Course Description | Course Outline | Data Warehousing | IT Training

1. Introduction to Data Warehousing

• Scope and levels of modeling
• Kinds of data
• The framework for data modeling
• Challenges in data management
• Five major characteristics of data warehouse
• Data Models:
• Corporate
• High level
• Detailed level
• Operational
• Decision support
• Types and technologies of data warehousing

2. Data Warehouse Architectures

• Centralized DW
• Functional DW
• Federated DW
• Independent Data Marts
• Dependent Data Marts

3. Data Warehouse Methodology

• Explanation of methodology steps
• Iterative nature of development

4. Information Gathering

• Facilitated sessions
• Interviews
• Information gathering techniques
  • Events
  • Objectives
  • Queries
  • Goals
  • Decisions
  • Problems
    

5. Data Store Layer

• Building the Data Warehouse Model
• Facts, dimensions
• Summarized data
• Levels of Data In the Enterprise
• Base grains
• Intermediate Summaries
• Specialized summaries

6. Modeling Time and History

• Short term and long term view
• Four ways of handling time and date
• Time-series data
• Capturing business changes
• Importance of representing the business time dimension

7. ETL Layer

• Defining transformation requirements
• Defining transformation rules
• The transformation requirements spreadsheet
• Building transformation processes
• Enforcing controls in the ETL process
• Designing the transformation process
• Complete coverage transformation types
• Dealing with change data
• Supporting surrogate keys
• Near-real time transformation

8. BI Layer

• Designing the BI interface
• Matching the BI interface to the user
• Types of BI technologies and design
• Types of reporting
• OLAP in all its forms:
  • MOLAP
  • HOLAP
  • DOLAP
  • ROLAP
• Data sparsity and density
• Data explosion due to calculations, rollups and summaries
  

9. Data Warehouse Technology

• Categories of warehouse tools
• Review of major products

10. Important Considerations and Issues

• System load
• Denormalization and performance
• Archiving and purging
• Data distribution and replication
• Change control
• Copy management
• Alternative Models For Copied Data

11. Managing Data Warehouse Projects

• Data warehouse project structure
• Managing multiple data warehouse projects
• Data distribution issues

12. Summary and Conclusion

• Selected warehouse projects
• Critical Success Factors

13. Case Studies

• Selected mini-exercises
• Complete group case study (moderately sized)
• Complete individual case study (large)