Characteristics

The volume refers to the amount of data that is collected and stored, which can range from gigabytes to exabytes and beyond. Big data requires special technologies for analysis and processing, as conventional computing methods cannot handle such large data sets.An example of a high-volume data source is the Large Hadron Collider (LHC), which is the world’s largest and most powerful particle accelerator. The LHC generates about 25 petabytes of data per year, which is equivalent to 6.25 million DVDs.

The velocity refers to the speed at which data is generated and processed. Big data often involves data that is streamed or comes in batches, which needs to be analyzed in real-time or near real-time. The faster the data is processed, the more value it can provide for decision making.An example of a high-velocity data source is the Twitter platform, which is a popular social media service that allows users to post and interact with messages called tweets. Twitter generates about 500 million tweets per day, which is equivalent to 6,000 tweets per second. 

The veracity refers to the trustworthiness and reliability of data, which can affect the accuracy and usefulness of the insights derived from Big data. Big data can contain errors, inconsistencies or missing values that need to be cleaned, validated and verified.An example of high veracity data would be the one from the World Health Organization (WHO), as they are collected and analyzed by experts and professionals, following rigorous and transparent standards.

The variety refers to the diversity of data sources and types, which can include structured, semi-structured or unstructured data. The variety of data poses challenges for integration, quality and analysis.An example of a high-variety data source is the NASA Earth Observing System (EOS), The EOS collects data from various sensors and instruments that measure different aspects of the Earth, such as temperature, precipitation, vegetation, clouds, etc.