Arkoa homepage
About Arkoa News & Events Contact Us Careers
  Course Catalog : Databases :

Logical Data Modeling
3 days

Course # 06-2000


Description

This course explains how to perform logical data modeling (LDM) and design a database suitable for the type of data it will hold. Participants will learn how to transform business information requirements into a logical data model and initial database design. This course includes a series of exercises in basic and advanced design.

Audience
System analysts/designers, application developers, and database administrators.

Prerequisites
A general introduction to databases is recommended but not required. This course should be followed by Arkoa's Relational Database Design course. SQL, Sybase, and Oracle courses are also available.

 


Format

  • Presentation
  • Written Exercises

Objectives
After completing this course, participants should be able to:

  • Identify and define business information requirements
  • Recognize the principles behind data architectures and corporate data models
  • Select an appropriate approach to data modeling
  • Create a logical data model
  • Describe the transition from a logical data model to a physical relational database design
  • Use advanced techniques to refine the data model

 


Topics

Introduction
  • Definition, benefits, and risks
  • Corporate vs. project data modeling
  • Logical vs. physical data modeling
  • Building the LDM

Approaches to Logical Data Modeling

  • Top-down, bottom-up, and view integration approaches
  • Selecting the right approach

Entity-Relationship Modeling

  • Why E-R modeling?
  • CASE tools
  • E-R modeling concepts
  • Relationships
    • Connectivity and cardinality
    • Optionality
    • Named
    • Many-to-many
  • Validating E-R models

Transactions vs. Decisions

  • DSS vs. OLTP
  • Data warehouse
  • Dimensional data
  • Star and snowflake schema
  • Snowflake design issues
  • Developing the LDM: Selecting and classifying data


 


Attributes and Keys

  • Domains
  • Derived and calculated attributes
  • Multi-valued attributes
  • Primary and foreign keys
  • Normalization

Modeling for Decision Support

  • OLTP
  • DSS
  • Dimensional data
  • Star schema
  • Snowflake schema
  • Developing the LDM

Advanced Entity Relationship Modeling

  • Dependent and associative entities
  • Subtypes and supertypes
  • Null attributes and recursive relationships
  • Naming objects