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Rating Your Dimensional Data Warehouse - Article Example

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The article titled Rating Your Dimensional Data Warehouse is a concise piece of scholarship that outlines the key metrics in evaluating a Data Warehouse. Data Warehouse as a data storage concept and decision making aid has evolved a lot over the past two decades. Yet, no…
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Rating Your Dimensional Data Warehouse

While a sum total score of 0 would indicate a system completely unsupportive of a dimensional approach, a score of 20 would indicate a system that is fully supportive of a dimensional approach.
The author outlays 12 of the 20 criteria in the article. Some of the criteria that pertain to the Architecture of the Data Warehouse are: Explicit Declaration, Conformed Dimensions and Facts, Dimensional Integrity, Open Aggregate Navigation, Dimensional Symmetry, Dimensional Scalability and Sparsity Tolerance, etc. Kimball explains Open Aggregate Navigation as follows:
“The system uses physically stored aggregates as a way to enhance performance of common queries. These aggregates, like indexes, are chosen silently by the database if they are physically present. End users and application developers do not need to know what aggregates are available at any point in time, and applications are not required to explicitly code the name of an aggregate. All query processes accessing the data, even those from different application vendors, realize the full benefit of aggregate navigation.” (Kimball, 2000)
Similarly, some of the criteria that fall under Administration category are: Graceful Modification, Dimensional Replication, Dimension Notification, Surrogate Key Administration, International Consistency, etc. Kimball explains Dimensinal Replication as follows:
“The system supports the explicit replication of a conformed dimension outward from a dimension authority to all the client data marts, in such a way that we can only perform drill-across queries on data marts if they have consistent versions of the dimensions. Aggregates that are affected by changes to the content of a dimension are automatically taken offline in each client data mart until we can make them consistent with the revised dimension and the base fact table.” (Kimball, 2000)
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