Data vault modeling: Difference between revisions

Revision as of 22:36, 2 April 2023

Revision as of 14:32, 22 April 2023

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Another issue to which data vault is a response is that more and more there is a need for complete auditability and traceability of all the data in the data warehouse. Due to [[Sarbanes-Oxley]] requirements in the USA and similar measures in Europe this is a relevant topic for many business intelligence implementations, hence the focus of any data vault implementation is complete traceability and auditability of all information.

''Data Vault 2.0'' is the new specification. It is an [[open standard]].<ref>[[#dvos2|A short intro to#datavault 2.0]]</ref> The new specification consists of three pillars: methodology ([[Software Engineering InstitutInstitute|SEI]]/[[Capability Maturity Model|CMMI]], [[Six Sigma]], [[Systems development life cycle|SDLC]], etc..), the architecture (amongst others an input layer (data stage, called [[persistent staging area]] in Data Vault 2.0) and a presentation layer (data mart), and handling of data quality services and master data services), and the model. Within the methodology, the implementation of best practices is defined. Data Vault 2.0 has a focus on including new components such as [[big data]], [[NoSQL]] - and also focuses on the performance of the existing model. The old specification (documented here for the most part) is highly focused on data vault modeling. It is documented in the book: Building a Scalable Data Warehouse with Data Vault 2.0.

It is necessary to evolve the specification to include the new components, along with the best practices in order to keep the EDW and BI systems current with the needs and desires of today's businesses.