Data Modelling

Data Modelling Training

Course Description:

Why Data warehousing

  • OLTP VS OLAP
  • Data Warehousing architecture

Normalization

  • 1st Normal form
  • 2nd  Normal form
  • 3rd  Normal form   with example

Conceptual Data Modeling

  • Discovering entities
  • Defining entities

Logical Data Modeling

  • Importance of logical data modeling in requirements
  • When to use logical data models
  • Elements of a logical data model
  • Super type and Sub types

Physical Data Modeling

  • Importance of physical data modeling in requirements
  • Elements of a physical data model
  • Difference between logical and physical data model

Identifying Relationships

  • One-to-one
  • One-to-many
  • Many-to-many
  • How to Resolve Many-to-many relationships

Identifying Attributes

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

 Dimensional Modeling

  • What is Dimensional Modeling?
  • Why Dimensional modeling
  • Data warehouse vs Data Mart
  • Star schema vs Snow Flake

Fact

  • What is Fact
  • Fact types

Dimension

  • What is Dimensions?

Dimension types

  • SCD Type- 1
  • SCD Type-2
  • SCD Type- 3
  • Junk Dimension
  • Role Playing Dimension
  • Confirmed Dimension

Erwin

  • Entity
  • Attribute
  • Creating logical Model
  • Creating Physical Model
  • Domain
  • Subject Area
  • Forward Engg
  • Reverse Engg
  • Complete compare

Some interview questions

Note: Based on student requirement course content can be altered


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