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This chapter provided some advanced techniques for responding to changes in source data. Building on the basic type 1 and type 2 changes introduced in 3, this chapter added the time-stamped dimension, type 3 change, and hybrid change. Time-stamped dimensions supplement the type 2 approach Type 2 changes preserve the historic context of facts but do not allow for point-intime analysis within a dimension. A fact table can be used to log the change history of a dimension table, associating each version with the range of dates during which it was effective. This fact table will have the same number of rows as the dimension table. A time-stamped dimension does the same job without requiring an additional fact table. This is done by adding effective and expiration dates, and managing these columns so two rows for the same natural key do not overlap.
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The time-stamped dimension is harder to load but makes it easier to load associated fact tables. A time-stamped dimension may contain attributes that behave as dimensions in some queries and facts in other queries. Type 3 changes offer a different spin on history When users want to connect either the old or new value to all facts, regardless of when the change occurred, neither type 1 nor type 2 is sufficient. The type 3 solution involves maintaining the current value for the attribute, as with a type 1 change, and an additional column to capture the prior value. Analysis can use either value by selecting the appropriate column. Multiple versions are possible, but this can lead to situations that require table definition changes when adding new columns or hardcoding particular versions into reports. Type 1/2 hybrid changes allow for both type 1 and type 2 responses When seemingly conflicting requirements call for an attribute to be treated as type 1 and type 2, design two attributes. One attribute will be treated as type 1. It will be updated whenever the source value changes and it can always be used to study all facts with the current value. The other attribute is treated as type 2: when a change occurs, a new row is added to the dimension table. This attribute can be used to group facts with the historically accurate value. End-user confusion can be avoided by careful naming, or by hiding the lesser-used attribute using a view. Analysts and report developers should be educated on the difference between the attributes, and when it is appropriate to use each of them.
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A transaction history can be ordered by using the effective_date column. Effective_ date and expiration_date can be used together to filter for records in effect at a particular point in time. A most_recent_row flag can be added to make it easy to select current records from the dimension.
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The advanced slow change techniques presented in this chapter can be employed in a variety of situations. Consult the sources listed here for additional examples, including some interesting embellishments. Time-Stamped Dimensions Ralph Kimball and Margy Ross provide a detailed example of a human resources solution that incorporates a time-stamped employee dimension in 8 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). They refer to the time-stamped dimension as a transaction tracking dimension. This chapter suggested that effective and expiration dates can be supplemented with effective and expiration times, if needed. For an example of this technique, see the Kimball and Ross example cited in the previous bullet. If a conformed rollup of a time-stamped dimension is also time-stamped, some unique issues will be faced in its maintenance. I discuss these issues in 8 of Mastering Data Warehouse Aggregates (Wiley, 2006). Type 3 Changes In 4 of The Data Warehouse Toolkit, Kimball and Ross provide an example of a type 3 change in a product dimension. When the department associated with a product changes, there is a need to use the old and new values. This chapter suggested that the type 3 approach can be extended to carry multiple versions of the changed attribute. Kimball and Ross use a sales force realignment scenario to illustrate this technique, complete with four historic versions of the attribute. In 4 of The Data Warehouse Toolkit, they call this technique predictable changes with multiple overlays. Type 1/2 Hybrid Changes Kimball and Ross refer to the type 1/2 hybrid as unpredictable changes with a single overlay. In 4 of The Data Warehouse Toolkit, they provide an example where department name changes can be handled using this approach. They suggest there is a similarity to type 3, but note that their example does not include tracking of the prior value. Kimball, Ross, Thornthwaite, Mundy, and Becker allude to an interesting spin on the type 1/2 hybrid in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008). They imagine a solution where the dimension table records type 2 changes. An outrigger table contains one row for each natural key, recording the current version of the attributes. This technique may be useful if there are several attributes in a table that demand the hybrid approach. Something similar can be achieved by joining a dimension table to an alias of itself using the natural key, and filtering the alias on a current_version flag.
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