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Disadvantages Of Big Data

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Internet is the major source which has resulted in the tsunami of data in the past few years. Big data is too big, it moves too fast, and doesn’t fit the structures of our existing database architectures. It is like an ocean of data in which we people swim in every day with an effort to come on the surface, but every day the level of data increases tremendously. Gone are the days when memory was used to be measured in Gigabytes or Terabytes or Petabytes, today it is measured in exabytes, zettabytes or yottabytes. With Big Data solutions, organizations can dive into all data and gain valuable insights that were previously unimaginable. The term “big data” can be pretty nebulous, in the same way that the term “cloud” covers diverse technologies. …show more content…
ISSUES RELATED WITH BIG DATA CHARACTERISTICS
Data Volume – It refers to the enormous amount of data that is been created each second, each minute and each hour of the day. 571 websites are created in a single minute. Total of 625000 GB of data is transferred from one end to another in single internet minute, may be terms of mails, pictures, posts etc. If we burn the amount of data present on planet earth today on DVDs and pile them in the form of a stack one upon another, the pile will be such huge that one can climb it and touch the moon, come back to earth and again repeat this process once.
Data Velocity – Data is being created at such high velocity that companies are finding it difficult to cope up with such high speed. They have to establish their infrastructure in such a manner that it is capable of handling such generated data Social media, E-Commerce has rapidly increased the speed and richness of data used for different business transactions.
Data Variety - All the data being generated is totally diverse consisting of raw, structured, semi structured and even unstructured data which is difficult to be handled by the existing traditional analytic systems. Mismatched data formats and data structures represent significant challenges that can lead to analytic …show more content…
It is not created from a vacuum. Different scientific experiments being carried out in the world today produces petabytes of data per day. Much of this data is of no use and has to be filtered out. The first challenge faced is to set filtering parameters as such that useful data doesn’t gets discarded. For example, suppose one sensor reading differs substantially from the rest: it is likely to be due to the sensor being faulty, but how can we be sure that it is not an artifact that deserves attention? We need research in the science of data reduction that can intelligently process this raw data to a size that its users can handle while not missing the needle in the haystack. The second challenge encountered is related to automatically generating right metadata to illustrate what data is recorded, how it is recorded and

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