1. Data Warehouse Architecture

2. Inmon’s Corporate Information Factory The enterprise data warehouse is not intended to be queried directly by analytic applications, business intelligence tools, or the like. Its purpose is to feed additional data stores dedicated to a variety of analytic systems. The enterprise data warehouse is usually stored in a relational database management system, and Inmon advocates the use of third normal form database design. Data Marts These are databases that support a departmental view of information. With a subject area focus, each data mart takes information from the enterprise data warehouse and readies it for analysis. Inmon advocates the use of dimensional design for these data marts. The data marts may aggregate data from the atomic representation in the enterprise data warehouse.

3. A simplified view of W.H. Inmon’s architecture: the Corporate Information Factory

4. Ralph Kimball’s data warehouse architecture

5. Ralph Kimball’s data warehouse architecture: the dimensional data warehouse The dimensional data warehouse in the center of Figure is the end result of the ETL process. It is an integrated repository for atomic data. It contains a single view of business activities, as drawn from throughout the enterprise. It stores that information in a highly granular, or atomic, format. The dimensional data warehouse differs from the enterprise data warehouse in two important ways. First, it is designed according to the principles of dimensional modeling. It consists of a series of star schemas or cubes, which capture information at the lowest level of detail possible. This contrasts with the Inmon approach, where the enterprise data warehouse is designed using the principles of ER modeling.

6. Second, the dimensional data warehouse may be accessed directly by analytic systems. Concept of a data mart becomes a logical distinction; the data mart is a subject area within the datawarehouse. In Figure, this is represented by the box that highlights a subset of the tables in the dimensional data warehouse.

7. Stand Alone Data Mart The stand-alone data mart is an analytic data store that has not been designed in an enterprise context. It is focused exclusively on a subject area. One or more operational systems feed a database called a data mart. The data mart may employ dimensional design, an entity- relationship model, or some other form of design. Analytic tools or applications query it directly, bringing information to end users.

8. Development of a stand-alone data mart is often the most expedient path to visible results. it does not require cross-functional analysis, the data mart can be put into production quickly. No time must be spent constructing a consolidated view of product or customer, for example. No time must be spent comparing data from the sales system withwhat is tracked in the accounting system. Instead, the implementation takes a direct route from subject area requirements to implementation. Because results are rapid and less expensive, stand-alone data marts find their way into many organizations.

9. They are not always built from scratch. A stand-alone data mart may become part of the application portfolio when purchased as a packaged application, which provides a prebuilt solution in a subject area. Packaged data marts may also be available as add-ons to packaged operational applications. Prebuilt solutions like these can further increase the savings in time and cost.

10. Multiple Standalone Data Marts