The following is the table of contents for Practical Data Modeling (working title), my new book. It is likely to evolve as I write it. As new chapters are added, they’ll be linked here so you can quickly find what you’re looking for.
Please note that reading these chapters will require a subscription to Practical Data Modeling. This is because I’m a solo writer, and writing helps me earn a living. If you’re a subscriber, you’ll get a free digital version of the final book. I’ll also send out some signed physical copies to some lucky people. You’ll also get access to exclusive community discussions and events, discounts on workshops and training, and much more. Thanks so much for your support!
The final book will be available as a physical paperback (possibly hardcover), digital download, and audiobook. I expect the book to be published around early Fall 2024, with the audiobook coming later.
Thanks,
Joe Reis1
Part 1 - Motivations for Data Modeling
Chapter 1 - What is (and isn’t) Data Modeling?
Chapter 2 - What is this all for? - out mid May 2024
Chapter 3 - History Lessons - out end of May 2024
Chapter 4 - Mixed Model Arts - out early June 2024
Part 2 - Doing Data Modeling
Chapter 5 - Core Components of Data Models
Chapter 6 - Building and Maintaining a Data Model
Chapter 7 - Operational Data Modeling
Chapter 8 - Analytical Data Modeling
Chapter 9 - Machine Learning and AI Data Modeling
Part 3 - Other Important Considerations
Chapter 10 - Architecture and Systems
Chapter 11 - People and Organizations
Chapter 12 - The Future of Data Modeling
Chapter 13 - Conclusion
Plus - Bonus chapters and articles for paying subscribers!
updated 4/27/2024
What are the thoughts on like, examples of different data modelling techniques? Feels like going into the intricacies of something like Data Vault, for example, would be out of scope (fair) but an example or high level explanation might be cool (with examples).
And perhaps a section on what *not* to do ("How to avoid chaos"). Otherwise can't wait
Looks good. Especially interested in the History chapter and the Analytical modeling chapters.