What is MapReduce?
MapReduce is a programming paradigm that allows us to perform distributed and parallel processing on large data sets in a distributed environment.
MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks and processing them in parallel on commodity servers. In the end, it aggregates all the data from multiple servers to return a consolidated output back to the application.
MapReduce program works in two phases: Map and Reduce. Map tasks deal with splitting and mapping data, while Reduce tasks shuffle and reduce the data. The input to each phase is key-value pairs, which are then sorted out by the map and reduce functions.
MapReduce programming offers several benefits to help gain valuable insights from big data:
- Scalability - Businesses can process petabytes of data
- Flexibility - It enables easier access to multiple sources of data and multiple types of data
- Speed - With parallel processing and minimal data movement, it offers fast processing of massive amounts of data
- Simple - Developers can write code in a choice of languages, including Java, C++, and Python
