In Doubt about which Kind of Modeling You need?

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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

Graph data modeling is a technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs.
Originally data modeling originated in the business realm. But with the advent of the relational model and normalization, data modeling became a more technical part of software engineering.

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

One of the major differences between relational and non-relational modeling is the absence of schemas (in the form of pre-defined metadata) existing alongside the data. Once upon a time, names and data types were determined once and rarely changed over time. The new world, with its general lack of schemas or possibly self-describing data, changes this.

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

In the graph world the “property graph” style of graphing makes it possible to rethink the representation of data models. Graph Data Modeling sets a new standard for visualization of data models based on the property graph approach. Property graphs are graph data models consisting of nodes and relationships. The properties can reside with the nodes and / or the relationships. Accordingly the property graph model consists of just 3 simple types, as laid out in this property graph representation of the meta model:
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This diagramming style is very close to what people - by intuition - draw on whiteboards. Rather than modeling data architecture on complex mathematics, we should focus on the psychology of the end user. If we do so, then engineering could be replaced with relevant business processes. In short, to achieve more logical and efficient results, we need to return to data modeling’s roots.

These matters and much more, in fact most aspects of modern data modeling, are the themes of this book:
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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.

Visualizing structure.

Data Modeling Requirements.

Graph Data Modeling explained.

Logical and physical levels.


The History of Data Modeling

The cover page of the book is by Manfred Christiansen and depicts the data modeler as an explorer in a complex world.
February 2019: New book from Thomas Frisendal. How to recycle / reverse engineer legacy data models into graph data models. Powerful tool, which Includes complete scripts for doing the transformations:
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There is much more about property graphs for data modeling in the book about Graph Data Modeling:
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