What is the difference between a data center and a data warehouse?

The difference between data middleware and data warehouse can not be simply summarized, they are in the data source, modeling methods, scenarios, applications, etc., the difference is still quite big, probably can be summarized as the following four points:

1, data sources are different

Traditional data warehouse to the business database of structured data, that is, the main with row and column structured data, such as tables; and data middleware is neither a tool nor a store, it can contain data warehouses.

2, modeling methods are different

Data warehouses tend to use a top-down construction model, which needs to be driven by clear business analysis, and the continuity is not high, while the data middleware uses a bottom-up approach, which can be combined with the changes in business requirements to iterate and upgrade, and is closer to the business side.

3, construction goals are different

Data warehouses to the output of a business theme BI reports and decision-making, single-purpose, data center advocates to open up the entire domain of the data silo, to eliminate the inconsistencies in data standards and caliber, to release the value of the business side of the data application.

4, data application is different

Data warehouse is mainly for management decision-making and other analytical scenarios, there are limitations in other aspects, such as data modeling, data tracking and probing, deep mining and so on. The data center provides data to the business system after servitization, which is not only limited to analytical scenarios, but also applicable to transactional scenarios, such as marketing recommendation, risk assessment, and so on.

We return to the official definition of the two:

Data Warehouse: A high-capacity repository that sits on top of multiple databases and serves the purpose of storing large amounts of structured data and allowing for frequent and repeatable analysis to help companies build business intelligence (BI).

Data Middle Office: Broadly speaking, it encompasses top-level data strategy, data governance system, as well as data management and operations, data culture cultivation, and organizational structure support, and is a set of systems for continuous management and operations.