Course Outline
Introduction
- Limitations of traditional data warehouse modeling architectures
- Advantages of adopting Data Vault modeling
Principles of Data Vault architecture and design
- SEI / CMM / Compliance considerations
Applications of Data Vault
- Dynamic Data Warehousing
- Exploratory Warehousing
- In-Database Data Mining
- Rapid Integration of External Information
Core Data Vault components
- Hubs, Links, and Satellites
Constructing a Data Vault
Modeling Hubs, Links, and Satellites
Data Vault reference rules
Interactions between components
Designing and populating a Data Vault
Transforming 3NF OLTP structures into a Data Vault Enterprise Data Warehouse (EDW)
Managing load dates, end-dates, and join operations
Business keys, relationships, link tables, and join strategies
Query techniques
Load processing and query execution
Introduction to the Matrix Methodology
Ingesting data into data entities
Populating Hub Entities
Populating Link Entities
Populating Satellites
Applying SEI/CMM Level 5 templates to achieve repeatable, reliable, and measurable outcomes
Creating a consistent and repeatable ETL (Extract, Transform, Load) process
Building and deploying highly scalable and repeatable warehouses
Conclusion
Requirements
- Familiarity with data warehousing concepts.
- Knowledge of database and data modeling principles.
Target Audience
- Data modelers
- Data warehousing specialists
- Business Intelligence specialists
- Data engineers
- Database administrators
Testimonials (1)
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