Logical Data Modeling with Akwatype

Logical Data Modeling with Akwatype

Logical data modeling is an intermediate step between conceptual modeling and technical
implementation. It translates the abstract elements of the conceptual model into a detailed
structure, specifying data types, relationships and constraints. Akwatype offers a powerful
framework for building a rigorous logical model, which serves as the basis for physical design
and implementation.


What is Logical Data Modeling ?


Logical modeling represents an organized, standardized data structure, without regard to
specific storage or performance details. It precisely defines the data types (text, numeric,
Boolean, etc.) for each attribute, as well as the relationships between entities (cardinality,
aggregation/composition, etc.). To take things even further, Akwatype makes it easy to add your
own metadata to the model.


The logic model is independent of future technical implementation.


Objectives of Logical Data Modeling in Projects


Logical modeling aims to formalize data in a structured and precise way to guarantee its
integrity. It helps identify constraints, such as primary and foreign keys and uniqueness rules, to
secure and validate information. This step reduces the risk of errors in later phases of the
project, and creates a solid foundation for the generation of physical models.


Logic Model Creation Process


1. Definition of Attributes and Data Types : For each entity in the conceptual model,
specify the data types (text, number, date, etc.) of the attributes, taking into account
business requirements.


2. Identify Primary and Foreign Keys: Assign primary keys to ensure the uniqueness of
records, and define foreign keys to establish dependencies between entities.

Note : Foreign keys are automatically deducted by Akwatype when generating SQL DDLs from
links and primary keys.


3. Defining Relationships and Constraints: Establish precise relationships (one-to-one,
one-to-many, many-to-many) and include referential integrity constraints to maintain data
consistency.


4. Model Validation: Review the logic model to ensure that it covers all business
specifications.

 

Example of a logic model :

 

This simple model describes a training management system. It is made up of courses, training
sessions and participants. Each course covers a specific topic, can be linked to reference
books, and is divided into several sessions. Sessions are associated with a trainer, and
participants can register for training sessions.


Sharing descriptions is key to the process of building and validating the model, and Akwatype
allows great flexibility in the generation of support materials for the various project stakeholders.

 

Conclusion


Logical modeling is an essential link in data structuring, connecting the conceptual vision to the
physical implementation. Using tools such as Akwatype, it becomes easier to formalize data
while ensuring its integrity and consistency. A well-designed logical model facilitates the
transition to the physical model, guaranteeing a robust, future-proof data system.

Model your data as early as possible
for effective design.

Structure your data modeling from the start of your projects to communicate, describe your workflows, and empower your development teams.