Data Modeling Training

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Course Description

Data modeling (data modelling) is the process of creating a data model for the data to be stored in a Database. This data model is a conceptual representation of Data objects, the associations between different data objects and the rules. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.


Data model emphasizes on what data is needed and how it should be organized instead of what operations need to be performed on the data. Data Model is like architect’s building plan which helps to build a conceptual model and set the relationship between data items.
The two types of Data Models techniques are

  1. Entity Relationship (E-R) Model
  2. UML (Unified Modelling Language)

Contents


  • Introduction to Logical Data Modeling
    • Importance of logical data modeling in requirements
    • When to use logical data models
    • Relationship between logical and physical data model
    • Elements of a logical data model
    • Read a high-level data model
    • Data model prerequisites
    • Data model sources of information
    • Developing a logical data model
  • Project Context and Drivers
    • Importance of well-defined solution scope
    • Functional decomposition diagram
    • Context-level data flow diagram
    • Sources of requirements
      • Functional decomposition diagrams
      • Data flow diagrams
      • Use case models
      • Workflow models
      • Business rules
      • State diagrams
      • Class diagrams
      • Other documentation
    • Types of modeling projects
      • Transactional business systems
      • Business intelligence and data warehousing systems
      • Integration and consolidation of existing systems
      • Maintenance of existing systems
      • Enterprise analysis
      • Commercial off-the-shelf application
  • Conceptual Data Modeling
    • Discovering entities
    • Defining entities
    • Documenting an entity
    • Identifying attributes
    • Distinguishing between entities and attributes
  • Conceptual Data Modeling-Identifying Relationships and Business Rules
    • Model fundamental relationships
    • Cardinality of relationships
      • One-to-one
      • One-to-many
      • Many-to-many
    • Is the relationship mandatory or optional?
    • Naming the relationships
  • Identifying Attributes
    • Discover attributes for the subject area
    • Assign attributes to the appropriate entity
    • Name attributes using established naming conventions
    • Documenting attributes
  • Advanced Relationships
    • Modeling many-to-many relationships
    • Model multiple relationships between the same two entities
    • Model self-referencing relationships
    • Model ternary relationships
    • Identify redundant relationships
  • Completing the Logical Data Model
    • Use supertypes and subtypes to manage complexity
    • Use supertypes and subtypes to represent rules and constraints
  • Data Integrity Through Normalization
    • Normalize a logical data model
      • First normal form
      • Second normal form
      • Third normal form
    • Reasons for denormalization
    • Transactional vs. business intelligence applications
  • Verification and Validation
    • Verify the technical accuracy of a logical data model
    • Use CASE tools to assist in verification
    • Verify the logical data model using other models
      • Data flow diagram
      • CRUD matrix