Big data

Big data is another modern buzzword that you can find around the data management and analytics platforms. The big really does not have to mean that the data volume is extremely large, although it usually is.

There are different imperatives linked to big data, which describe the theorem. These would include the following:

• Volume: Volume really describes the quantity of the data. There's a big potential to get value and insights from large-volume datasets. The main challenge is that the data sets are so big and complex that the traditional data-processing application software's are inadequate to deal with them.
• Variety: Data is not strictly a relational database anymore, and data can be stored in text files, images, and social feeds from a social network.
• Velocity: While we want to have the data available in real-time, the speed of the data generation is challenging for regular DMBS systems and requires specialized forms of deployment and software.
• Veracity: With a large amount of possible data sources, the quality of the data can vary, which can affect the data analysis and insights gained from the data.

Here are some big data statistics that are interesting:

• 100 terabytes of data are uploaded to Facebook every day
• Every hour, Walmart customers' transactions provide the company with about 2.5 petabytes of data
• Twitter generates 12 terabytes of data every day
• YouTube users upload eight years worth of new video content every day