Logical Data Model (LDM)
Purpose: Design the data structure with detail and rigor (still tech-agnostic)
Characteristics:
Audience: Data architects, systems designers, technical leads
Detail Level: Comprehensive structure—defines exactly what data exists
Focus Areas: Entities, attributes, keys, relationships, cardinality, normalization
Technology: Database-agnostic—could be implemented in any system
Tools: ER/Studio, erwin Data Modeler, DbSchema , Lucidchart , UML tools
What It Shows:
Every entity and its attributes
Primary keys and foreign keys
Data types (conceptually—not DBMS-specific)
Relationship cardinality (1:1, 1:N , M:N )
Normalization rules applied
Constraints and business rules
💡 Example:
• Entity: CUSTOMER
o Attributes : CustomerID ( PK ), FirstName , LastName , Email , DateCreated
• Entity: ORDER
o Attributes : OrderID ( PK ), CustomerID ( FK ), OrderDate , TotalAmount
Relationship : CUSTOMER → ORDER ( 1 to Many )
Benefits:
Technology-independent—can map to SQL, NoSQL, etc.
Comprehensive enough for implementation
Catches design flaws before coding
Can be reused across multiple physical implementations
Foundation for database design and documentation
Limitations:
Still missing database-specific details
Doesn't address performance tuning
No information about indexes, partitioning, storage
