Big Data
McKinsey Global Institute gives the definition of a dataset, whose scale is so large that it far exceeds the scope of traditional database software tools for collecting, storing, and managing analysis. It has four characteristics:Large data size, fast data flow, diverse data types, and low value density.
Technically, big data and cloud computing are as closely connected as the two sides of a coin. Big data cannot be processed by a single computer, but must use a distributed architecture. It is characterized by distributed data mining for massive data. However, it must rely on cloud computing for distributed processing, distributed databases and cloud storage, and virtualization technologies. Data requires special techniques to handle large amounts of data efficiently. Technologies applicable to big data include MPP (massively parallel processing) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.
Data, or massive data, refers to the amount of data involved that is too large to be retrieved, managed, processed, and organized into information by current mainstream software tools to help companies make more positive decisions in a reasonable amount of time.