Data Modeling Patterns 101

Data Modeling Patterns 101

Data Modelling is a systematic way that creates a visual representation to connect different data points with each

Data Modelling is a systematic way that creates a visual representation to connect different data points with each other.

Data modeling helps determine the different data in a system and establish relevant connections between them. It creates conditions that paves way for data analysis which in turn improves the performance by organizing data structures and relationships.

In simple words, data modeling is nothing but a blueprint for a data structure that helps in evaluating various business requirements and structures.

Significance of Data Modeling Patterns:

  • Patterns provide composite building blocks as they can be used for multiple purposes.
  • Patterns are very pivotal as they make data modeling efforts effective and efficient.
  • Patterns allow the developers to build on others’ accomplishments, which reduces errors.

Various Patterns of Data Modeling

The book “Data Model Patterns: Conventions of Thought” by David Hay, demonstrates various common aspects in businesses such as employees, customers, relationships, products, procedures, etc. It states that structures in organizations are similar, or have similar components that derive the basic components in the patterns.

Data modelers are well aware of the different patterns and know, how these patterns create structures that enable the data to relate and communicate with their complementary data points to create pivotal information.

Here are a few standards and advanced data model patterns that can be applied to suitable business situations:

The Standard Model

This model caters to the basic aspects of a business that includes the relation between:

  • People and organizations as well as the hierarchy
  • Different type of products
  • Agreement between different parties
  • Standard activities
  • Geographic locations


This model helps define and differentiate a particular product type. It is helpful in the case of businesses consisting of multiple products, services, and solutions that need to be set apart from each other depending on the unit of measurements or distinct parameter that is assigned to the product type.

For example, the parameter “capacity” might be used to describe a boiler, while the parameter “interest rate” might be used to describe a savings account, proving that one of the advantages of this approach is that it caters to various industries that heavily depend on the data and nature of their products, services or solutions.

Also Discover: 6 Best Free & Open Source Data Modeling Tools

Building with Patterns

While designing an application, developers often stumble upon an issue where they need to model their data that will cater to both the structure of the organization, the product, as well as the consumers or clients.

The Schema Versioning Pattern:

With the changing structures, requirements, and objectives, data models also need to be updated in a certain manner that compliments an application.

The implementation of this pattern is relatively easy. The pattern allows documents to exist in the same database collection regardless of having different fields or different types in the same field as MogoDB’s polymorphic aspect.

The Document Versioning Pattern:

This pattern keeps a version for the historic data that is available when required.

This pattern utilizes one system for the documents that change and one system to keep the history of previous documents.

Document versioning pattern avoids the usage of multiple systems by managing current documents in one and keeping the history in another, which is easily accessible.

The Preallocation Pattern:

This pattern initially creates an empty structure, which would be filled in the future, through memory allocation to avoid performance issues.

This pattern allows a known structure to build itself in a development cycle. Although having lost its generic use case, this pattern is still utilized in some situations.

The Tree Pattern:

This pattern resolves the queries of a hierarchal data structure.

As for many patterns, there is often adaptability between simplicity and performance, although in the case of the tree patterns you get better performance by avoiding multiple joins while managing the update on its graph.

Concluding Words

A lot of businesses choose to custom build software according to the unique business aspects as well as the vision they have for the venture where patterns are extended to create an appropriate model and be versatile in nature.

One may stick to a certain pattern while creating a Data Model or utilize various permutations and combinations that are complementary to one another, yet understanding that this is the core from where they would build up an entire empire.

Youn may also like to read: Top 15 Data Masking Tools – 2020

About Jason Hoffman

I am the Director of Sales and Marketing at Wisdomplexus, capturing market share with E-mail marketing, Blogs and Social media promotion. I spend major part of my day geeking out on all the latest technology trends like artificial intelligence, machine learning, deep learning, cloud computing, 5G and many more. You can read my opinion in regards to these technologies via blogs on our website.