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Conceptual vs. Logical vs. Physical Data Models

It has become common to speak of levels of data models.

Conceptual is commonly thought of as a high-level, enterprise-wide, abstract data model.  The conceptual model is the one to present to executives for an initial understanding of the data model.  It may also be a preliminary model to which detail is added as the modeling process progresses.

Physical, at the bottom, is how the data is stored in a database implemented in some database management system (DBMS) or NoSQL tool, its physical realization. 

Logical is something in between, adding detail to the conceptual model but free of physical implementation details which do not contribute to the logical understanding of the model.  Sometimes a Relational or Entity-Relationship (ER) model is considered the logical model.

In preparation for this presentation, attendees are encouraged to view the 35 minute video from David Hay entitled Kinds of Data Models -- and How to Name Them.”  (Search on YouTube or go to:   http://bit.ly/QwSkOY  ).  David is one of the pre-eminent thinkers and authors on data modeling.  This video captures the essence of popular thinking regarding the characterization and distinctions between conceptual, logical, and physical data models.  My presentation could be considered a rebuttal or contrary view to the one promulgated by David Hay. 

What you'll get from this presentation:

* Insight into what is meant by a conceptual model.

* Managing multiple conceptual models, keeping them up to date and in sync with your logical models.

* The methods and guidelines which drive the logical data modeling process.  Why are all data models logical?  If all data models are logical, what makes a model conceptual?

* What does it mean to say that our data models should be "outward facing"?

* The first things we should be looking for in the data modeling process.

* When does a logical model become a physical model; where is the separation?

* Conceptual models in a NoSQL environment.

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