According to Gartner Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
It is related with data, both structured and unstructured but it is not the amount of data that is important, it is what do we do with that data and how do we do it so that the organisation is beneficial to take better decisions and make strategic moves.
In most big data circles, these are called the four V’s: volume, variety, velocity, and veracity. (You might consider a fifth V, value.)
Volume. Organizations collect data from a variety of sources, transactions, social network and information from sensor or machine-to-machine data. Now days storing of this data is made easy by technologies like Hadoop. The volume of the data could be in Tera Bytes, Peta Bytes, etc
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. This is not in our control. It just gets coming in.
Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data, machine data and financial transactions.
Veracity. Data comes in any structure. It could be dirty, incomplete, inaccurate, etc
Value. Using the above 4 Vs and applying the big data tools and techniques, we can take out the value from this data so that businesses can make smart, customer centric moves to increase their sales, or increase market share or serve customer in better way.
The biggest driving factors behind BigData are machine generated data, content on social media, data generated by software applications, cloud adaption, mobile devices and its apps, etc.
In further blog posts, we will see how to consider and use different elements of big data to come up with solutions of big data projects.
- BigData Architecture Stages
- Data Collection
- Save Data on BigData server
- Data transformation
Thank you for reading.