Building The Logical Data Model
About This Course
If you work with distributed teams, including offshore developers and testers, you know that the more distant the development team, the greater the need for precision. Precise doesn't mean bigger documents in more abstruse notations. In this course you will learn a simple and compact system for collaborative modeling that enables you to capture the most information in the smallest space with the least work in a way that's easily testable and highly adaptable. By doing this precise data analysis you will deliver more value in less time with higher quality.
- Learn how to organize a problem domain's concepts into a formal and accurate relational data model.
Create tables, columns, relations, and constraints that accurately reflect data requirements.
Use basic principles of normalization to ensure consistency.
Understand when "de-normalization" is and is not appropriate.
Create non-relational data models (such as XML schemas) and learn how to convert between relational and nonrelational models.
This course is valuable for anyone who needs to accurately understand and manage the role of data and information in any given business processes or area. Perfect for:
- Business Analysts
- Data Modelers
- Data Analysts
- Process Modelers
- Project Managers
Section 1. Basics of the Relational Data Model?
- Logical vs. Physical Models
- Tables, Columns, Keys, and Relations
- Entity-Relationship Diagramming (ERDs) and UML Class Diagrams
Section 2. Normalization Techniques
- Why is normalization important?
- Common Normal Forms
- Real-World Data Issues
Section 3. Data Models in Context
- Evaluating Data Requirements
- Data Analysis in Business Process Models
- Dealing with Legacy Systems and Data
Section 4. Applying Data Models
- Data Models and UI Designs
- Hierarchical Data Models (e.g. XML)
- Object-Relational Mapping