by | Released November 2014 | ISBN: 9781771373289

In this Learning Data Modeling training course, expert author Michael Blaha will teach you how to build data models, as well as prepare a data model for simple problems. This course is designed for users that have some programming experience, however, no data modeling experience is needed.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. Working files are included, allowing you to follow along with the author throughout the lessons. Show and hide more Publisher resources Download Example Code

1. Getting Started
   • About The Course 00:03:24
   • About The Author 00:02:49
   • What Is A Database? 00:02:23
   • What Is A Data Model? 00:01:52
2. Data Model Development Process
   • Data Model Inputs And Outputs 00:02:06
   • Data Model Notations 00:03:00
   • UML Versus IE - Conceptual, Logical And Physical 00:01:19
3. Basic Data Modeling
   • Class And Attribute 00:06:12
   • Operation 00:01:32
   • Domain 00:03:55
   • Association 00:05:00
   • IE Entity Type And Relationship Type 00:03:15
   • Association Name 00:04:46
   • Association End 00:04:09
   • Multiplicity - UML 00:03:40
   • Multiplicity - IE 00:02:32
   • Generalization - UML 00:03:32
   • Generalization - IE 00:04:06
   • Abstract Versus Concrete Superclass 00:02:11
   • Practical Tips 00:01:59
   • Self Assessment Test - Basic Modeling Data 00:04:44
4. Advanced Data Modeling
   • Identity 00:02:35
   • Derived Data 00:02:34
   • Current Versus Historical Data 00:01:07
   • Association Class 00:05:21
   • Ordered Association 00:05:26
   • Qualified Association - UM 00:05:12
   • Qualified Association - IE 00:02:36
   • Large Taxonomies 00:03:05
   • Package 00:02:46
   • Abridged UML Metamodel 00:02:04
   • Abridged IE Metamodel 00:01:14
   • Modeling Pitfalls 00:03:27
   • Practical Tips 00:01:47
   • Self Assessment Test - Advanced Data Modeling 00:03:45
5. Create A UML Data Model
   • Problem Statement 00:01:52
   • Finding Classes 00:04:32
   • Finding Associations - Part 1 00:05:00
   • Finding Associations - Part 2 00:05:53
   • Finding Generalizations 00:01:37
   • Iterating And Refining The Model - Part 1 00:02:39
   • Iterating And Refining The Model - Part 2 00:04:42
   • Adding Attributes 00:04:48
   • Cleaning Up Layout 00:04:04
   • Simplifying The Model 00:01:55
   • Evolving A Model - Part 1 00:02:13
   • Evolving A Model - Part 2 00:04:46
   • Enterprise Architect Techniques - Part 1 00:03:48
   • Enterprise Architect Techniques - Part 2 00:05:20
   • Enterprise Architect Techniques - Part 3 00:04:25
6. Translate A UML Data Model Into An IE Data Model
   • Creating Subject Areas 00:02:39
   • Creating Entity Types 00:02:44
   • Creating Domains 00:06:13
   • Adding Attributes - Part 1 00:06:14
   • Adding Attributes - Part 2 00:03:25
   • Creating Relationship Types - Part 1 00:05:04
   • Creating Relationship Types - Part 2 00:03:34
   • Creating Relationship Types - Part 3 00:05:07
   • Subtyping 00:03:20
   • Adding Alternate Keys 00:03:36
   • Cleaning Up The Layout 00:01:38
   • ERwin Techniques - Part 1 00:04:58
   • ERwin Techniques - Part 2 00:04:00
7. Model Quality
   • Model Quality 00:01:02
   • Normal Forms 00:04:02
   • Constraints 00:03:27
   • Hillard Graph Complexity 00:07:04
   • Hoberman Data Model Scorecard 00:05:21
8. Kinds Of Data Models
   • Operational Data Models 00:03:30
   • Enterprise Data Models 00:05:34
   • Data Warehouses - Part 1 00:05:12
   • Data Warehouses - Part 2 00:04:54
   • Data Warehouses - Part 3 00:03:15
   • Master Data Models 00:04:08
9. Database Design
   • Schema Adjustments 00:04:48
   • Attribute Details - Part 1 00:04:27
   • Attribute Details - Part 2 00:06:28
   • Attribute Details - Part 3 00:07:44
   • Primary And Alternate Keys 00:08:09
   • Indexes 00:06:45
   • Referential Integrity - Part 1 00:08:22
   • Referential Integrity - Part 2 00:06:28
   • Check Constraints - Part 1 00:06:45
   • Check Constraints - Part 2 00:07:08
   • Views 00:08:44
   • Other Aspects Of Design 00:03:53
   • Self Assessment Test - Database Design 00:03:15
10. Create A SQL Server Database
    • Creating A New Database 00:03:39
    • Executing Schema 00:02:29
    • Inspecting Metadata 00:08:33
    • Loading Sample Data 00:04:03
    • Querying Sample Data 00:06:34
11. Create An MS-Access Database
    • Generating An ERwin Schema 00:03:08
    • Creating Tables 00:06:17
    • Creating Indexes 00:03:22
    • Creating Constraints And Default Values 00:02:52
    • Defining Foreign Keys 00:03:44
    • Creating Views 00:04:32
    • Loading Sample Data 00:03:55
    • Querying Sample Data 00:04:21
12. Software Engineering
    • Development Frameworks 00:03:25
    • Agile Data Modelling 00:03:19
    • Documenting A Model - Part 1 00:03:49
    • Documenting A Model - Part 2 00:04:25
    • Presenting A Model 00:02:45
13. Data Modeling Patterns
    • Overview 00:01:37
    • Tree - Hardcoded 00:01:26
    • Tree - Simple 00:01:37
    • Tree - Structured 00:01:16
    • Tree - Overlapping 00:04:06
    • Tree - Changing Over Time 00:03:22
    • Tree - Degenerate Node and Edge 00:01:29
14. Database Reverse Engineering
    • Motives 00:01:30
    • Comparison With Forward Engineering 00:02:21
    • Outputs 00:01:08
    • Inputs 00:02:08
    • Process 00:05:35
    • Principles 00:01:41
    • Example - Part 1 00:05:54
    • Example - Part 2 00:08:08
15. Conclusion
    • Wrap-Up 00:06:19
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