Introduction of Big Data

Big Data main pic

Big Data is relatively a newer term in the field of Information Technology and it is defined as the collection of different complex and huge-sized data sets, which cannot be easily stored, captured, analyzed or shared by using the available tools. In big markets, the term Big Data is mostly used for identification of different available data sets.

In simplest form, the term Big Data refers data that is so fast, huge and complex and its almost impossible to calculate or process by using the traditional methods.

There are three components of Big Data, which are commonly known as V’s (Volume, Velocity and Variety). These are as follows:

  1. Volume:

Volume refers to the huge amount of data being stored in the storage medium. This huge amount of data stored to process by using the Big Data applications.

  1. Velocity:

Velocity refers to the lightning speed at which the data streams must be analyzed and processed.

  1. Variety:

The term variety refers to the different forms and sources from which the data is collected and stored in the medium in different medium, such as audio, video, text and images.

In modern days, the different tech companies, including Google and Microsoft has to manage, process, store and visualize a large amount of data each day. The traditional tools aren’t equipped to handle the complexity of managing and storing large amount of data and this is the place where the effectiveness of Big Data comes into play.

Big Data uses the NoSQL databases than can easily store the data in a way that doesn’t require strict adherence. When Big Data is managing or process large amount of data, it is often classified as analytical data and operational data.

In today’s world, the Big Data is used in nearly every industry that needs to identify different patterns, trends, feedback and questions from customers on daily basis. Companies and organizations uses the Big Data to grow their businesses by understanding the customer decisions, making forecasts and enhancing research models.

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