Learning Data Modeling

A step by step guide to data modeling concepts and best practices underpinning sound database design.

What you'll learn

conceptually plan a coherent data model to plan and design enterprise-quality databases.

differentiate between UML and IE data models.

create databases with SQL and Microsoft Access.

Requirements

some knowledge of programming principles is strongly recommended.

Description

Truly effective database design depends on having a coherent data model to work from. This course will help you learn the theory and process of creating data models suitable for everything from small business to enterprise and data center environments. Michael Blaha will teach you how to plan and construct data models, as well as build upon those models through an actual database. You will start by learning about the data modeling development process, then jump into basic and advanced data modeling. From there, Michael will teach you how to create a UML data model, including finding classes, adding attributes, and simplifying the model. This video tutorial also covers how to translate a UML data model into an IE data model, model quality, the different kinds of data models, and database design. You will also learn how to create an SQL server database, an MS-Access database, and develop frameworks. Finally, Michael will teach you about data modeling patterns and database reverse engineering. Once you have completed this computer based training course, you will be fully capable of creating your own data models.

Overview

Section 1: Getting Started

Lecture 1 Important - Download These First - Working Files

Lecture 2 About The Course

Lecture 3 What Is A Database?

Lecture 4 What Is A Data Model?

Lecture 5 How To Access Your Working Files

Section 2: Data Model Development Process

Lecture 6 Data Model Inputs And Outputs

Lecture 7 Data Model Notations

Lecture 8 UML Versus IE - Conceptual, Logical And Physical

Section 3: Basic Data Modeling

Lecture 9 Class And Attribute

Lecture 10 Operation

Lecture 11 Domain

Lecture 12 Association

Lecture 13 IE Entity Type And Relationship Type

Lecture 14 Association Name

Lecture 15 Association End

Lecture 16 Multiplicity - UML

Lecture 17 Multiplicity - IE

Lecture 18 Generalization - UML

Lecture 19 Generalization - IE

Lecture 20 Abstract Versus Concrete Superclass

Lecture 21 Practical Tips

Lecture 22 Self Assessment Test

Section 4: Advanced Data Modeling

Lecture 23 Identity

Lecture 24 Derived Data

Lecture 25 Current Versus Historical Data

Lecture 26 Association Class

Lecture 27 Ordered Association

Lecture 28 Qualified Association - UML

Lecture 29 Qualified Association - IE

Lecture 30 Large Taxonomies

Lecture 31 Package

Lecture 32 Abridged UML Metamodel

Lecture 33 Abridged IE Metamodel

Lecture 34 Modeling Pitfalls

Lecture 35 Practical Tips

Lecture 36 Self Assessment Test

Section 5: Create A UML Data Model

Lecture 37 Problem Statement

Lecture 38 Finding Classes

Lecture 39 Finding Associations - Part 1

Lecture 40 Finding Associations - Part 2

Lecture 41 Finding Generalizations

Lecture 42 Iterating And Refining The Model - Part 1

Lecture 43 Iterating And Refining The Model - Part 2

Lecture 44 Adding Attributes

Lecture 45 Cleaning Up Layout

Lecture 46 Simplifying The Model

Lecture 47 Evolving A Model - Part 1

Lecture 48 Evolving A Model - Part 2

Lecture 49 Enterprise Architect Techniques - Part 1

Lecture 50 Enterprise Architect Techniques - Part 2

Lecture 51 Enterprise Architect Techniques - Part 3

Section 6: Translate A UML Data Model Into An IE Data Model

Lecture 52 Creating Subject Areas

Lecture 53 Creating Entity Types

Lecture 54 Creating Domains

Lecture 55 Adding Attributes - Part 1

Lecture 56 Adding Attributes - Part 2

Lecture 57 Creating Relationship Types - Part 1

Lecture 58 Creating Relationship Types - Part 2

Lecture 59 Creating Relationship Types - Part 3

Lecture 60 Subtyping

Lecture 61 Adding Alternate Keys

Lecture 62 Cleaning Up The Layout

Lecture 63 ERwin Techniques - Part 1

Lecture 64 ERwin Techniques - Part 2

Section 7: Model Quality

Lecture 65 Model Quality

Lecture 66 Normal Forms

Lecture 67 Constraints

Lecture 68 Hillard Graph Complexity

Lecture 69 Hoberman Data Model Scorecard

Section 8: Kinds Of Data Models

Lecture 70 Operational Data Models

Lecture 71 Enterprise Data Models

Lecture 72 Data Warehouses - Part 1

Lecture 73 Data Warehouses - Part 2

Lecture 74 Data Warehouses - Part 3

Lecture 75 Master Data Models

Section 9: Database Design

Lecture 76 Schema Adjustments

Lecture 77 Attribute Details - Part 1

Lecture 78 Attribute Details - Part 2

Lecture 79 Attribute Details - Part 3

Lecture 80 Primary And Alternate Keys

Lecture 81 Indexes

Lecture 82 Referential Integrity - Part 1

Lecture 83 Referential Integrity - Part 2

Lecture 84 Check Constraints - Part 1

Lecture 85 Check Constraints - Part 2

Lecture 86 Views

Lecture 87 Other Aspects Of Design

Lecture 88 Self Assessment Test

Section 10: Create A SQL Server Database

Lecture 89 Creating A New Database

Lecture 90 Executing Schema

Lecture 91 Inspecting Metadata

Lecture 92 Loading Sample Data

Lecture 93 Querying Sample Data

Section 11: Create An MS-Access Database

Lecture 94 Generating An ERwin Schema

Lecture 95 Creating Tables

Lecture 96 Creating Indexes

Lecture 97 Creating Constraints And Default Values

Lecture 98 Defining Foreign Keys

Lecture 99 Creating Views

Lecture 100 Loading Sample Data

Lecture 101 Querying Sample Data

Section 12: Software Engineering

Lecture 102 Development Frameworks

Lecture 103 Agile Data Modelling

Lecture 104 Documenting A Model - Part 1

Lecture 105 Documenting A Model - Part 2

Lecture 106 Presenting A Model

Section 13: Data Modeling Patterns

Lecture 107 Overview

Lecture 108 Tree - Hardcoded

Lecture 109 Tree - Simple

Lecture 110 Tree - Structured

Lecture 111 Tree - Overlapping

Lecture 112 Tree - Changing Over Time

Lecture 113 Tree - Degenerate Node and Edge

Section 14: Database Reverse Engineering

Lecture 114 Motives

Lecture 115 Comparison With Forward Engineering

Lecture 116 Outputs

Lecture 117 Inputs

Lecture 118 Process

Lecture 119 Principles

Lecture 120 Example - Part 1

Lecture 121 Example - Part 2

Section 15: Conclusion

Lecture 122 Wrap-Up

developers and IT professionals who want a thorough understanding of formal data concepts and models as they relate to database design.