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Slowly changing dimension
In data management and data warehousing, a slowly changing dimension (SCD) is a dimension that stores data which, while generally stable, may change over time, often in an unpredictable manner. This contrasts with a rapidly changing dimension, such as transactional parameters like customer ID, product ID, quantity, and price, which undergo frequent updates. Common examples of SCDs include geographical locations, customer details, or product attributes.
Various methodologies address the complexities of SCD management. Ralph Kimball's Data Warehouse Toolkit has popularized a categorization of techniques for handling SCD attributes as Types 1 through 6. These range from simple overwrites (Type 1), to creating new rows for each change (Type 2), adding new attributes (Type 3), maintaining separate history tables (Type 4), or employing hybrid approaches (Type 6 and 7). Type 0 is available to model an attribute as not really changing at all. Each type offers a trade-off between historical accuracy, data complexity, and system performance, catering to different analytical and reporting needs.
The challenge with SCDs lies in preserving historical accuracy while maintaining data integrity and referential integrity. For instance, a fact table tracking sales might be linked to a dimension table containing information about salespeople and their assigned regional offices. If a salesperson is transferred to a new office, historical sales reports need to reflect their previous assignment without breaking the relationships between the fact and dimension tables. SCDs provide mechanisms to manage such changes effectively.
The Type 0 dimension attributes never change and are assigned to attributes that have durable values or are described as 'Original'. Examples: Date of Birth, Original Credit Score. Type 0 applies to most date dimension attributes.
This method overwrites old with new data, and therefore does not track historical data.
Example of a supplier table:
In the above example, Supplier_Code is the natural key and Supplier_Key is a surrogate key. Technically, the surrogate key is not necessary, since the row will be unique by the natural key (Supplier_Code).
If the supplier relocates the headquarters to Illinois the record would be overwritten:
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Slowly changing dimension
In data management and data warehousing, a slowly changing dimension (SCD) is a dimension that stores data which, while generally stable, may change over time, often in an unpredictable manner. This contrasts with a rapidly changing dimension, such as transactional parameters like customer ID, product ID, quantity, and price, which undergo frequent updates. Common examples of SCDs include geographical locations, customer details, or product attributes.
Various methodologies address the complexities of SCD management. Ralph Kimball's Data Warehouse Toolkit has popularized a categorization of techniques for handling SCD attributes as Types 1 through 6. These range from simple overwrites (Type 1), to creating new rows for each change (Type 2), adding new attributes (Type 3), maintaining separate history tables (Type 4), or employing hybrid approaches (Type 6 and 7). Type 0 is available to model an attribute as not really changing at all. Each type offers a trade-off between historical accuracy, data complexity, and system performance, catering to different analytical and reporting needs.
The challenge with SCDs lies in preserving historical accuracy while maintaining data integrity and referential integrity. For instance, a fact table tracking sales might be linked to a dimension table containing information about salespeople and their assigned regional offices. If a salesperson is transferred to a new office, historical sales reports need to reflect their previous assignment without breaking the relationships between the fact and dimension tables. SCDs provide mechanisms to manage such changes effectively.
The Type 0 dimension attributes never change and are assigned to attributes that have durable values or are described as 'Original'. Examples: Date of Birth, Original Credit Score. Type 0 applies to most date dimension attributes.
This method overwrites old with new data, and therefore does not track historical data.
Example of a supplier table:
In the above example, Supplier_Code is the natural key and Supplier_Key is a surrogate key. Technically, the surrogate key is not necessary, since the row will be unique by the natural key (Supplier_Code).
If the supplier relocates the headquarters to Illinois the record would be overwritten: