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Figure 16-6 Snowflaking for the benefit of the reporting tool
16 Design and Business Intelligence 385
An SQL generator that cannot comprehend conformed rollups like day and month may be able to drill across in this scenario. If the primary reporting tool in a particular environment cannot drill across conformed rollups, as in Figure 16-5, but can with a snowflake, as in Figure 16-6, then the warehouse team might seriously consider relaxing the general guideline against snowflaking. Blind adherence to a principle simply limits the value of the solution. In this limited case, the drill-across benefit may outweigh the disadvantages of snowflakes as discussed in 7. If there are no other options, this may be a very good reason to snowflake. This decision should be made carefully. Once the solution is built, it will be hard to reverse. If the reporting tool is ever replaced, significant rework will be required to convert to a star configuration. You may wind up living with drawbacks of snowflakes indefinitely, including potential impact on query performance, defeat of a star-join optimizer, the need for additional key management, and the ripple effect of slow changes through the snowflaked dimension.
Semi-Additivity
Semi-additive facts pose a different kind of challenge for business intelligence deployments. It is usually impossible to prevent a query generator from aggregating them across the nonadditive dimension, producing incorrect results. For tools that can prevent this, another problem emerges: the rare case where this behavior is desired will also be prevented. The best solution to this problem calls for limiting full access to trained developers and power users. A less functional but safe version of the star can be made available to novice users. They will be able to accomplish less, but at least they will not be making mistakes that could have serious consequences.
Using Semi-additive Facts
Semi-additive facts can be safely summed across some but not all dimensions. As you learned in 11, fact tables that are periodic snapshots usually have at least one semiadditive fact, which cannot be summed across snapshot periods. The bank account example from Figure 11-2 depicts a daily snapshot of account balances. Its dimensions include day, account holder, branch, and account. The account balance fact is semi-additive. It may be summed across account holders, branches, or accounts in a meaningful manner. However, it is not appropriate to sum it across days. The balance in your bank account on Friday is not the sum of balances from Sunday through Thursday. It is also important to recognize that for some semi-additive facts, dimensions other than time are the problem. A star that tracks sales goals, as in Figure 16-5, contains facts that are not additive across versions of the sales plan. When using a semi-additive fact, it is important to do one of two things: Constrain the query for a specific instance of the non-additive dimension, or Group the query results by instances of the non-additive dimension An example of the former is to select the sum of account_balance for a specific date. An example of the latter is to sum account_balance and group the results by day. In this case, it will also be important not to create totals within the report.
Part VI
PART VI
Tools and Documentation
Many query-generating tools do not comprehend the concept of semi-additivity and are, therefore, unable to see to it that one of these steps is taken whenever a semi-additive fact is summed. For these tools, a fact is a fact. If it is present in a query, it will be aggregated and grouped by whatever dimensions are included. If it is likely that inexperienced users will be putting together reports, it is quite possible that they will inadvertently sum the semi-additive fact across the problem dimension. While they are likely to understand that this does not make business sense, it is easy to forget that the query must either be filtered or grouped by the dimension in question. The result is a disastrous misstatement of the facts.
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