Glossary: Data Warehouse

A data warehouse (DW) cleans, extracts, and transforms a large amount of traditional database data for transaction processing, and reorganizes the data according to the needs of decision topics. A large number of organizations have discovered that in today's competitive and fast-growing world, data warehouses are a valuable tool. Data Warehouse System Construction expert W. h. inmon definition: "a data warehouse is a topic-oriented, integrated, time-varying, and non-loss-prone data set that supports the decision-making process of management departments ". This definition points out the main characteristics of a data warehouse: subject-oriented, integrated, time-varying, and non-easy to lose, separate a data warehouse from other data storage systems (such as relational database systems, transaction processing systems, and file systems.

First, theme-oriented, it needs to provide comprehensive information for decision makers. The organization of such information should take the subject content of business work in the enterprise as the main line, and it is the unification of data and algorithms. After the data enters the data warehouse from the external data source, the data is stored in the most suitable way under the guidance of a topic, according to the President and the necessary changes. This is because only such an organization can provide comprehensive information availability. The data warehouse answers the following questions: "Which region has the smallest market share of our products?" and "where is the problem with our product quality ?" And other theme-specific questions, while traditional databases answer questions like "What is our annual output ?" And other specialized issues.

Second, integration. Although the data in the data warehouse comes from the daily operation data, it is not a simple merger or migration of the data. The data stored in the data warehouse is a value-added and unified processing of the daily operation data, for example, Uniform Naming rules and uniform measurement units. Because of the Structure of daily operation data, the methods are implemented in different codes and naming rules. However, no matter how the data warehouse is designed, implemented, and the results must be consistent, data and methods must be stored in a single, globally acceptable format. Only in this way can DSS use the data without worrying about the consistency of the data.

Third, it is historic and reflects historical changes. Operational databases are mainly concerned with data within a certain period of time. Data in a data warehouse usually contains historical information, and the system records the information of each stage from a previous location to the present, with this information, you can make a quantitative analysis and prediction of the enterprise's development history and future trends.

Fourth, relative stability. Data in operational databases is usually updated in real time, and changes occur in a timely manner as needed. The data in the data warehouse is mainly used for enterprise decision-making and analysis. The data operations involved are mainly data queries. Once a data enters the data warehouse, it is generally retained for a long time, that is, Data Warehouses generally have a large number of query operations, but few modification and deletion operations, usually only need to be loaded and refreshed on a regular basis.

Generally speaking, a data warehouse is a type of semantic consistent data storage. It acts as a physical implementation for decision-making to support data models and stores the information required for strategic decision-making. Data Warehouses are also often seen as an architecture. Generally, data from heterogeneous data sources is integrated to support structured and specialized queries and analysis, and decision making.