Basic Introduction of Hadoop Map Reduce

An open source java implementation of MapReduce framework, Hadoop is introduced by Google. However, the main developer and contributor of Hadoop is said to be Yahoo, which amazed a lot of people because Yahoo, being one of the major competitors of Google released an open source version of a framework that was introduced by it competitor, Google. Nevertheless, Google has granted patent for it.

One of the major reasons why Yahoo could easily use the technology is because the Map and Reduce functions and features have been known and used in the field of functional programming for a lot of years. This is another major reason why Hadoop Map Reduce has gained a higher popularity as part of the Apache project. Today, numerous companies are using this technology as a significant component in their web architecture.

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The technology is used to simplify the process of data management within organizations. Every organization depends upon its data to function and perform better. However, it is seen that large and complicated data present within the organization increases complications and reduce work productivity. In such situations, the used of Hadoop Ecosystem helps organization manage data better by distributing large data clusters into various small parts.

It is the major and most significant framework for data analysis and processing that sometimes can be presented as an alternative to conventional relational databases. However, it is not a real database even if it does offer no SQL one called H Base as one of its major tools because it is a framework for distributing major data processes.

On the other hand, Map Reduce is a basic programming model that is introduced by Google, which is a significant part of Hadoop. It is based on the use of two major functions taken from basic fundamental programming: Map and Reduce, where Map processes a key pair into a list of intermediate key pairs and Reduce takes an intermediate key and the set of values for that particular key. In this process, the user writes both the mapper and the reducer processes. Hadoop Map framework groups together intermediary values linked with the same key to process them to the equivalent Reduce.

If you feel that by including Hadoop framework you save increase your organizational proficiency and manage data within the organization better, you can find this framework for free anywhere on the net. However, in order to excel in the field and make the best use of this framework, Hadoop training is extremely important.