Data-Intensive Computing With Hadoop

Data-Intensive Computing With Hadoop Introduction:

Data-intensive computing is a class of parallel computing applications which use a data parallel approach to processing large volumes of data typically terabytes or petabytes in size and typically referred to as big data.

Overview Of Data-Intensive Computing With Hadoop Job Support:

The Data intensive computing is collecting, managing, analyzing, &  understanding data at volumes  & rates that push the frontier of the current technologies.The Big Data is the hottest trend in the current business  & IT world right now.

We are living in the age of  the big data where due to the rapid development in the computational power  & the WWW, we are producing an overwhelming amount of data, which has led to the need of an change in the existing architectures & mechanisms of the data processing systems. The Big data- as these large chunks of data is generally called has the redefined the current data processing scenario.

The Data sets of increasing volume & the complexity are often very difficult to process with the standard  HPC or  the DBMS technology. Large-scale data processing is particular popular in the fields of  the linguistics, data mining, machine learning, bioinformatics & the social sciences, but certainly not limited to those disciplines.

The open-source frameworks such as Apache Spark &  Hadoop have been developed with this challenge in mind & can be of great benefit for the data-intensive computing

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