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Data Modeling Training

 

Simsion & Associates has a high-regarded program of data-modeling training from introductory to master-class level based on the book Data Modeling Essentials.

Please note: Graeme Simsion no longer delivers these courses personally. Instructors are experienced professionals who have worked closely with Graeme Simsion in the past.

Typical programs include:

Data Modeling Essentials

I believe two to three days is a suitable length for an in an introductory course.  There is a limit to how much material people can absorb (particularly if they lack any experience in the field), and many courses I’ve seen have impressive outlines, but don’t really achieve learning goals.  My basic course is two days.  If extending it to three days, I focus on giving the attendees more experience and confidence with the basic techniques rather than introducing more concepts.

Data modelling is a design skill, and people learn it best by doing it.  Accordingly, I use a true “workshop” format built around exercises and case studies.  I cover:

Part I – the Basics

What data modelling is and where it fits
The Relational Model and basics of good structure, including normalisation to BCNF
E-R and UML notations
Subtypes and supertypes
Good practice in attribute and key definition

Part II – Putting it Together

A framework for data modelling
Understanding user requirements
Developing a conceptual model
From conceptual model to logical model
The physical model and performance issues

This course broadly follows the structure of the first two parts of Data Modelling Essentials, Third Edition by Simsion (me) and Witt, and the book itself serves as course notes – a much more satisfactory and comprehensive  “take away” package than Powerpoint notes. 

Data Modeling Masterclass

Part I Introduction - Data Modeling in today’s world

  1. The value of data modeling
  2. Where does data modeling fit?
  3. Role and responsibilities of the data modeler
  4. Choice and creativity in data modeling
  5. What makes a good data model? – Quality criteria and trade-offs
  6. What makes a good data modeler?
  7. Data modeling and object oriented techniques
  8. Data modeling and extended RDBMSs
  9. Data modeling and packaged software

Part II Tools and Techniques

Working with Generalization

  1. Subtypes and Supertypes
  2. Exploring alternatives
  3. Tradeoffs and implementation options
  4. Generalization of attributes

Attributes and Columns

  1. Getting attributes under control
  2. Domains and types
  3. Dealing with complex attributes

Keys and identity

  1. What makes a good primary key?
  2. Issues with structured keys
  3. Independent and Dependent Entities
  4. Weak and Regular Keys
  5. Surrogate keys
  6. Issues with foreign keys (split, derivable, multiple)

Normalization Revisited

  1. Normalization – myths and misunderstandings
  2. Beyond Third Normal Form
  3. Problems that normalization won’t fix

Alternatives and Extensions to Notations

  1. Practical issues with notations
  2. UML vs E-R and other alternatives
  3. Transferability

The Time Dimension

  1. Key principles
  2. The audit trail approach
  3. The snapshot approach

Part III  Data Modeling from End-to-End

The Data Modeling Project

  1. Stages and Deliverables – defining the boundaries
  2. A project planning check list for data modelers
  3. Choosing the right tools
  4. Managing change

Understanding User Requirements

  1. How do we document requirements?
  2. Effective interviews and workshops
  3. Using a class hierarchy

Conceptual Modeling

  1. The conceptual modeler’s tool-kit
  2. Business rules
  3. Using patterns
  4. Verifying the model with users

Logical Database Design

  1. Conceptual to Logical – understanding and managing the mapping
  2. Implementing Subtypes
  3. Dealing with Classifications
  4. Common problems and solutions

Physical Design and the Data Modeler

  1. Overview of tradeoffs
  2. Making an effective contribution to the physical design phase

 

Part IV Enterprise Modeling and Data Management (Optional)

  1. The roles of the enterprise model
  2. Traditional approaches to data management
  3. Reinventing data management