Explaining the difference between a data warehouse and a database

Data Warehouse: A strategic (data) collection that provides all types of data to support the decision-making process at all levels of an organization.

Big Data: The amount of information involved is so large that it cannot be captured, managed, processed, and organized in a reasonable amount of time through mainstream software tools to help businesses make more positive decisions.

Traditional database: A collection of large amounts of data stored in a computer for a long period of time, organized, ****able, and uniformly managed.

In fact, from the three definitions, we don't seem to have much difference.

A database refers to a collection of data, a data warehouse is also a collection of data, and big data is also a place where data is processed and stored.

But the difference lies in the application scenarios, and the different technical principles of building.

Traditional databases are storing relational data modeled according to paradigm, mainly used for OLTP (on-line transaction processing) translated as online transaction processing software. Big data is built according to the map redurce paradigm out of the processing, storage software, mainly used for OLAP is to do analytical processing. Big data and traditional databases, there is a bigger difference lies in the amount of data processed as well as the size of the calculation, when traditional databases, can not be calculated in a short period of time acceptable to people, then this data is called big data, need to use to big data technology processing. The data warehouse is essentially a way of processing data, rather than a basic software, it can rely on traditional databases, can also rely on big data technology to build.

You can refer to this article: data warehouse (2) data warehouse, big data and the difference between traditional databases - Zhihu (zhihu.com)