Course Description | Course Outline | Data Warehousing | IT Training

1. Data Warehouse Architectures

• Centralized
• Functional
• Federated
• Imbedded data marts
• Dependent data marts
• Independent data marts

2. Analytical Modeling Primer

• Facts and dimensions
• Different schemas
• The star schema
• The snowflake schema
• The normalized model
• Pros and cons
• Query types :
• Inside out
• Outside in
• Joins types :
• Standard joins
• Fact-to-fact joins
• Heterogeneous products
• Subtyping
• Heterogeneous products or customers

3. Dimensions

• Definition of dimensions
• Dimension levels
• Dimensional hierarchies
• Multiple dimensions
• Behavioral dimensions
• Ragged dimensions
• Complex dimensions
• Huge dimensions

4. Keys

• Natural vs. surrogate keys
• Pros and cons of surrogate keys
• Effect of surrogate keys on ETL

5. Time and History

• Definition of time series and history
• Methods for handling time and history
• Actual date/time stamp
• Current record only
• Simple history
• Delimited history
• Complex history
• Period vs. date
• The calendar
• Simple calendar
• Complex calendar
• Building the calendar
• Bucketizing time
• Recency, frequency, periodicity

6. Changing dimensions

• Slowly and rapidly changing
• Overwriting
• Versioned
• Using Period ID
• Current and last

7. Dimensions and Hierarchies

• Dimension hierarchies and networks
• Alternatives for handling hierarchies
• Flattened, recursive
• "Bill of materials", snowflaked

8. Value Banding

• Importance of
• Recency, frequency, monetary

9. Aggregation

• How to improve performance
• Best practices
• Rollups and cubes
• Types of aggregates
• Aggregate navigation
• Automatic summary tables
• Collections of data

10. Operational Data Store (ODS)

• What it is
• What it is not
• Kinds and uses of ODS