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
  • Attributes: CustomerID (PK), FirstName, LastName, Email, DateCreated
  • Entity: ORDER
  • 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

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