Course Outline
Introduction
- Limitations of existing data warehouse data modeling architectures.
- Advantages of Data Vault modeling.
Overview of Data Vault architecture and design principles
- SEI / CMM / Compliance.
Applications of Data Vault
- Dynamic Data Warehousing.
- Exploration Warehousing.
- In-Database Data Mining.
- Rapid Linking of External Information.
Data Vault components
- Hubs, Links, Satellites.
Building a Data Vault
Modeling Hubs, Links, and Satellites
Data Vault reference rules
Interactions between components
Modeling and populating a Data Vault
Converting 3NF OLTP systems to a Data Vault Enterprise Data Warehouse (EDW).
Understanding load dates, end-dates, and join operations.
Business keys, relationships, link tables, and join techniques.
Query techniques.
Load processing and query processing.
Overview of Matrix Methodology.
Ingesting data into data entities.
Loading Hub Entities.
Loading Link Entities.
Loading Satellites.
Utilizing SEI/CMM Level 5 templates to achieve repeatable, reliable, and quantifiable results.
Developing a consistent and repeatable ETL (Extract, Transform, Load) process.
Building and deploying highly scalable and repeatable warehouses.
Closing remarks.
Requirements
- Fundamental understanding of data warehousing concepts.
- Fundamental understanding of database and data modeling concepts.
Target Audience
- Data modelers.
- Data warehousing specialists.
- Business Intelligence specialists.
- Data engineers.
- Database administrators.
Testimonials (1)
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