Distributed data processing

Distributed data processing refers to the practice of managing and analyzing large volumes of data across multiple machines or nodes in a network. This approach divides the data and processing tasks among several computing units, which can work concurrently, improving efficiency and speeding up processing times compared to traditional, centralized data processing methods. Key features of distributed data processing include: 1. **Scalability**: Systems can easily scale horizontally by adding more nodes to handle larger datasets or increased workloads.