MapReduce works in two main phases: the Map phase and the Reduce phase. During the Map phase, the input data is divided into smaller sub-problems, which are processed in parallel across different nodes. Each node processes its chunk of data and produces intermediate key-value pairs. In the Reduce phase, these key-value pairs are aggregated and processed to generate the final output. This parallel processing capability is particularly beneficial for nanotechnology research, where data sets are often too large to be handled by a single machine.
