Apache Hadoop


                                                   By Sreedevi Nair

 In todays world we find data everywhere, its like the air we breath surrounded all around us. The data volumes are growing at a fast rate. This fast growth calls for the need of a strategy to process and analyse the growing data. Hadoop Map Reduce method has proved to be a powerful Computation Model. Hadoop HDFS another Big Data method has become quite popular for its open source features with flexible scalability, less total cost of ownership and allows data stores without the need to have data types or schemas defined.

Data intensive problems have become quite prevalent in organisations of numerous industries. Analyzing and processing large volumes of data becomes quite tedious. Hadoop Implementation has helped to overcome the hurdles in large scale data intensive computing.

It was estimated that the total volume of digital data produced worldwide in 2011 was already around 1.8 zettabytes (one zettabyte equal to one billion terabytes) compared to 0.18 zettabytes in 2006. Data has been generating in an explosive way. Back in 2009, Facebook already hosted 2.5 petabytes of user data growing at about 15 terabytes per day.

Although Hadoop has become the most prevalent data management framework for the processing of large volumes of data in clouds, there exist issues with the Hadoop scheduler that can seriously degrade the performanace of Hadoop running in clouds.

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