TDWI Advanced Dimensional Modeling: Techniques for Practitioners Training in Norway

• BI program managers
• Business analysts
• Data architects and modelers
• BI architects and developers
• Data integration architects and developers
• Project managers
• Database administrators
• “Power users” and business subject matter experts

• Why most subject areas require multiple fact tables, and how to identify them
• When to use alternatives to the basic transaction fact table, including periodic snapshots, accumulating snapshots, and type-specific stars
• How to cope with dimensional intricacy using techniques such as bridge tables, mini-dimensions, time-stamped dimensions, hybrid slow changes, and other slow change options
• Techniques to ensure your data warehouse will scale as new subject areas are added
• How design fits into development methods, who should be involved in design activities, and what tasks and outputs should be incorporated

Module One

• Fundamentals
• Dimensional Modeling
• Process measurement
• Uses beyond database design
• Information Architectures and Dimensional Data
• Inmon’s CIF Architecture
• Kimball’s Dimensional Bus
• Stand-alone Data Marts
• Fundamental Terms and Best Practices
• Facts and dimensions
• Surrogate keys, natural keys and slow change processing
• Fundamental concepts including grain, sparsity and additivity
• Best practices for fact tables and dimension tables
• Implementations
• Relational (star and snowflake)
• Multidimensional (cube)
• Cubes as primary store
• Cubes as derived data stores

Module Two

• Multiple Stars
• Multiple star solutions
• Designing multiple fact tables
• Identifying multiple processes
• Differences in dimensionality
• The pitfalls of single fact table design
• Using multiple stars
• How not to query multiple fact tables
• The concept of drilling across
• What you need to know about your query and reporting tools
• Conformance and business value
• High impact business questions span processes
• The concept of conformance
• Ensuring subject areas work together
• Enabling incremental implementation

Module Three

• Advanced Fact Table Design
• Transaction schemas
• Transaction grain
• Shortcomings of transaction designs
• Periodic Snapshots
• Snapshot grain and period
• Semi-additivity, density, and impact on BI
• Building both transaction and snapshot schemas
• Snapshots and averages
• Accumulating Snapshots
• Studying process efficiency
• Accumulating metrics in a single row
• Lag analysis
• Impacts on slow change processing and data integration
• Building both transaction and accumulating snapshots
• Factless Fact Tables
• Processes that seem to lack metrics
• Factless fact tables that track events
• Pros and cons of adding constant-value fact
• Factless fact tables that track conditions
• Comparing conditions to actual events
• Heterogeneous Attributes
• Attributes that vary based on category
• The impact of modeling a single set of attributes
• Core and custom dimensions
• Core and custom fact tables
• Alternatives to core and custom solutions

Module Four

• Design and Data Integration
• The data integration process
• Loading a dimension table
• Loading fact tables
• Key lookup processing
• Designing to aid key management
• The impact of slow changes on key lookups
• Adding attributes to aid lookup process
• Designing to reduce slow change bottlenecks
• The bottlenecks of slow change processing
• How dimension checksums aid slow change processing
• Specifying data transformation rules
• How to capture standard translations and data quality rules
• Why this must be done at design-time
• Invalid or late reference data
• Receiving valid facts with invalid or missing reference data
• Adding rows to dimension tables so that facts can be loaded
• Adjusting facts when reference data arrives
• Adding columns to support QA
• Housekeeping columns in dimension tables
• Housekeeping dimensions for fact tables

Module Five

• Advanced Dimension Design
• Understanding hierarchies
• Drilling with and without hierarchies
• Multiple hierarchies in one dimension
• Impact of hierarchies on BI, ETL and DBA perspectives
• Why to model hierarchies
• Snowflake schemas
• Dimension Reuse
• Modeling roles
• Querying with roles
• Nulls
• Problems introduced by NULL values
• Avoiding NULL dimension attributes
• Avoiding NULLs with optional relationships
• NULL facts

Module Six

• Advanced Slow Change Processing
• Time-stamped Dimensions
• Why type 2 is not good enough
• Time-stamped dimensions (transaction dimensions)
• Using with a fact table
• Hybrid attributes that behave like facts and dimensions
• Mini-dimensions
• Large and expanding dimension tables
• The use of a mini-dimension to stem growth
• Impact on schema capability
• Type 4 and Type 5 slow changes
• Loading the mini-dimension
• Current and Previous
• Limited access to unchanged value
• Hybrid Responses
• Tracking both Type 1 and Type 2 responses
• For dimension Attributes (Type 6)
• For entire dimension tables (Type 7)

Module Seven

• Bridge Table Design
• The dimension bridge
• When a single fact needs to reference more than one dimension row