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Chapter 6.1: Introduction to Data Vault 2.0
divided into parallel work streams. The model can be built incrementally over time with
little to no reengineering efforts when change arrives. The model can be automatically
generated (with human input around the concepts and business keys), to expedite and
accelerate the process.
A Technical View
The data vault modeling is a hybrid approach based on third normal form and
dimensional modeling aimed at the logical enterprise data warehouse. The data vault
model is built as a ground-up, incremental, and modular models that can be applied to big
data, structured, and unstructured data sets.
DV2 modeling is focused on providing flexible, scalable patterns that work together to
integrate raw data by business key for the enterprise data warehouse. DV2 modeling
includes minor changes to ensure the modeling paradigms can work within the constructs
of big data, unstructured data, multistructured data, and NoSQL.
Data Vault Modeling 2.0 changes the sequence numbers to hash keys. The hash keys
provide stability, parallel loading methods, and decoupled computation of parent key
values for records. There is an alternative for engines that hash business key values
internally—the option of utilizing the true business keys as they are, without sequences or
hash surrogates. The pros and cons of each technique will be detailed in the data vault
modeling section of this chapter.
How Is Data Vault 2.0 Methodology Defined?
A Business View
The methodology utilizes best practices from software development best practices such
as CMMI, Six Sigma, TQM, Lean Initiatives, and cycle time reduction and applies these
notions for repeatability, consistency, automation, and error reduction.
DV2 methodology focuses on rapid sprint cycles (iterations) with adaptations and
optimizations for repeatable data warehousing tasks. The idea of DV2 methodology is to
enable the team with agile data warehousing and business intelligence best practices.
DV2 encompasses methodology as a pillar or key component to achieve the next level of
maturity in the data warehousing platform.
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