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Every record that is not new may contain type 1 changes. When a type 1 attribute changes on the source side, it is overwritten in the data warehouse. To identify a type 1 change, an incoming record is matched to the current record in the warehouse for the natural key value. The type 1 attributes are compared. If they do not match, a type 1 change has been identified. In Figure 17-2, this comparison is performed in step 1.5. In the example data from Figure 17-3, each record with a natural key that is present in the dimension table must be checked for type 1 changes. This requires finding the current record for the natural key value and comparing the type 1 attributes. This can be accomplished via a lookup as well. If the type 1 attributes do not match, the SKU has undergone a type 1 change. The second source record, SKU B-3691, has a type 1 attribute value that does not match the current corresponding record in the product table. The product_name has changed from Twine Spool to TwineSpool 12. The dimension table must be updated with this change; processing flows through the branch of the diagram labeled B. TIP Type 1 changes can be identified by comparing the source record to the current dimension record for the natural key value. If any type 1 attributes do not match, a type 1 change has occurred. Type 1 changes must be applied to all corresponding warehouse records with the same natural key. There may be more than one dimension row corresponding to the source record that has changed, because it may already have undergone type 2 changes. In step 1.6

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of Figure 17-2, type 1 changes are applied to all existing dimension table records that share the natural key value not the current version only. In the case of SKU B-3691, the dimension table contains three corresponding records. Past changes to the SKU s brand_manager, a type 2 attribute, have resulted in numerous versions of this product. For all three versions of B-3691, the product_name must be updated. Updating only the current version would be a serious error, since all versions may be connected to facts in the fact table. TIP It is necessary to apply a type 1 change to every row in the dimension table that shares the same natural key. Applying it only to the current version results in inconsistent facts. Once the type 1 change has been applied, processing for the source record is not complete. It must also be checked to see whether any type 2 changes have occurred. Likewise, any records that did not contain a type 1 change must also be checked for type 2 changes. Both branches out of step 1.5 in Figure 17-2 therefore lead into step 1.7. This example conveys the basic principle behind type 1 processing, but reality can be far more complex. As noted in 3, a type 1 change to an attribute that is not completely dependent on the natural key will require more complex processing. See the sidebar Type 1 Complexity for more information.

When a dimension table contains a type 1 attribute that is not fully dependent on the natural key, simple comparison to the existing dimension record is not sufficient to determine if an update is needed. For example, suppose the table in Figure 17-3 also contains a brand_name column. Brand_name is determined by brand_code. It is therefore only partially dependent on the product s natural key. Suppose that brand_name is designated as a type 1 attribute. If the brand_name of an existing product changes, this may or may not call for an overwrite. It is necessary to determine if the brand_code, a type 2 attribute, has also changed. If so, the product has been assigned a new brand, and a new row is required. There has been no type 1 change. If the brand_code has not changed, however, the new brand_name is indicative of a type 1 change. It is important that dimensional designers be cognizant of the increased ETL complexity that will be necessitated by attributes like this. In order to avoid this situation, designers may choose to forgo the use of type 1 attributes that do not correspond to the grain of the dimension. As noted in s 3 and 15, type 1 attributes can also increase the complexity involved in maintaining aggregates or cubes. If the use of such an attribute is necessary, design documentation must carefully spell out the dependency of the attribute so that ETL developers can process it correctly. Notice that in a snowflake design, the ETL challenge is slightly mitigated because the attributes of brand will be in a separate table.

5,000

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