Data Modelling Training

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Data Modelling  in software engineering is the process of creating a data model for an information system by applying formal data modelling techniques.

Data models provide a structure for data used within information systems by providing specific definition and format. If a data model is used consistently across systems then compatibility of data can be achieved. If the same data structures are used to store and access data then different applications can share data seamlessly.

Data models represent information areas of interest. While there are many ways to create data models, only two modelling methodologies stand out, top-down and bottom-up:

  • Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an enterprise perspective. They may not promote data sharing, especially if they are built without reference to other parts of the organization
  • Top-down logical data models, on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template.

Sometimes models are created in a mixture of the two methods: by considering the data needs and structure of an application and by consistently referencing a subject-area model. Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred.


Section1: 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 modelRead a high-level data model
  • Read a high-level data model
  • Data model prerequisites
  • Data model sources of information
  • Developing a logical data model

Section2: Project Context and Drivers

  • Importance of well-defined solution scope
  • Functional decomposition
  • Context-level data flow diagram
  • Sources of requirements
  • Data interpretation Mechanism
  • Class diagrams
  • Other documentation
  • 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

Section3: Conceptual Data Modeling

  • Discovering entities
  • Defining entities
  • Documenting an entity
  • Identifying attributes
  • Distinguishing between entities and attributes

Section4: 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

Section5: Identifying Attributes

  • Discover attributes for the subject area
  • Assign attributes to the appropriate entity
  • Name attributes using established naming conventions
  • Documenting attributes

Section6: 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

Section7: Completing the Logical Data Model

  • Use supertypes and subtypes to manage complexity
  • Use supertypes and subtypes to represent rules and constraints

Section8: 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

Section9: Verification and Validation

  • Verify the technical accuracy of a logical data model
  • Verify the logical data model using other models
  • Data flow diagram


  • Professional approach towards training using latest techniques
  • Minimal batch strength to give individual attention to all
  • Unlimited lab / practice environment access provided to all candidates
  • Flexible batch schedule – Attend missed sessions with next batch
  • We provide free placement assistance because we care about your career

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