Description: A logical database is an abstract representation of the structure of a database, focusing on the data and its relationships without considering how it is physically implemented. This approach allows database designers to define the organization of data, tables, columns, and relationships clearly and understandably. The logical database acts as a bridge between the conceptual model, which describes an organization’s information requirements, and the physical model, which deals with the actual implementation in a database management system (DBMS). Key features of a logical database include implementation independence, meaning that changes in the physical structure do not affect the logical data, and normalization, which aims to reduce redundancy and improve data integrity. This approach is fundamental in the design of relational databases and is widely used in various database systems, where the structure of data can be defined logically before their physical storage. In summary, the logical database is essential for ensuring that data is managed efficiently and effectively, facilitating the understanding and maintenance of information over time.
History: The concept of logical database emerged in the 1970s with the advent of relational database models proposed by Edgar F. Codd. His work laid the groundwork for the creation of database management systems that separate data logic from physical storage, allowing for greater flexibility and efficiency in information management.
Uses: Logical databases are primarily used in the design of database management systems, allowing developers and administrators to define the data structure clearly and organized. They are also fundamental in data normalization, which helps reduce redundancy and improve information integrity.
Examples: A practical example of a logical database can be found in the design of a customer management system, where tables for customers, orders, and products are defined, establishing relationships between them. In various database systems, the structure of data can be logically designed before being stored, allowing for efficient data management.
