Course Outline
Introduction
- Limitations of current data warehouse data modeling architectures.
- Benefits of Data Vault modeling.
Overview of Data Vault architecture and design principles.
- SEI / CMM / Compliance.
Data Vault applications.
- Dynamic Data Warehousing.
- Exploration Warehousing.
- In-Database Data Mining.
- Rapid Linking of External Information.
Data Vault components.
- Hubs, Links, and Satellites.
Building a Data Vault.
Modeling Hubs, Links, and Satellites.
Data Vault reference rules.
Interaction between components.
Modeling and populating a Data Vault.
Converting 3NF OLTP 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 the Matrix Methodology.
Ingesting data into data entities.
Loading Hub Entities.
Loading Link Entities.
Loading Satellites.
Using 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
- Knowledge of data warehousing concepts.
- Knowledge of database and data modeling concepts.
Audience
- Data modelers.
- Data warehousing specialists.
- Business Intelligence specialists.
- Data engineers.
- Database administrators.
Testimonials (1)
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