Data modeling defines and applies structure to the information systems in an enterprise. Data stored in various relational databases needs data modeling to depict the relationship between entities in the databases. The models provide pictorial views of how the data flows across the enterprise, departments, or business areas. Before creating a database for any application, you need well-constructed data models to maintain the integrity of data and improve query performance. This course provides in-depth knowledge and hands-on practice in data modeling and design.
After introducing the basic concepts and principles, the course addresses data modeling techniques and practices in four modeling areas: conceptual, logical, physical and dimensional. The course first addresses the collection of user requirements, followed by design approaches for logical and physical models. You will study real-world examples of data models for transactional systems, data marts and enterprise data warehouses. Expert instructors will share their practical experiences.
This is a hands-on course using an industry-leading data modeling tool in class. By the end of the course, you will be able to create data models for enterprise applications.
- Overview of data modeling
- Principles of data modeling
- Types of data modeling: Conceptual, Logical, and Physical
- Logical data modeling: Building data models; Cardinality rules; Transformation rules
- Physical data modeling: Database standards; Domains and classwords; Roll-ups and roll-downs; Data model repository options
- Dimensional data modeling: Star schema modeling; Snow flake modeling
- Top ten mistakes to be avoided