Course Outline
Introduction
- Limitations of traditional data warehouse modeling architectures
- Advantages of Data Vault modeling
Data Vault architecture and design fundamentals
- SEI / CMM / Compliance considerations
Data Vault use cases
- Dynamic Data Warehousing
- Exploration Warehousing
- In-Database Data Mining
- Rapid Linking of External Information
Core Data Vault components
- Hubs, Links, and Satellites
Constructing a Data Vault
Modeling Hubs, Links, and Satellites
Data Vault reference rules
Interaction between components
Modeling and populating a Data Vault
Transforming 3NF OLTP data 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 optimization techniques
Load processing and query execution
Matrix Methodology overview
Ingesting data into data entities
Populating Hub Entities
Populating Link Entities
Populating Satellites
Utilizing SEI/CMM Level 5 templates to achieve reproducible, reliable, and measurable outcomes
Establishing a consistent and reproducible ETL (Extract, Transform, Load) process
Building and deploying highly scalable and reproducible warehouses
Conclusion
Requirements
- Foundational knowledge of data warehousing principles
- Basic understanding of database and data modeling concepts
Target Audience
- Data modelers
- Data warehousing specialists
- Business Intelligence experts
- Data engineers
- Database administrators
Testimonials (1)
how the trainor shows his knowledge in the subject he's teachign
