Distributed processing is a technique used to divide a large set of data or task into smaller pieces that can be processed simultaneously on different machines. This technique is often used to accelerate the execution of tasks that require many calculations, such as analyzing large sets of data or training complex machine learning models.
There are several technologies commonly used in distributed processing. One of these is MapReduce, which is a programming model developed by Google that allows for the parallel processing of large sets of data on multiple machines. MapReduce consists of two main functions: "map," which processes the input data, and "reduce," which combines the output of the map function.
Another technology used in distributed processing is Apache Spark, which is an open-source data processing engine that can handle large sets of data in a distributed manner. Spark can process data in-memory, which makes it faster than other distributed processing technologies.
Other technologies used in distributed processing include Hadoop, which is an open-source framework for storing and processing large sets of data, and Apache Flink, which is a streaming data processing engine.
Overall, distributed processing is a powerful tool for accelerating the execution of tasks that require many calculations, and there are a variety of technologies available for implementing it.
