What is Big Data and the V’s of Big Data

Big Data and V of Big Data

Big data has become quite a buzz these days and we all here that Data Scientist and Data Analytics is the sexiest job in 21st century. But What is big Data? What are the 4 V’s of Big Data and the difference between Data and Big Data – Data vs Big Data . What is the job scope in Big data industry and future of jobs in the industry. Let’s understand this concept:

What is Big Data and Comparison with Data :

Big data is basically a large dataset which in turn can not be analyzed,  maintained and studied by the normal RDBMS & traditional computer systems. Thus there is no specific definition of size of Big data. Then you must be wondering why there is a need to understand big data concepts:

Need of Big Data Concepts:

Today we are living a digital world wherein most of the things have become digital from doing shopping online to studying online. All those eCommerce websites giving you recommendations of products and providing discounts on those recommended products. Have you ever wondered how this has happened…here comes the magic of big data analytics 🙂

But before that do you also want to learn basics of SQL? If yes, check this tutorial here. Also, check Interviews tips of different analytical companies.

Characteristics of Big Data (4 vs of Big Data) :

There are 4 characteristics of big data that together creates the difference and makes Data vs Big Data a discussion:

4 vs of Big Data

4 vs :

  1. Volume – By volume we mean the scale of data in size.. It is very important to analyze the volume to scale us the hardware and software and manage it accordingly for a faster retrieval process of data.
  2. Velocity – Velocity means the speed with which it is increasing each day. Analyze the streaming data.
  3. Variety – In digital world, data can be text, image, audio and video and this increase the uncertainty in data
  4. Veracity – Veracity meaning depicts the biases, noises and abnormality present in big data. Analyze the uncertainty present in the data.

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