Big Data Virtualization and Visualization: On the Cloud

Significance Of Big Data And Applications

Big data management was initially a question of concern for only the big commercial entities such as Google, Yahoo, Microsoft, big research organizations (such as Nasa which may have to deal with trillions of bytes of satellite imagery, massive capturing of space signals etc), governments and so on. With the recent explosion of internet usage we can see, that more and more devices are being hooked up with the cloud (internet), and there are multiple government, commerical and defence orgranizations which are collecting and/or interested in analysing this big data. According to recent estimates, this data will continue to grow from terabytes into exabytes and possibly even beyond. This data needs to be made available for scientific and commerical needs to the interested parties in addition to its normal use. This can include different user segments such as academia, industry etc. Academic use of big data can result in further research and enablement of big data over cloud, experiments on big data in cloud computing, cloud based big data research, application containers, big data in virtual environments etc. Commercial use of this data can be quite wide ranging such as sentiment analysis, customer behaviour, future trend analysis, pattern mining in big data and the like. This generates a need for a sustainable infrastructure which can hold and maintain big data with opportunities for extended processing, visualization and transformations. Big data has some distinguishing characteristics which makes it unique than traditional data and which are described below in some detail:

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  Volume: Big data is typically in the order of terabytes, peta bytes and even more. It become obvious that such data cannot be stored in any traditional means easily.

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  Value: Big data brings value. It may be holding precious gems of information ready to be mined. This information will remain hidden if it cannot be fully processed and analyzed.

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  Velocity: Big data such as sensors data collected through traffic sensors, home devices sensors, public places sensors, space signals analysis data, flight data of a Boeing 737. It is obvious that such data will be arriving, in fact streaming at a very fast pace. This generates the need to filter the noise out and store the remaining interesting big data quickly.

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  Veracity: The data validity or truthfulness is important nonetheless. The ultimate value to be derived from big data depends solely on the level of veracity in the big data. The big data has to be truly authentic and should contain only minor defects and errors.