align-toparrow-leftarrow-rightbackbellblockcalendarcamerachatcheckchevron-downchevron-leftchevron-rightchevron-small-downchevron-small-leftchevron-small-rightchevron-small-upchevron-upcircle-with-crosscrosseditfacebookglobegoogleimagesinstagramlocation-pinmagnifying-glassmailmoremuplabelShape 3 + Rectangle 1outlookpersonplusImported LayersImported LayersImported Layersshieldstartwitteryahoo

Data Vault is a next-generation technique for (data)modeling the Enterprise Data Warehouse (EDW). It provides full historical storage and integration of data loaded from multiple (disparate) applications and systems and other sources of information and is designed for large scale, rapidly changing (agile!) and industry standard Data Warehouse and Business Intelligence platforms. Stated as the 'optimal choice for modeling the EDW in the DW2.0 framework' by Bill Inmon it fits in perfectly with other existing approaches such as the well-known Kimball methodology. Data Vault provides the EDW architecture, the modular components and built-in audit trail to meet these ambitions. Due to the way Data Vault defines the entity types the EDW becomes a fully standardised and asynchronous environment where data can be added and loaded at any time. If you are looking for 100% of the data 100% of the time Data Vault is the technique to use. Dan Linstedt - the father of Data Vault - defines the Data Vault as ‘a detailed historically oriented, uniquely linked set of normalised tables that support one or more areas of the business’. In other words; it is a semi-normalised model where key distribution, relationships and descriptive data are separated. The historical data is denormalised to some extent, labeling it as a hybrid modeling approach for data modeling. It is the way Data Vault uses the archetypical entities to allows for a natural, almost organic, controlled expansion of the model that makes it such a powerful technique. Data Vault's architecture specifically caters for today’s complex Data Management needs, focusing on dealing with issues such as auditing, tracing of data, loading speed and resilience to change. DW and BI solutions more than ever need infrastructure that can handle massive data volumes, be responsive to change and provide data auditability and traceability without massive effort. Data Vault can scale out for very large implementations while retaining flexibility to change. Everyone is invited to join this group and discuss possibilities, implementation, case studies and in general what everyone would like to know about Data Vault.

Join us and be the first to know when new Meetups are scheduled
Log in with Facebook to find out
By creating a Meetup account, you agree to the Terms of Service


  • Aug 12, 2014 · 8:00 AM

    TDWI Event - CGU's data vault use case

    9 Data Vaulters

    Some of you have indicated that they are intested in hearing about use cases of DV being implemented in a large organisation, so I wanted to draw your attention to the... Learn more
  • Jul 24, 2013 · 5:30 PM

    Inaugural DV Meetup!

    4 Data Vaulters

    This first kick-off session of the local Melbourne DWH modelling / Data Vault community focuses on the basics of Data Vault including a brief introduction covering the... Learn more

What's new

Founded Jun 2, 2013

People in this
Meetup are also in:

Sign up

Meetup members, Log in

By clicking "Sign up" or "Sign up using Facebook", you confirm that you accept our Terms of Service & Privacy Policy