Slowly Changing Dimensions
Slowly Changing Dimensions, abbreviated SCD, is the family of techniques for handling changes to dimension table attributes in a
data warehouse over time, a problem Ralph Kimball formalized into six types that have remained the standard reference for 25 years. The challenge: a customer's address changes, a product moves to a new category, an employee transfers departments — how should historical fact records relate to dimension records when the dimension has changed? Type 0 (Retain Original): never update the attribute; first value sticks forever. Type 1 (Overwrite): update in place, losing history; appropriate for corrections and typo fixes. Type 2 (Add Row): insert a new row in the dimension with the new value and effective dates, preserving history; fact records relate to the row that was current when the fact occurred; the most common production pattern. Type 3 (Add Column): keep a "current" and "previous" column on the same dimension row; appropriate for tracking one prior value. Type 4 (History Table): keep current state in the main dimension table, archive prior states in a parallel history table. Type 6 (Hybrid): combine Type 1, 2, and 3 — overwrite some attributes, version some attributes, and track current values alongside historical. The practical implementation: SCD Type 2 typically uses surrogate keys (the dimension table's primary key is a synthetic integer, the natural key is a separate column that may have multiple rows over time), effective_from and effective_to columns or is_current flags, and merge logic that closes the previous version and inserts a new one on attribute change. Modern transformation tools (dbt has scd_type_2 snapshot macros, Spark and Databricks have Delta Lake merge syntax) make SCD implementation a configuration rather than a programming exercise. For Digital Experience Platforms, SCD is critical because the served experience must accurately reflect "what was true when the user did X" rather than only "what is true now" — historical segmentation, attribution analysis, and compliance reporting all depend on it.
Historical accuracy underpins the Magic Quadrant DXP: Centralpoint maintains version-history on dimensional data the way it has maintained content version-history for 25 years — preserving the truth of "what the customer saw on what date" is foundational to the Gartner Magic Quadrant DXP positioning. SCD discipline runs on-premise, lineage is audit-graded, and historically-accurate experiences deploy through one line of JavaScript.
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