Big data refers to ultra large data sets analyzed computationally for patterns, trends, and associations, often relating to human social networks. The term encompasses not only the size (volume) of data but also the variety of types of data as well as the speed (velocity) at which it is processed.
The big in Big Data is not about just the size (or volume) of data. Certainly, size is part of it. But, big also applies to other aspects of the data. These other aspects include how widely varying it is in data type and structure and how quickly the data is processed.
So, the big in Big Data refers to all the three V's: Volume, Variety and Velocity.
Volume: Big data involves extremely large volumes of data, often terabytes or petabytes, collected from a variety of sources like web logs, social media, or sensors.
Velocity: The speed at which data is generated and needs to be processed is also a defining characteristic of big data. Real-time or near-real-time (meaning the data is processed and results obtained as fast as the data is acquired) information processing can be essential for activities such as fraud detection and online advertising.
Variety: Big data comes in multiple data types — structured, semi-structured, and unstructured. This variety includes data from different sources such as text, videos, emails, databases, and transactions.
Big data technologies include tools and processes used to handle these datasets and extract meaningful information from them. This capability is crucial across various sectors, including business, healthcare, finance, science, and technology, to drive innovation and efficiency.
In traditional scientific computing, datasets can certainly be big in volume but are less frequently, if ever, big in the other two V's of big data. That said, as ML/AI become more commonly used to support scientific computing, this distinction is becoming less relevant.
