Data vault modeling: Difference between revisions

Revision as of 11:07, 29 March 2023

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[[File:Data Vault Example.png|thumb|upright=1.5|Simple data vault model with two hubs (blue), one link (green) and four satellites (yellow)]]

'''Data vault modeling''', also known as 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> is a [[database]] modeling method that is designed to provide long-term historical storage of [[data]] coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed and [[Resilience (organizational)|resilience]] to change as well as emphasizing the need to [[Audit trail|trace]] where all the data in the database [[Data lineage|came from]]. This means that every [[Row (database)|row]] in a data vault must be accompanied by record source and load date attributes, enabling an auditor to trace values back to the source. The concept was published in 2000 by [[Dan Linstedt]].

Data vault modeling makes no distinction between good and bad data ("bad" meaning not conforming to business rules).<ref>[[#dvsuper|Super Charge your data warehouse]], page 74</ref> This is summarized in the statement that a data vault stores "[[Single source of truth|a single version of the facts]]" (also expressed by [[Dan Linstedt]] as "all the data, all of the time") as opposed to the practice in other data warehouse methods of storing "a [[single version of the truth]]"<ref>[[#rdamhof1|The next generation EDW]]</ref> where data that does not conform to the definitions is removed or "cleansed". A data vault enterprise data warehouse provides both; a single verdion of facts and a single source of truth.<ref>Building a scalable datawarehouse with data vault 2.0, p. 6</ref>