Designing the Data Warehouse for Analysis Services - Expert Cube Development with SSAS Multidimensional Models - page 5

In contrast, in Inmon's view, data coming from the OLTP systems needs to be stored
in the enterprise data warehouse and from there, goes to the data marts, as shown in
the following diagram:

OLTPSystem(s)

Data mart OLAP

Data mart

Data
Warehouse

OLAP

What is important is to understand is that the simple phrase "data warehouse" has
different meanings in each of these methodologies. Finally, it is worth mentioning
that in the recent years several authors proposed combined methodologies (see, for
an example, http://www.sqlbi.com/articles/sqlbi-methodology ).

We will adopt Inmon's meaning for the term data warehouse. This is because in
Inmon's methodology, the data warehouse is a real database, while in Kimball's
view, the data warehouse is composed of integrated data marts. For the purposes
of this chapter, though, what is really important is the difference between the data
warehouse and the data mart. At the end, the source of an SSAS cube needs to be
a data mart, maybe composed of many fact tables and dimensions, but definitely a
data mart.

The data mart
Whether you are using the Kimball or Inmon methodology, the frontend database
just before the Analysis Services cube should be a data mart. A data mart is a
database that is modeled according to the rules of Kimball's dimensional modeling
methodology, and is composed of fact tables and dimension tables.

As a result, we'll spend a lot of time discussing data mart structure in the rest of
this chapter. However, you will not learn how to build and populate a data mart
from reading this chapter; the topics by Kimball and Inmon we've already cited
do a much better job than we ever could.