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In the illustration, the book identified by SKU C770077 has undergone a change in genre, from Fiction to Classics. This change was handled with a type 2 response. A new surrogate key value of 201 was assigned, and new rows were inserted in the core and custom tables. In both tables, the old and new versions of the product are highlighted. In the core table, the old and new rows appear identical, save for their surrogate keys. This occurs because the changed attribute, genre, appears only in the custom table book_product.
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Core and custom dimension tables can be joined interchangeably to fact tables as required by the analytic situation. Nevertheless, it is highly recommended that corresponding custom fact tables be designed in order to avoid possible analytic confusion. These need not be physical tables; views or similar alternatives will suffice. If there are custom facts that correspond to specific types, then custom fact tables for each type will be necessary. Each custom fact table will contain any core facts, along with type-specific facts. It will contain rows only for the specific type represented.
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The Same Facts for All Types
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If the same facts are recorded for all types, you have seen that core or custom versions of the dimension can be joined to the fact table as required by the analytic situation. The core version is used when studying across all types; analysis will be limited to common dimension attributes. A custom version is used when studying a specific type; analysis can include typespecific attributes. This interchangeability is facilitated by the common key domain. In the case of the core and custom product dimension tables from Figure 13-2, for example, if all orders involve the same facts, a single order_facts table is possible. It will contain a row for each order line; one of the associated dimensions will be product. When conducting analysis across all product types, the core product dimension can be joined to the fact table; analysis focused on subscription products can be accomplished by joining the subscription_product dimension table to the fact table. While this meets all the analytic requirements, there is a potential drawback. Any query that joins to a custom dimension implicitly constrains the fact table for a specific type. For example, suppose that a user studying subscription orders builds a report that groups order_dollars by publication_category. This report requires that order_facts be joined to the custom subscription_product dimension table. Although the query has no filter in its where clause, results are effectively constrained as if the following filter had been added to the SQL:
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where product_type = 'Subscription'
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This is not a problem in and of itself, but can lead to confusion as analysis continues. If the report designer subsequently decides to remove the publication_category from the report, the join to the subscription_product table is no longer needed and may be removed from the query. This also removes the implicit filter on product_type, opening the report up to all product types, rather than simply subscriptions. The result is a huge jump in the value of facts on the report, such as order_dollars. This problem can easily be solved by building custom replicas of the order_facts table, each of which only contains the rows for a particular product type. The result is a set of core and custom stars, as depicted in Figure 13-5. The core star contains the core product
13 Type-Specific Stars 315
ORDER_FACTS day_key customer_key product_key transaction_id order_dollars . . . PRODUCT Core Star Analysis across all product types
CUSTOMER
BOOK_ORDER_ FACTS day_key customer_key product_key transaction_id order_dollars . . . BOOK_ PRODUCT
Custom Stars Product-typespecific analysis
CUSTOMER
SUBSCRIPTION_ ORDER_FACTS day_key customer_key product_key transaction_id order_dollars . . . SUBSCRIPTION_ PRODUCT
CUSTOMER
Figure 13-5
Core and custom stars for orders
dimension, and a fact table that contains rows for all orders. Each custom star contains a custom product dimension, and a fact table that contains rows only for the corresponding product type. Analysis focused on a particular type takes place by working with the appropriate custom star. The user can build, execute, and refine queries, maintaining a focus on the particular type regardless of which attributes are included in the query. This approach does not require the development of physical replicas of the order_facts table; each type-specific copy can be created using a view, like this:
CreATe VIew subscription_order_facts AS SeLeCT day_key, customer_key, product_key, transaction_id, order_dollars FrOM order_facts, book_product where order_facts.product_key = book_product.product_key
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