Big Data and Three V's

We hear one term constantly when we talk about Big Data and that is Data Volume. Size does matter and in every phase of life, but when we talk about big data, there are several other attributes attached to it i.e. Data Velocity and Data Variety (Russom, 2011). 

Data Volume:

In today’s time when we talk about big data, we refer to a large number of data sets companies are storing and processing to forecast the demand. If we talk further and ask what the size of this data is? the answer will be around exabytes (1 million TB) or petabytes (1,000 TB) (Reca, 2018).

With this amount of data comes a bigger problem of storing and processing, which can cost a lot if a company goes traditionally. But don’t be heartbroken as there are many cloud options available in the market for data warehousing and one of the most famous is Amazon Redshift (Reca, 2018). 

  

Data Velocity:

Big data flows into a company’s database with very high frequency and in large volumes.  

This data mostly consists of real-time streaming data which help companies in making recommendation or improvement for their customers.

The most popular way of processing this data is the streaming process and apache organization stands out on expectations (Reca, 2018).

Data Variety:

Big Data can come through various sources, the most popular ones in today’s time are web sources, clickstream, social media and IoT devices (Russom, 2011). 

A variety of big data can be further divided into “structured”, “unstructured” and “Semi-structured” data. 

This data is stored in its raw form in big data lakes, as variety and volume cannot stay without each other. Big data file storage systems like Amazon S3 or Google Firestore, come into rescue for companies to store their raw data and use them whenever required.

Reference List:

1. Reca, M. (2018) The 3+ Vs of Big Data: Volume, Velocity, Variety, and a whole lot more, FlyData | Real Time MySQL Replication to Amazon Redshift. Available at: https://www.flydata.com/blog/3-vs-of-big-data/ (Accessed: 3 February 2020).
2. Russom, P. (2011) ‘Big Data Analytics’, BIG DATA ANALYTICS, p. 38.
3. Wilder-James, E. (2012) What is big data?, O’Reilly Media. Available at: https://www.oreilly.com/radar/what-is-big-data/ (Accessed: 4 February 2020).