Course Outline
Introduction
- Limitations of existing data warehouse modeling architectures
- Advantages 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, 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 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 outcomes
Developing a consistent and repeatable ETL (Extract, Transform, Load) process
Building and deploying highly scalable and repeatable warehouses
Closing remarks
Requirements
- A foundational understanding of data warehousing concepts
- A foundational 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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