2. Features: Compared with traditional data warehouse applications, big data analysis has the characteristics of large amount of data and complex query analysis.
2. The concept of "big data" was first put forward by Viktor Mayer-Sch?nberger and Kenneth Cookeye in The Age of Big Data, which means that all data are used for analysis and processing, not random analysis (sampling survey). Big data has 4V characteristics, namely volume, speed, diversity and value.
3. The definition of Gartner, a research institution of "big data", is given. "Big data" is a massive, high-growth and diversified information asset, which needs a new processing mode to have stronger decision-making power, insight and discovery ability and process optimization ability. The strategic significance of big data technology lies not in mastering huge data information, but in specialized processing of these meaningful data. In other words, if big data is compared to an industry, then the key to the profitability of this industry lies in improving the "processing ability" of data and realizing the "value-added" of data through "processing".
4. From a technical point of view, the relationship between big data and cloud computing is as inseparable as the front and back of a coin. Big data cannot be processed by a single computer, and it must adopt a distributed architecture. It is characterized by distributed data mining of massive data, but it must rely on distributed processing of cloud computing, distributed database, cloud storage and virtualization technology.
5. With the advent of the cloud era, big data has attracted more and more attention. Zhucloud's team of analysts believes that big data is usually used to describe a large number of unstructured data and semi-structured data created by a company, and it will take too much time and money when it is downloaded to a relational database for analysis. Big data analysis is often associated with cloud computing, because real-time analysis of large data sets requires a framework such as MapReduce to distribute work to dozens, hundreds or even thousands of computers.