Data vault modeling: Difference between revisions

Revision as of 14:58, 11 September 2024

Revision as of 07:59, 2 October 2024

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{{Original research|date=August 2019}}

In its early days, Dan Linstedt referred to the modeling technique which was to become data vault as ''common foundational warehouse architecture''<ref>Building a scalable datawarehouse with data vault 2.0, p. 11</ref> or ''common foundational modeling architecture''.<ref>Building a scalable datawarehouse with data vault 2.0, p. xv</ref> In [[data warehouse]] modeling there are two well-known competing options for modeling the layer where the data are stored. Either you model according to [[Ralph Kimball]], with conformed dimensions and an [[Enterprise bus matrix|enterprise data bus]], or you model according to [[Bill Inmon]] with the database [[normal forms|normalized]]<ref>{{CitationCite web needed|date=August2020-02-03 2019|title=Data Warehouse Concepts: Kimball vs. Inmon Approach |url=https://www.astera.com/type/blog/data-warehouse-concepts/ |access-date=2024-10-02 |website=Astera |language=en-US}}</ref>. Both techniques have issues when dealing with changes in the systems feeding the data warehouse{{Citation needed|date=August 2019}}. For conformed dimensions you also have to cleanse data (to conform it) and this is undesirable in a number of cases since this inevitably will lose information{{Citation needed|date=August 2019}}. Data vault is designed to avoid or minimize the impact of those issues, by moving them to areas of the data warehouse that are outside the historical storage area (cleansing is done in the data marts) and by separating the structural items (business keys and the associations between the business keys) from the descriptive attributes.

Dan Linstedt, the creator of the method, describes the resulting database as follows: