Page 213 - Data Architecture

Chapter 6.1: Introduction to Data Vault 2.0
solid reliable engineering implementations. Data Vault 2.0 offers all of this, including
customer references (some of the largest commercial organizations and government data
stores in the world).

This might sound like overkill; however, the team (once properly trained) can deliver
sprint work products in a one- and two-day life cycle. The solution is foundational and
offers building block components that easily fit together in a standardized fashion.

Accelerating the teams' progress by leveraging automation and workflow process tooling
(specifically with Data Vault 2.0 authorized tools) becomes a must-do.

Today, there are customers around the world whom have implemented petabyte-level
distributed Data Vault 2.0 solutions with some of the latest big data technology. More
information from business to technical and from tooling to data platforms can be found in
the data vault community: http://DataVaultAlliance.com (free to join).

What Is Data Vault 2.0 Modeling?

A Business View

The data vault model is based on a business concept model. Capturing concepts or
elements of the business needs to be unified at a logical level and then mapping those
concepts to the raw data level and the business process levels. The concept model starts
with an individual data item like a customer, product, or service. Then, these concepts are
uniquely identified by business keys that travel across the lines of business (from data

inception to data “death”).

The model separates relationships or associations (links) from identifiers (hubs) from the

descriptive data that change over time (satellites). This allows the model to store
commonly defined data sets mapped to a concept level and ties that data to multiple
business process levels. These business processes are the ones that execute within the
source systems.

By capturing the data set in this manner, the model can easily represent multiple logical
units of work, along with shifting business hierarchies and shifting processes.
Furthermore, the conceptual model can be applied in automation and generation tools,
data virtualization tools, and query tools to better meet the needs of the enterprise.

Because this model (at a build process level) is focused on concepts, it can be split or

213