Big data

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Big data is data that is too large to process using traditional methods.Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead massively parallel software running on tens, hundreds, or even thousands of servers. An example of big data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible. When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive data sets. Big data has the following characteristics: Very large, distributed aggregations of loosely structured data, Petabytes/exabytes of data, Millions/billions of people, Billions/trillions of records, Loosely-structured and often distributed data, Flat schemas with few complex interrelationships, Often involving time-stamped events, Often made up of incomplete data,Often including connections between data elements that must be probabilistically inferred Applications that involved Big-data can be: Transactional (e.g., Facebook, PhotoBox), or, Analytic (e.g., ClickFox, Merced Applications)



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