Graph Data Modeling is for You, if You …
- need to model data for NoSQL such as key / value stores, document databases or graph databases), or, for that matter, SQL
- are working in analytics, big data and/or data science and must prepare data for business
- are a developer, who develop data models as you go
- are an experienced relational data modeler/developer, who thinks, “There must be a better way of doing this"
Most of the content is forward-looking and should appeal to many professionals, regardless of their level of previous engagement with traditional data modeling.
Data Modeling - Time for a Change
In parallel with this, educational psychologists developed concept mapping: a form of concept modeling which today has been successfully adopted by the business rules community.
Adding psychology to the equation means that data modeling is not a done deal. If you are looking for a modern approach to data modeling, keep reading!
NoSQL also needs Data Modeling
Despite the fact that “No” in “NoSQL” stands for “not only,” most people associate it with “Not” SQL. In any case, the absence of schemas does not imply the absence of business requirements or the modeling of these requirements—a major theme of this book. We also will focus on the business requirements of good data modeling even in schema-less contexts.
Spending time on schema qualities means that developers work from sharp definitions, which in turn leads to greater productivity and well-structured applications.
Introducing Graph Data Modeling
These matters and much more, in fact most aspects of modern data modeling, are the themes of this book:
This site gives an introduction to the book and is organized in three parts:
How to explore the business context and map the meaning and the structure.
Business Concept Mapping explained.
Business / conceptual level.
Data Modeling Requirements.
Graph Data Modeling explained.
Logical and physical levels.
The History of Data Modeling