May 13, 2019



Before one can go understanding big data, it is imperative that one first have a basic understanding and knowledge of data first. In terms of computers and computer science, “data is the quantities, numbers, plots, characters, etc. that are stored on magnetic tapes such as say CD’s, pen drives, hard disks, etc. and transmitted in the form of electric signals.” An even more basic definition would be “the raw facts and figures that explain and describe the state of an item over a course of time.”

However, with the stupendous amounts of data that is generated by the world on a daily basis, data is too small a word to actually describe the size. As such, the concept of big data was unearthed. Big data in itself is nothing too sophisticated in itself. Big data is nothing else but data that is extremely large and complex, something that keeps growing with time, and something that cannot be processed to the extent of even a single percent while making use of and deploying the traditional tools and models that we have been using to process standardized data.


Nothing makes the concepts of something clear better than examples that provide a clear and better insight into the same. As such, some examples of big data include-

1. The data produced by stock exchange and trade markets is something that can be very easily termed as big data. With thousands of companies listed on such markets with billions of trades and exchanges made in a single day, it is not really tough to imagine the size of data that a single stock market would produce in a single day. As such, just imagine, how much data the market of the entire world would be producing!

2. Social media is yet another great example of a domain that deals with big data. With millions and millions of people logging into their social media accounts on a daily basis, posting photos, videos, updating their status, texting, tagging, commenting, scrolling, etc. the data produced by a single platform in itself touches levels that are beyond the scope of complete processing even with the computation power we have at our disposal today. For example, Facebook in itself is responsible for producing close to 500 terabytes of social media data on a daily basis.


The contents of big data are very broadly categorized into 3 broad and major categories. These include-

1. Structured data

This is the data that has a definitive structure and format to it. This defined structure and format of data is what help us (as users) to easily access, store, and process such kinds of data.

Examples include database tables, relational databases, etc.

2. Unstructured data

The complete opposite of structured data- unstructured data are those that lack any sort of mandated format and full of random stuff. It is a collection of heterogeneous items from which deriving value is the toughest job of all. Examples include social media data, a collection of heterogeneous items, etc.

3. Semi-structured data

It is basically a collection of both types of data stored in a single place. Examples include XML files, emails, etc.

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The world of big data has forever been closely linked with data analytics owing to the need for the tools and models of data analytics to help sieve and filter through the mess of data that it contains. So, it is not hard to comprehend the level of importance and support given by data science to each and every industry of the modern world. Therefore, it is not rocket science to understand that data analytics training in Bangaloreand certification in the field of data science is always going to be in your best interes