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often meet with limited success. 10, Recursive Hierarchies and Bridges, demonstrates how a hierarchy bridge table can be used to support analysis along an instance hierarchy. Like the solutions for multi-valued attributes, this solution is a limited form of snowflake, and it will introduce the possibility of double counting if used incorrectly.
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Repeating Groups Last, designers sometimes choose to allow a limited form of snowflaking when a group of attributes appears more than once in a dimension table. If customers have a billing address and a shipping address, for example, a series of attributes representing address repeats in the dimension table. Although not undesirable in and of itself, this situation may become a matter of concern if the set of repeating attributes is very large or if it appears in a large number of additional places. In these situations, a limited form of snowflaking may be considered: the use of outriggers.
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On rare occasions, a repeating set of attributes may lead to concerns over inconsistent representation, particularly if the attributes repeat in multiple tables. If the set of attributes is large, concerns of secondary importance may arise over the attributes impact on the length of rows. In these situations, developers may consider relaxing the restrictions against snowflaking. The repeating attributes are placed in a new table, linked to the dimension via a primary key / foreign key relationship. Called an outrigger, this table guarantees a single ETL process and consistent representation, but it can have an impact on usability and performance.
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A common challenge in dimension table design involves repeating groups of attributes. When a dimension table contains a group of attributes that appears multiple times, or which appears in multiple dimension tables, concerns may arise regarding row length or consistency. If other mechanisms to resolve these issues are not effective, outriggers offer a solution of last resort. The salesrep table in Figure 7-6 contains multiple examples of repeating attributes. Each row in this dimension table represents a salesperson. For any given salesperson, there are two physical office locations of interest to the business. The office where the person works is known as the work location; the office to which the position reports is known as the reporting location. Capturing both these locations requires repeating the ten attributes that describe an office location. A second set of repeating attributes represents significant dates associated with the salesrep. One set represents the date of hire, while a second set reflects the date of the last performance review. Nineteen attributes are associated with a date, and each of these attributes must be repeated twice. While this dimension table does not have an excessive number of attributes, it is easy to envision it incorporating far more. Dates and locations, for example, may actually have more attributes than pictured, and each addition will be repeated. As the number of attributes grows, the table diagram in Figure 7-6 grows longer, and row length becomes wider.
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SALESREP salesrep_key saleperson_id salesperson_name reporting_location_id reporting_location_name reporting_location_type reporting_location_address1 reporting_location_address2 reporting_location_city reporting_location_state reporting_location_zipcode reporting_location_zip_plus5 reporting_location_main_phone work_location_id work_location_name work_location_type work_location_address1 work_location_address2 work_location_city work_location_state work_location_zipcode work_location_zip_plus5 work_location_main_phone territory_code territory_name region_code region_name region_manager hire_date hire_day_of_week_number hire_day_of_week_name hire_day_of_week_abbr hire_day_of_month hire_holiday_flag hire_weekday_flag hire_weekend_flag hire_month_number hire_month_name hire_month_abbr hire_quarter hire_quarter_month hire_year hire_year_month hire_year_quarter hire_fiscal_period hire_fiscal_year hire_fiscal_year_period review_date review_day_of_week_number review_day_of_week_name review_day_of_week_abbr review_day_of_month
Two sets of attributes describing locations
Two sets of attributes describing dates
Figure 7-6 Repeating location and date attributes in a dimension table
Row length should not be a concern simply because a dimension table spans more than one page in a schema diagram. Dimension tables in a star schema can have many attributes, as discussed in 3. This may be unusual in an ER model but not in a dimensional model. If row size becomes a legitimate technical concern, it may be possible to employ some of the techniques offered in 6. Sometimes, however, these techniques cannot address the issue. Repeating groups may be a primary contributor to excessive row size, and the solution may be to eliminate them. A large set of attributes that repeats may also lead to concerns regarding the ETL process. Will the contents of these repeating columns be populated in a consistent manner Will the exact same codes be transformed into the same description values What if different ETL programs are populating different instances of the repeating attributes For example, columns such as reporting_location_type and work_ location_type contain the same kind of information. If these location types are derived during the ETL process through a set of rules, storing them as separate attributes risks the possibility that these rules will not be applied consistently. Similarly, if the name of a location changes, this change must be applied consistently to work_location_name and reporting_location_name. Concerns over redundant ETL processing may grow if the attributes in question appear in other dimension tables as well. There may be another table that also incorporates location information, for example. This may be a concern even if there are relatively few attributes in question or if they do not repeat in a given table. The risk can be mitigated by carefully documenting the process that loads these columns. Developers may also be able to incorporate reusable code or function calls to guarantee consistent maintenance of the columns in question.
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