How can you choose between logical and physical data modeling?

1 What is logical data modeling?

Logical data modeling is the process of creating a conceptual representation of your data, without considering the technical details of how it will be stored or accessed. Logical data models focus on the entities, attributes, and relationships that define your data, as well as the business rules and constraints that govern it. Logical data models are independent of any specific database system, platform, or technology, and can be used to communicate with various stakeholders, such as business analysts, developers, and end-users.

2 What is physical data modeling?

Physical data modeling is the process of translating your logical data model into a specific database schema, taking into account the technical aspects of how your data will be stored and accessed. Physical data models include details such as table names, column names, data types, indexes, keys, constraints, and triggers. Physical data models are dependent on the chosen database system, platform, and technology, and can be used to implement and optimize your data structures and processes.

3 Why are both types of data modeling important?

Both logical and physical data modeling are important for different reasons. Logical data modeling helps you understand and validate your data requirements, as well as identify any gaps, inconsistencies, or redundancies in your data. Logical data modeling also helps you ensure that your data supports your business goals and needs, and that it is consistent and reliable across different applications and systems. Physical data modeling helps you implement and optimize your data structures and processes, as well as enhance the performance, security, and scalability of your data. Physical data modeling also helps you adapt your data to the specific features and limitations of your database system, platform, and technology.

4 How to choose between logical and physical data modeling?

The choice between logical and physical data modeling depends on several factors, such as the scope, complexity, and purpose of your project, the availability and expertise of your resources, and the expectations and preferences of your stakeholders. Generally speaking, logical data modeling is more suitable for projects that require a high-level, abstract, and flexible view of your data, while physical data modeling is more suitable for projects that require a detailed, concrete, and optimized view of your data. However, in most cases, you will need to use both types of data modeling, as they complement each other and provide different perspectives and benefits for your data management.

5 How to balance logical and physical data modeling?

Balancing logical and physical data modeling can be challenging, as they involve different levels of abstraction, detail, and complexity. Nevertheless, there are some best practices to help you achieve a good balance. To start, begin with a logical data model which will define and validate your data requirements, as well as provide a common language and understanding among your stakeholders. Additionally, use a physical data model as a guide for implementing and optimizing your data structures and processes, while adapting to the specific database system, platform, and technology. Additionally, keep your logical and physical data models aligned and consistent by using tools such as data mapping, data lineage, and data governance. Furthermore, review and update your data models regularly to accommodate changes in your data requirements, business needs, or technologies. Finally, utilize feedback and testing to evaluate and improve your data models.

6 How to learn more about data modeling?

If you want to learn more about data modeling, there are plenty of resources available, such as books like Steve Hoberman's Data Modeling Made Simple and Graeme Simsion and Graham Witt's Data Modeling Essentials. You can also take courses and workshops like Coursera's Data Modeling and Relational Database Design or Udemy's Data Modeling for Business Analysts, or even earn a Certified Data Modeler certification from DAMA International. Additionally, you can join communities like the Data Modeling Community on LinkedIn, ask questions on the Data Modeling Forum on Stack Overflow, or attend data modeling conferences organized by various organizations.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?