1. Overview and Fundamentals
Dimensional Modeling
Overview and Fundamentals

2. What Is Dimensional Modeling?

3. Dimensional Modeling An Overview The Fundamentals
Structure
Terminology
Benefits
The Fundamentals
Facts and Dimensions
The 4 Step Design Process

4. What does Data Warehouse look like
Kimball Data Warehouse
Back Room Staging Area (The kitchen)
Data Presentation Area (The Dinning Room)
Star Schema
Corporate Information Factory (CIF)
Normalized Dimensions
Dual ETL Loading, Warehouse and Data Marts
Operational Data Store (ODS)
Hybrid Data Warehouse

5. Kimball Data Warehouse

6. Corporate Information Factory

7. Terminology Dimensions
The time independent, textual and descriptive attributes by which users describe objects.
Combining all the attributes including hierarchies, rollups and sub-references into a single dimension is denormalization.
Often the “by” word in a query or report
Not time dependent
Facts
Business Measurements
Most Facts are Numeric
Additive, Semi-Additive, Non-Additive
Built from the lowest level of detail (grain)
Very Efficient
Time dependent

8. Star Schema
Singe data (fact) table surrounded by multiple descriptive (dimension) tables

9. Benefits
Performance (Integer relationships, natural partitioning, Single joins benefit SQL optimizer)
Source system independence and multiple integration
Supports Change management
Usability/Simplicity (easy to read, interpret, join, calculate)
Presentation (Consistency, Taxonomy, Labeling)
Reuse (Conformed dimensions reduce redundancy, Role-plays)

10. Dimension Change Strategy
Type 1: Is used when the old value of the attribute has no significance or can be discarded.
Easy and Fast
Type2: Partitions history so that fact tables properly reflect original values.
Requires use of Surrogate Keys
Causes table growth due to additional history rows
Users must be aware of the added complexity
Effective Dates used secondary to cleaner fact joins

11. Dimension Change Strategy
Type 3: Additional attribute used to capture changes.
Used less frequently then Type 1 or 2.
Simultaneously supports two views of the world.
Does not trend changes over time.
Current and Prior or Current and Original Attributes
Hybrid Type: Combination 1, 2 & 3 changes
New attribute for predictable series (such as yearly changes)
Type 2 changes with prior or original attributes included
Expanded dimension table for durable key inclusion in fact
Added complexity to users

12. Dimension Role Playing
A single table that plays multiple roles (using views) to create synonym dimension attributes.
Most common role playing dimension is the Date Dimension. i.e. separate role playing dimensions for order date and ship date.

13. Fact Table Types Characteristic Transaction Periodic Snapshot
Accumulating Snapshot
Time period
Point in time
Regular, predictable intervals
Indeterminate time span, typically short-lived.
Grain
One row per transaction event
One row per period
One row per life
Fact table loads
Insert
Insert and Update
Fact row updates
Not revisited
Revisited whenever activity
Date dimension
Transaction date
End-of-period date
Multiple dates for multiple milestones
Facts
Transaction activity
Performance for a predefined time interval.
Performance over finite lifetime

14. Modeling Design Process
Identify the Business Process
Source of “measurements”
Identify the Grain
What does 1 row in the fact table represent or mean?
Identify the Dimensions
Descriptive context, true to the grain
Identify the Facts
Numeric additive measurements, true to the grain

15. Step 1 - Identify the Business Process
This is a business activity typically tied to a source system.
Not to be confused with a business department or function. An Orders dimensional model should support the activities of both Sales and Marketing.
“If we establish departmentally bound dimensional models, we’ll inevitably duplicate data with different labels and terminology.”

16. Step 2 - Identify the Grain
The level of detail associated with the fact table measurements.
A critical step necessary before steps 3 and 4.
Preferably it should be at the most atomic level possible.
“How do you describe a single row in the fact table?”

17. Step 3 - Identify the Dimensions
The list of all the discrete, text-like attributes that emanate from the fact table.
They are the “by” words used to describe the requirements.
Each dimension could be though of as an analytical “entry point” to the facts.
“How do business people describe the data that results from the business process?”

18. Step 4 - Identify the Facts
Must be true to the grain defined in step 2.
Typical facts are numeric additive figures.
Facts that belong to a different grain belong in a separate fact table.
Facts are determined by answering the question, “What are we measuring?”
Percentages and ratios, such as gross margin, are non-additive. The numerator and denominator should be stored in the fact table.

19. For More Information
Articles, Design Tips and Newsletters
Designing A Scalable DW/BI System
Microsoft BI Using the Kimball Method
Using SSIS to Populate a Kimball Method Data Warehouse