The key dimensions, like customer and product, that are shared across the different facts will be built once and be used by all the facts Kimball et al. This is the document where the different facts are listed vertically and the confirmed dimensions are listed horizontally.
Where ever the dimensions play a foreign key role in the fact, it is marked in the document. This serves as an anchoring document showing how the star schemas are built and what is left to build in the data warehouse. Source: Zentut. Now we have seen the pros and the cons of the Kimball and Inmon approach, a question arises. Which approach to use when? This is faced by the data warehouse architects every time they start building a data warehouse.
Here are the deciding factors that can help an architect to choose between the two. It has been proven that both the Inmon and Kimball approach works for successfully delivering data warehouses. In a hybrid model, the data warehouse is built using Inmon model and on top of the integrated data warehouse, the business process oriented data marts are built using star schema for reporting.
We cannot generalize and say that one approach is better than the other. They both have their advantages and disadvantages and they both work fine in different scenarios. The architect has to select an approach for the data warehouse depending on the different factors, few key ones were identified in this paper.
Breslin, Mary. Accessed May 22, Inmon, W. Building the Data Warehouse, Fourth Edition. Marakas, George M. Prentice Hall, Kimball, Ralph, and Margy Ross. Accessed May 26, Accessed May 25, Building the Data Warehouse, 4th Edition.
Building the Data Warehouse, 4th Edition W. Added to Your Shopping Cart. About the Author Bill Inmonthe father of the data warehouse concept, has written 40 books on data management, data warehouse, design review, and management of data processing. Bill has published more than articles in many trade journals.
Bill founded and took public Prism Solutions. Bill holds two software patents. Articles, white papers, presentations, and much more material can be found on his Web site, www. Permissions Request permission to reuse content from this site. Evolution of Decision Support Systems. The Data Warehouse Environment. The Data Warehouse and Design. Granularity in the Data Warehouse.
The Data Warehouse and Technology. The Distributed Data Warehouse. Executive Information Systems and the Data Warehouse.
External Data and the Data Warehouse. If you use SAN, you need to make sure the data warehouse has enough space allocation. Building and maintaining a data warehouse isn't a trivial task, so be sure you have up-to-date knowledge of Author : K.
The payback period is less than one year, more than justifying the costs of building and maintaining a data warehouse. Developing the data warehouse project plan involves identification of all the tasks necessary to implement the data warehouse.
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