The difference between database and big data

For database researchers and practitioners, the transformation from database (DB) to big data (BD) can be compared to "pond fishing" to "fishing on the sea floor". "Pond fishing" represents the data management method in the traditional database era, while "sea fishing" is the data management method in the big data era. These differences are mainly reflected in the following aspects:

1, data scale

The most obvious difference between database and big data is scale. The scale of the database is relatively small, even the database that was previously considered to be relatively large, such as VLDB (very large database), is still far from the big data XLDB (very large database).

Generally, MB is the basic unit of database processing, and GB, TB and PB are the basic units of big data processing.

2. Data type

Traditional database data is single, often only one or a few, and these data are mainly structured data. There are hundreds of millions of kinds of big data, including both structured data, semi-structured data and unstructured data. Importantly, the share of semi-structured and unstructured data is increasing.

3. The relationship between schema and data

Traditional databases always have patterns before generating data. In many cases, it is difficult to determine the mode of big data in advance, and the mode can only be determined after the data appears, and the mode is constantly evolving with the growth of data volume.

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Traditional database data is the object of its processing. In addition to data, the processing object of big data can also predict the possibility of other data through these data, and use the collected data as resources to help solve problems in many other fields.