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Fact table and dimension table have same number of rows
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Figure 8-2 A fact table records the change history of the policy dimension
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You may notice that this fact table does not contain any facts. That is okay; it is still useful. For example, it can be used to count the number of policies in effect on a particular date with married versus single policy holders. Fact tables like this are known as factless fact tables, and 12 discusses them in detail. The dates associated with each row merit some additional scrutiny. As noted, the fact table includes a day_key_effective, indicating when the change went into effect, as well as a day_key_expired, indicating when the change was superseded by a new version. It is useful to avoid overlapping dates, in order to avoid any confusion when building queries. For each row in the fact table, the effective and expiration dates are inclusive. If a policy changes today, today s date is the effective date for the new version of the policy. Yesterday s date is the expiration date for the previous version. The pair of dates can be used to determine the policy s status at any point in time. Add one filter looking for an effective date that is before or equal to the date in question, and another for an expiration date that is greater than or equal to the date in question. The current version of a policy has not expired, and it has no expiration date. Instead of recording a NULL key, it refers to a special row in the day dimension. This row typically contains the largest date value supported by the relational database management system (RDBMS), such as 12/31/9999. Described in 6, this technique avoids the need to test for null values when building queries. When the row does expire, the date will be replaced with the actual expiration date. It is also necessary to look a bit more closely at the grain of the fact table, described earlier as one row for each policy change. It is important to document what exactly a policy change is. If Hal Smith s family size and number of children change on the same day, is that one change or two In the case of policy changes, it is likely to represent a set of changes that are logged in a source system as part of a single transaction. In other cases, it may be necessary to log each change individually. This may require the ability to store multiple changes for a single day. To support this, you can add a time dimension and a pair of time_keys to supplement the day_keys. The time_keys refer to the time of day the record became effective (time_key_effective) and the time of day it expired (time_key_expired.) The policy_change_facts star effectively captures the history of changes to the policy dimension, so that people can identify what policies looked like at any particular point in time. This information can now be accessed, even for days when there are no payments. You may have noticed something peculiar about this fact table. Since it contains a row for each change to a policy, it has the same number of rows as the policy dimension itself. This is not necessarily a bad thing, but it suggests that there may be a more effective way to gather the same information.
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A time-stamped dimension is one that tracks the history of changes in the dimension table itself, rather than through a separate fact table. The solution captures the same information as a tracking star like the one in Figure 8-2. The dimension is outfitted with additional columns to capture the effective and expiration dates for the row. Optionally, an additional column may track the transaction type or reason for the change. A flag is added, which can be used easily to select the current row in the table for any given natural key.
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