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Introduction to Logical Data Modeling

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Duration: 1 Day

Method: Instructor led

Price: $500.00

Course Code: DW1013



Audience

Developers, Business Analysts, System Analysts, Managers and Data Base Administrators.

Description

This course introduces students to the principles and process of gathering and translating business data requirements into a graphical logical data model. Participants will see the practical application of the basic data modeling techniques commonly utilized by Business Analysts. With a combination of lecture, examples and lab exercises, attendees are guided through the concepts, use and creation of the Context Diagram, Data Flow Diagram and Entity Relationship Diagram.

Objectives

Upon successful completion of this course, the student will be able to:

  • Recognize why data modeling is used and the benefits of this practice
  • Discuss data modeling approach
  • Interpret and create Context Diagrams, Data Flow Diagrams, and Entity Relationship Diagrams
  • Define the three types of data models: Conceptual, Logical and Physical
  • Create a complex data model and validate against a key set of business requirements
  • Distinguish and describe Entities, Relationships, and Attributes
  • Describe the Normalization process
  • Understand the use of primary keys and foreign keys

Prerequisites

None, though business knowledge and some knowledge of programming principles is recommended.

Topics

  1. Introduction to Data Modeling
    • Database Life Cycle (Requirements Analysis, Logical Design, Physical Design, Implementation)
    • Logical Model
    • Physical Model
    • Advantages of Modeling
    • Data Modeling Details
    • Conceptual Data Modeling (Bachman, UML, ER, CHEN)
    • Entity-Relationship Model
    • Entities, Attributes, Relationships
    • Myths and Realities
    • Data Model Usage
    • Types of Data Models (Contextual, Conceptual, Logical, Physical)
    • Development Approaches (Top-Down, Bottom-Up)
  2. Data Flow Diagrams
    • Data Flow Diagrams (DFD)
    • External Entities
    • Data Stores
    • Data Flows
    • Processes
    • Context DFD
    • Level – 0 DFD
    • Exercise: Case Study Part 1 - Create a Context DFD for Uber/Amazon Concert Sponsors
  3. Conceptual Data Modeling
    • Identifying Entities
    • Entity Litmus Test
    • Labeling Entities
    • Relationships (one-to-one, one-to-many, and many-to-many)
    • Relationship Labels
    • Attributes
    • Identifiers and Descriptors
    • Notations (Chen, IEW, IDEFIX)
    • Exercise: Case Study Part 2 - Create a Conceptual Data Model for Uber/Amazon Concert Sponsors (with only unique identifier
  4. Logical Data Modeling
    • From Conceptual to Logical (Key Differences)
    • Entity Types (Fundamental, Associative, Attributive)
    • Fundamental Entity
    • Associative Entity
    • Recursive Relationships
    • Repeating Groups
    • Attributive Entity
    • Supertypes and Subtypes
    • Primary Key
    • Guidelines for Primary Keys
    • Exercise: Case Study Part 3 – Move towards a logical model (Refine associative and attributive entities;
    • Refine recursive entities; Find sub type relationships; Define primary key for all entities)
  5. Normalization
    • Fundamentals of Normalization
    • Goals of Normalization
    • 0NF, 1st NF, 2nd NF, 3rd NF
    • Atomic Values - Eliminating Repeating Groups (1st NF)
    • Functional Dependence - Removing Partial Dependencies (2nd NF)
    • Removing Transitive Dependencies (3rd NF)
    • Boyce-Codd
    • Exercise: Case Study Part 4 – Normalize the logical model (3NF) you created in the previous exercise
  6. APPENDIX – What Happens Next?
    • Physical Model Overview
    • Roles and Responsibilities
    • References