quantum mechanics to perform computations that would be practically impossible for classical computers. It's not inherently designed for workload distribution across multiple systems in the same manner as grid computing.

Edge computing deals with processing data at the edge of the network, close to the source of data generation. While it can improve response times and reduce bandwidth usage by processing data locally, it does not primarily focus on the distribution of large datasets across