The Data Warehouse Toolkit¶
Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Denormalization
- Database design. Materialized views, summary tables, denormalized read models, and embedded documents are the classic case
This sourceCanonical treatment of dimensional modeling, denormalized star schemas, and summary/materialized tables that trade write-side synchronization for read-side speed.
- Database design. Materialized views, summary tables, denormalized read models, and embedded documents are the classic case
Domain-specific¶
Mechanisms¶
- Aspect Lifecycle Register
- Modeled well, it behaves like a Type 2 slowly-changing dimension: each change is a new dated version, and no history is silently lost.
This sourceDescribes Type 2 slowly changing dimensions as adding a dated row for each change so prior history remains available.
- Modeled well, it behaves like a Type 2 slowly-changing dimension: each change is a new dated version, and no history is silently lost.
- Coarse View / Fine View Toggle
- It also invites inconsistent roll-ups when the coarse level is allowed to drift from the sum of its parts, so a number shown at the top no longer survives a drill-down — a failure that erodes trust fast because it looks like a data error.
This sourceKimball's dimensional-modeling guidance treats aggregates as consistent rollups of atomic facts and preserves drill-down to detailed data.
- It also invites inconsistent roll-ups when the coarse level is allowed to drift from the sum of its parts, so a number shown at the top no longer survives a drill-down — a failure that erodes trust fast because it looks like a data error.
- Dimensional Star Schema
- Handling history badly is a subtler trap: overwrite a dimension and you silently rewrite the past, so genuine "as-was" reporting needs an explicit slowly-changing-dimension policy
This sourceExplains that Type 1 dimension overwrites destroy history and that history-preserving slowly changing dimension techniques are required for point-in-time reporting.
- Handling history badly is a subtler trap: overwrite a dimension and you silently rewrite the past, so genuine "as-was" reporting needs an explicit slowly-changing-dimension policy
Verification¶
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Does it back the claim? Not recorded. Neither this nor any other of the 5 citations of this work carries a recorded support check.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://www.wiley.com/en-us/The+Data+Warehouse+Toolkit%3A+The+Definitive+Guide+to+Dimensional+Modeling%2C+3rd+Edition-p-9781118530801 ×2
- https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/books/data-warehouse-dw-toolkit/ ×1
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