based and data-driven.

What's So Important About Patterns?

Patterns make life easier. In the enterprise BI world, patterns enable automation and
generation while reducing errors and error potential. Patterns are the heartbeat of the
Data Vault 2.0 system of business intelligence. Once the team has accepted the principles
that building a data warehouse or BI system is the same as building software, it is possible
to extend that thought to pattern-driven design.

A pattern is a recurring solution to a problem within a context.

Christopher Alexander

Think about it, how often have IT teams said they “need one pattern for loading history,
one pattern for loading current, and yet another pattern for loading data in real-time?”
Other teams have made the statement that “this part of the data model works for these
reasons, and this other part of the data model was constructed differently because of
exceptions to the design rules.” Much of this contributes to what is commonly called
conditional architecture.

Conditional architecture is defined as a pattern that only works for a specific case often
based on an IF condition. When the case changes (i.e., volume or velocity or variety) the
boundaries, the architecture needs to change. Thus, conditional architecture is born.

Conditional architecture is a horrible way to construct/design an enterprise BI solution.
The reason is because when volume grows and timelines (velocity changes) shrink, then
reengineering takes place in order to rectify or correct the design. This leads to a solution
that continues to cost more and more money and take longer and longer to change. In
other words, it leads to a brittle architecture over time. This (especially in a big data
solution) is a very bad construct.

At some point, the business can’t or won’t be able to pay for the reengineering costs. This
is typically when the solution is torn down and rebuilt (green-field approach). With the
patterns of Data Vault 2.0 (both architecture and implementation), rearchitecture and
reengineering are avoided for 98% of the cases where volume grows, velocity changes,
and variety increases.

Having the right pattern/design based on mathematical principles means that the team no

Chapter 6.5: Introduction to Data Vault Implementation