Big data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed with traditional data processing tools. The term "big data" encompasses not only the size of the data but also its variety, velocity, and complexity. These datasets often include structured and unstructured data from various sources, such as social media, sensors, devices, and more.

The three Vs commonly associated with big data are:

• Volume: Refers to the sheer size of the data generated or collected. The volume of big data is typically measured in terabytes, petabytes, or even exabytes.

• Velocity: Refers to the speed at which data is generated, collected, and processed. In many big data scenarios, data is generated in real-time or near-real-time, requiring rapid processing.

• Variety: Indicates the diverse types of data that can be included in big data. This includes structured data (like databases), unstructured data (like text or images), and semi-structured data (like XML files).

There are additional Vs that are sometimes included, such as Veracity (dealing with the quality of the data) and Value (extracting meaningful insights from the data). Some discussions also include other Vs like Validity, Volatility, and Vulnerability.

The definition of "big" data is somewhat relative and can vary depending on the context and the capabilities of available technology. What may be considered big data for one organization might not be for another. Generally, datasets in the range of terabytes to exabytes are considered indicative of big data.