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This problem may be solved by producing two merged fact tables: one that drills across on order date and one that drills across on order date for orders and shipment date for shipments Alternatively, two semantic layers can be set up, with the appropriate business views, aliases, and joins to support the two viewpoints TIP When there are multiple ways to compare two processes, a query generator cannot infer what users want based on the data elements they have requested Build multiple merged fact tables for each possibility, construct different semantic layers for each possibility, or leave drilling across in the hands of expert developers..

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Multiple stars may bring yet another challenge. It is necessary to understand whether and how your reporting tool supports conformed dimensions. When two distinct dimension tables conform, a single logical attribute can be found in multiple physical locations. This occurs when conformed replicas are stored in different databases, or when one dimension is a conformed rollup of another. In the semantic layer, it is generally preferable to have a single logical dimension attribute. Some reporting tools are unable to comprehend situations like this. In Figure 16-5, for example, dimension attributes such as month, quarter, and year can be found in multiple places. In the orders star, they are found in the day table. In the sales goals star, they are found in the month table. It might be desirable to provide a single set of date attributes in the semantic layer. They can be combined with the goal facts to study

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plans, order facts to study actuals, or both to drill across. Unfortunately, many tools are unable to comprehend the possibility that there are multiple possible places to find month (or quarter, or year.) When a business intelligence tool is unable to identify cases where conformed attributes, such as month, can appear in multiple tables, it will not be able to drill across automatically. In this case, drilling across may once again be left to developers who manually construct queries. Alternatively, a separate merged fact table can be provided, eliminating the need to drill across. TIP Conformed dimensions may result in a single logical attribute appearing in multiple physical locations. Some reporting tools can understand this, and generate SQL that fetches the attribute from the appropriate location. If a tool does not have this ability, drilling across will be a manual process. A merged fact table can be added to obviate the need for manual drill-across reports. Reporting tools that cannot identify conformed dimensions may be able to automatically drill across in this situation if the schema is designed as a snowflake, rather than a star. This approach, depicted in Figure 16-6, ensures that each attribute is stored in a single location.

When it attempted to make this expense ruling in 1993 and 1994, however, the board was nearly pummeled out of existence by CEO groups, institutional investors, the major accounting firms, the Securities and Exchange Commission (SEC), Congress, and the White House As the FASB itself explained: When the FASB developed FAS 123 (Financing Accounting Standard 123) in the mid-1990s, the Board proposed requiring that [expensing] treatment because it believed that this was the best way to report the effect of employee stock options in a company s financial statements The FASB modified that proposal in the face of strong opposition by many in the business community and in Congress that directly threatened the existence of the FASB as an independent standard setter 9.

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.

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