MapReduce is a programming system that is apart of Apache Hadoop.
MapReduce enables scalability across multiple servers in a Hadoop cluster. A Hadoop cluster is a type of computational cluster designed specifically for storing and analyzing huge amounts of unstructured data in a distributed computing environment. It processes data by splitting petabytes of data into smaller chunks, and processes them in parallel on Hadoop commodity servers. In the end, it aggregates all the data from multiple servers to return a consolidated output back to the application.
The term “MapReduce” refers to two separate and distinct tasks that Hadoop programs perform.
- Map Job – where it takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs).
- Reduce Job – combines those data tuples based on the key and modifies the value of the key.
MapReduce programming offers several benefits:
- Scalability – Businesses can process petabytes of data stored in the Hadoop Distributed File System (HDFS).
- 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 large amounts of data.
- Simple – Developers can write code in a choice of languages, including Java, C++ and Python.
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