Data Modeling - Time for a Change
Originally data modeling was business oriented. 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.
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!
Graph data modeling is a technique superior to traditional data modeling for both relational and graph, document, key-value, leveraging cognitive psychology to improve big data designs.
In parallel with this, educational psychologists developed concept mapping.
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!
Graph data modeling is a technique superior to traditional data modeling for both relational and graph, document, key-value, leveraging cognitive psychology to improve big data designs.
Aside: There are a number of signals that indicate the changes in this space:
If anybody has given the legacy ways an irreversible "enough is enough", then his name is Dave McComb. See the reference to his book "Software Wasteland" here on this page.
- Agile everything
- The rise of Knowledge Graphs
- SQL is going to add a property graph extension and a new graph query language
- The tried and trusted RDF / OWL stack is being extended in the direction of property graphs
- GraphQL
- …
If anybody has given the legacy ways an irreversible "enough is enough", then his name is Dave McComb. See the reference to his book "Software Wasteland" here on this page.
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.
This data model is very close to what people - by intuition - draw on whiteboards. Rather than modeling data as formal logic, 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:
Graph Data Modeling is for You, if You …
- need to model data for graph databases, or, for that matter, SQL (yes, indeed)
- work in analytics, big data and/or data science and must visualize data structures
- develop data models as you go
- think “There must be a better way than classic data modeling"
There are 3 ways to learn more about graph data modeling
1: Online learning plan from Dataversity;
2: A book covering most aspects of modern data modeling
The cover page of the book is by Manfred Christiansen and depicts the data modeler as an explorer in a complex world.
3: This website gives you an introduction only and is organized in three parts:
How to explore the business context and map the meaning and the structure.
Business Concept Modeling explained.
Business / conceptual level.
Visualizing structure.
Data Modeling Requirements.
Graph Data Modeling explained.
Logical and physical levels.
Miscellaneous:
The History of Data Modeling
The guy behind this site is Thomas Frisendal:
