Introduction to MapReduce

Using MapReduce Functionality to Process Data

MapReduce information is generally associated with Google. But does this technology have just one implementation. Of course not! MapReduce can be used in various other ways. People have this believe that MapReduce is licensed by Google so its authority is entirely in its hand, which is actually not the case. The fact is that there are many independent MapReduce projects going on all over the world, some of them are working under commercial vendors, some of them from open source activities. This arrangement is same as that of relational database where SQL or MySQL are used together, even though from two different mediums.

Since this MapReduce function is developed for handling Bigdata sets, this idea has great significance. This idea is well understood and accepted in the industry and developers are using it quite openly for their benefit. As a programmer now you don't need to understand the internals of parallel processing. The MapReduce information floating over the web has proved that there are large numbers of projects which are running with the creation of libraries, protocols, codes etc. Due to all these facilities large chunk of data can be easily submitted and can be easily analyzed by the computers in order to find a better solution for future. Moreover, using the MapReduce technologies you cane easily get the most effective and accurate results fairly fast. This is indeed the need of the hour. Every business needs accurate figures, proper analysis and fast results for their business development and growth.

If you want an efficient system for managing your large data sets, read a lot on Hadoop tutorials online and learn how they can be used in your running system to bring the best results. One thing to be aware while choosing a solution for your business - Not all large volumes of data or analysis are eminently suitable on MapReduce platform. It is very important to know here that these large data sets if can be broken into smaller parallel chunks can only be analyzed. As the smaller chunks can best fit in the MapReduce system, thus you should choose a function after proper analysis. Why to do wasteful investment?

If you need any further information on MapReduce and its benefits you should read online. Here you will find the best reasons and the most appropriate solutions for all your data management and other related issues. You will come to know the situations where relational database system succeeds and where MapReduce fails.

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