Data Warehouse Architectures

Among the architectures, two most popular ones are Inmon's Corporation Information Factory and Kimball's Dimensional Data Warehouse.

• Corporation Information Factory, also known as hub and spokes. The later can be told by the following diagram easily.

It consolidates information various data source throughout enterprise into a centralized repository (enterprise data warehouse). The enterprise data warehouse is designed under third normal form, and it is not queried directly by warehouse applications. Data marts, each tailored to the needs of particular business group, are built upon the enterprise data warehouse. These data marts utilize dimensional design and are queried by data warehouse applications.

•  Dimensional Data Warehouse, AKA Enterprise Bus Architecture

This looks similar to Enterprise Information Factory according to the elements and data flow in the drawing, since they are all focusing on enterprise context and driven by the same enterprise analytic needs, but they are quite different on implementation.

Bus architecture is a better name for this solution because it expressed the implementation principle this architecture follows to build up a data warehouse system. It makes the most detailed data directly available to end users in dimensional form but in a business-process-aligned (rather than departmentally aligned) manner, so that this model can be implemented step-by-step according to business process to satisfy different group of users according to priority and likely to deploy earlier than the use of Inmon's approach.

some of its characters include data warehouse being designed according to principles of dimensional design, accessed directly by analytic systems. Data mart becoming virtual concept and residing inside the data warehouse.    

 In practice, we do not exclusive use one over another. The right technology should be picked to fit the right situation. For example, in Kimball's dimensional data warehouse, TNF tables can take place naturally.

Pictures are from web and book of Star Schema

data warehouse - fact and dimenssion

From WIKI.

The centralized table in a star schema is called a fact table. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys. Fact tables contain the content of the data warehouse and store different types of measures like additive, non additive, and semi additive measures.

In a data warehouse, a dimension is a data element that categorizes each item in a data set into non-overlapping regions. A data warehouse dimension provides the means to "slice and dice" data in a data warehouse. Dimensions provide structured labeling information to otherwise unordered numeric measures. For example, "Customer", "Date", and "Product" are all dimensions that could be applied meaningfully to a sales receipt. A dimensional data element is similar to a categorical variable in statistics.

The primary function of dimensions is threefold: to provide filtering, grouping and labeling. For example, in a data warehouse where each person is categorized as having a gender of male, female or unknown, a user of the data warehouse would then be able to filter or categorize each presentation or report by either filtering based on the gender dimension or displaying results broken out by the gender.

Each dimension in a data warehouse may have one or more hierarchies applied to it. For the "Date" dimension, there are several possible hierarchies: "Day > Month > Year", "Day > Week > Year", "Day > Month > Quarter > Year", etc