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Snowflakes and Outriggers
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You may have noticed that dimension tables contain a lot of redundancy. For example, the product table in Figure 3-1 contains several brand attributes. Presumably, there are far fewer brands than there are products. Storing brand attributes in the product table requires brand values to be repeated for many products. Entity-relationship modelers use principles of normalization to drive this kind of redundancy out of relational designs. Brand attributes, for example, would be removed from the product table and stored in a separate table. In their place, a foreign key reference to the new brand table is added. In a dimensional design, the result is a variation on the star schema called a snowflake schema. The brand table is referred to as an outrigger. This technique provides numerous benefits for operational systems, which must support a wide variety of transaction types concurrently. Analytic systems are used differently, and do not usually benefit from this kind of design. Snowflakes may be useful, however, if your RDBMS or business intelligence software is specifically optimized for snowflakes. There are also some situations where limited use of outriggers provides analytic benefit. These issues are fully explored in 7, Hierarchies and Snowflakes. TIP Do not use the principles of normalization to guide dimension table design. Analytic databases do not benefit from these techniques. Situations that call for snowflakes and outriggers are the exception rather than the rule.
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Even those not steeped in the practice of entity-relationship modeling may be put off by some of the techniques described in this chapter. Newcomers to dimensional design are often repulsed by redundant storage of multiple part codes and their components, storage of common combinations of elements such as names, or the expansion of flag values from Boolean indicators into descriptive text. These techniques can seem wasteful. Why not simply perform these computations on the fly at query time The precomputation and storage of these redundant data elements have three advantages in an analytic environment: performance, usability, and consistency. Recall that dimensions are used to specify query predicates, define the scope of aggregation, govern the ordering of data, and establish master detail structure in reports. If these elements must be computed at query time, performance will be significantly impacted. Precomputing and storing these columns reduces the burden on the DBMS at query time, allows the database administrator to optimize performance through the use of indexes and other techniques, and opens the door to more efficient query execution.
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Second, this level of redundancy makes it much easier for users to interact with the analytic database. It is far easier for the end user to select data where the country is United States rather than look for records where the first two digits of region_code are 07. If a report calls for customer names to appear in a last-name-first format, it is much easier to select that in a single column than it is to concatenate multiple fields together, omitting the space after first name if there is no middle initial, and so forth. If a user wants to break out credit orders, it is much easier to have a flag that reads Credit Approved or Credit Not Approved than it is to translate a Boolean value. A business intelligence tool may provide some of this functionality but does not offer the simultaneous performance benefits. Last, explicit storage of all dimensions guarantees that they are consistent, regardless of the application being used. A business intelligence tool can be used to make it easier to provide full names, isolate substrings, or decode flags, but these shortcuts are only available to users who are interacting with the analytic database through that tool. If there are other tools in use, or if report developers are writing their own SQL, there is the danger that these dimensions are not being constructed consistently. TIP Embrace redundancy in the dimensional model. Explicit storage of each dimension attribute maximizes performance, aids usability, and guarantees consistency across applications. The rules of normalization are best suited to the needs of operational systems, not analytic systems. None of this should be interpreted as an argument against the use of business intelligence tools. In fact, these tools are extremely valuable components of the data warehouse architecture. Using these tools in an effort to save space, however, is misguided. Storing dimensions explicitly ensures performance, usability, and consistency. Computing dimensions on the fly saves a few dollars worth of disk space.
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