Tuesday, March 15, 2016

The Divergent types of Big Data

In a pursuit to the software industry, big data refers to those data sets that exceed the capabilities of traditional databases. Big data is a kind of collection of divergent data, should be able to adapt to intelligence.  For many big data users, the definition of big data is an acronym for predictive analytics. For few others, the definition of big data is just an impressive amount of 1s and 0s.
The term ‘Big Data' is too general. The few different categories of Data today, are listed below:

 Big Data

Big Data : Such data are the classic predictive analytics problems where you want to unearth trends or push the boundaries of scientific knowledge by the mining of complex huge amount of data. A typical human genome scan generates about 200GB of data and the number of human genomes scanned is doubling every seven months, according to a study conducted by the University of Illinois (And we're not even counting the data from higher-level analyses or the genome scans from the 2.5 million plants and animal species that will be sequenced by then.) By 2025, we will have 40 exabytes of human genomic data or about 400 times more than the 100 petabytes now stored in YouTube. In general, larger the data sets, precise will be the conclusions. Still, the vast scope means rethinking where and how data gets stored and shared.

Fast Data : To seize the velocity of data in real-time is among the most important challenges of the big data. Compute of complex mathematical analytics enhances the accuracy in predicting the data at real-time. Every information or data is expected to process on a figure tip as a FAST data. Business can quickly analyze a consumer's personal preferences as they pause by a store kiosk and dynamically generate a 10% off coupon. Fast Data sets can be high in volume, but the value revolves around being able to deliver it on time. All availability of data, in real time, is generating the need to keep pace with the retrieval and process of the information. Some important data has to be forecasted immediately in real time, for example, the status of vital parameters of a patient at ICU, prediction of weather forecast, data from crucial sensors, an accurate traffic forecast in real-time than a perfect analysis an hour before, mandatory data from installed cameras at railways or airport, to detect telltale signals of intoxication to keep people away from falling onto the tracks or at airbase, etc. Big players of these fields like IBM and Cisco are building and designing their systems keeping these multifaceted properties of data in mind.

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