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Extra info for Data Modeling Techniques for Data Warehousing
These types of implementation typically have minimal impact on IS resources and can result in a very fast implementation. However, the minimal integration and lack of a more global view of the data can be a constraint. That is, the data in any particular data mart will be accessible only to those in the workgroup, department, or line of business that owns the data mart. Be sure that this is a known and accepted situation. Figure 6. Data Mart Architectures. They can be independent or interconnected.
On the other hand, having data of a low granularity would support only specific queries. But, with the reduced volume of data, you would realize significant improvements in performance. The size of a data warehouse varies, but they are typically quite large. This is especially true as you consider the impact of storing volumes of historical data. To deal with this issue you have to consider data partitioning in the data architecture. We consider both logical and physical partitioning to better understand and maintain the data.
The ER model is an abstraction tool because it can be used to understand and simplify the ambiguous data relationships in the business world and complex systems environments. Dimensional modeling uses three basic concepts: measures , facts , and dimensions . Dimensional modeling is powerful in representing the requirements of the business user in the context of database tables. Both ER and dimensional modeling can be used to create an abstract model of a specific subject. However, each has its own limited set of modeling concepts and associated notation conventions.