Ensemble Modeling Forms (DWH Models)

This is a copy/paste post of Hans Hultgren‘s blog post about the forms of Ensemble Modeling:
https://hanshultgren.wordpress.com/2013/06/16/ensemble-modeling-forms/

Ensemble Modeling Forms: Modeling the Agile Data Warehouse

Ensemble-modeling


Anchor Modeling. Data Vault Modeling. Focal Point Modeling. To name a few. In fact there are dozens of data warehouse data modeling patterns that have been introduced over the past decade. Among the top ones there are a set of defining characteristics. These characteristics are combined in the definition of Ensemble modeling forms (AKA Data Warehouse Modeling). See coverage notes on the Next Generation DWH Modeling conference here (and summary here).

The differences between them define the flavors of Ensemble Modeling. These flavors have vastly more in common than they have differences. When compared to 3NF or Dimensional modeling, the defining characteristics of the Ensemble forms have an 80/20 rule of commonality.

All these forms practice Unified Decomposition (breaking concepts into component parts) with a central unique instance as a centerstone (Anchor, Hub, Focal Point, etc.).
Each separates context attributes into dedicated table forms that are directly attached to the centerstone.
Each uncouples relationships from the concepts they seek to relate.
Each purposefully manages historical time-slice data with varying degrees of sophistication concerning temporal variations.
Each recognizes the differences between static and dynamic data.
Each recognizes the reality of working with constantly changing sources, transformation and rules.
Each recognizes the dichotomy of the enterprise-wide natural business key.
From that foundation of commonality, the various forms of Ensembles begin to take on their own flavors.

While Data Vault is foundationally based on the natural business key as the foundation of the centerstone (Hub), both Anchor and Focal Point center on a unique instance of a concept where the business key is strongly coupled but separate from the centerstone (Anchor, Focal Point).
Both Data Vault and Anchor aim to model Ensembles at the Core Business Concept level while Focal Point tends to deploy slightly more abstracted or generic forms of concepts.
Data Vault and Focal Point utilize forms of attribute clusters (logical combinations) for context while Anchor relies on single attribute context tables.
And there are other differentiating factors as well.

There is one thing that we can all agree on: modeling the agile data warehouse means applying some form of Ensemble modeling approach. The specific flavor choice (Data Vault, Anchor, Focal Point, etc.) should be based on the specific characteristics of your data warehouse program.

* Learn more on Anchor Modeling with Lars Rönnbäck here: Anchor Modeling

More info about Ensemble Modeling: https://hanshultgren.wordpress.com/2012/11/20/ensemble-modeling/








DateTimeOffset to Date Time Offset (Dimension) Identities

These SQL functions might come in handy for your ETL and DWH.
I’m currently designing a DWH which will have three ‘TimeDimensions’:
DateDimension (grain = day)
TimeDimension (grain = second)
TimeZoneDimension (grain = offset in minutes)

Simply said, these functions convert a DATETIMEOFFSET value into three types of integer identity values (which can be used in your dimensions).

DateTimeOffset to Date Time Offset Identities - Results

Want it? 🙂
Download it here:
DateTimeOffset to Date Time Offset Identities – SQL Code