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Isolate Dimension Roles
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Many fact tables include multiple relationships to a single dimension table. As you learned in 6, these various relationships represent different roles for the dimension. A fact table tracking mortgage applications, for example, contained three references to an employee dimension: one for a mortgage officer, one for a clerical processor, and one for an underwriter. Isolating a particular role in a query involves aliasing the dimension table and joining it to the appropriate foreign key column in the fact table. This may be achieved using SQL aliasing or views. Quite often, it can also be done within a business intelligence tool s semantic layer. Leveraging this ability makes good sense, because it reduces the need to maintain external views and guarantees that the correct joins are used to isolate individual roles. TIP Make use of the built-in aliasing capability in the semantic layer to support dimension roles. As you will see, aliasing may provide additional advantages as well. Careful application of aliasing techniques may be useful in separating parts of the database schema so that a query generator does not attempt to link them together.
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Business intelligence tools do not present information to end users in table/column format. Instead, they typically offer some alternative organizing principle, such as a folderbased metaphor or a hierarchical tree that can be expanded and collapsed. Items can be placed in whatever arrangement makes sense. This kind of presentation metaphor can be used to make the model more understandable to a user. For example, the semantic layer can be used to relocate degenerate dimensions,
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grouping their presentation to end users with other dimension attributes, rather than colocating them with facts. Attributes from very large dimensions may be subdivided into groups, or vice versa. All these techniques can aid in making the available facts and dimensions more comprehensible to end users. TIP Make use of a semantic layer s capability to organize the available facts and dimensions in a manner that will make sense to end users. It is also useful to note that the semantic layer does not need to expose every aspect of the dimensional schema. There is no need, for example, to expose surrogate keys to end users. These attributes carry absolutely no business meaning, serving only as a means to uniquely identify rows of data. Other housekeeping columns that support maintenance of ETL processes may also be hidden from users.
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Working with SQL-Generating BI Tools
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Getting a handle on how the business intelligence tools of the target architecture generate queries is essential to ensuring attainment of the full breadth of analytic possibility afforded by a dimensional model. Capabilities of the BI tool may necessitate the addition of derived schemas or views. Balancing the need to guard against inaccurate results with the need for analytic flexibility may require setting up different semantic layers for novice users and expert developers. Some dimensional constructs will also call for different join configurations based on the question being asked. In each of these cases, standard features of dimensional designs may require adjustment in order to work well with the BI tool. Common examples include the presence of multiple stars, multi-table browses, semi-additive facts, and bridges. The way each of these is handled by the SQL-generating BI tool will influence the configuration of the BI tool as well as the schema itself.
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Learning to work with a business intelligence tool often takes place in the context of a schema that contains a single star. This is unfortunate, because many of the challenges for query generators stem from the presence of multiple stars. It is important to understand whether and how the tool will handle drill-across reports, queries that do not involve facts, and conformed dimensions. In addition, it is important to realize that most tools will not be able to generate queries when there is more than one way to compare two processes.
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