Big data processing demands massive computational power. In many cases, it expects more power than one machine can handle. This problem has caused engineers to look for new strategies for handling tremendous amounts of data. The advent of cloud computing is making it possible to handle large datasets by applying several networked devices to create vast computing grids.

Unlike conventional parallel processing, this device mesh can share and examine information across several endpoints. Consensus algorithms are then used to optimize the outcomes. The model learns by using past data with the expectation that the device mesh will work better on future data. This method could be employed to develop a smart spam filter, an automated chatbot system, or a bank fraud alert system, for example.