DIMENSIONAL MODELINGA primer on data modeling techniques for data warehouse design

By Rauf Ahmed

Agenda

What is a Data Warehouse? What problem it solves? Where does Dimensional Modeling fit in? Basic concept of Dimensional Modeling Foundation of Design Concepts Q & A

Data Warehouse

“Data warehousing is the design and implementation of processes, tools, and facilities to manage and deliver complete, timely, accurate, and understandable information for decision making. It includes all the activities that make it possible for an organization to create, manage, and maintain a data warehouse or data mart.”

(IBM Data Modeling Techniques for Data Warehousing)

Data Warehouse Goals

Easy information access Consistent information presentation Adaptive and resilient to change Information assets protection Foundation for improved decision making Acceptable by Business Community

(The Data Warehouse Toolkit)

Data Analysis Techniques

(IBM Data Modeling Techniques for Data Warehousing)

Query

Analyze

Discover

Data Warehouse Basic Elements

(The Data Warehouse Toolkit)

Data Presentation Area

Key Considerations… Dimensional Model Vs Normalized Model Global Data Warehouse Vs Independent Data Marts Top-down Vs Bottom-up Atomic Vs Summarized Data

(The Data Warehouse Toolkit)

Dimensional Model Components

A fact is a collection of related data items, consisting of measures and context data. A fact contains the information the business

is interested in

A dimension is a collection of members or units of the same type of views. A

dimension is the window to the information contained in the facts

Dimensional Model Myths

Dimensional models and data marts are for summary data only.

Dimensional models and data marts are departmental, not enterprise, solutions and Dimensional models and data marts can’t be integrated

Dimensional models and data marts are not scalable Dimensional models and data marts are only

appropriate when there is a predictable usage pattern

(The Data Warehouse Toolkit)

Dimensional Model Process

Select business process to model

Declare grain of the business process

Choose dimensions that apply to each fact table row

Identify numeric facts that will populate each fact table row

Sample Dimensional Model

(The Data Warehouse Toolkit)

Design Concepts 1

Snow flake vs Star Schema How many dimensions? Degenerate Dimensions Surrogate Keys Null Keys Handling Date Dimension and its Surrogate Key Factless Fact Tables

(The Data Warehouse Toolkit)

Design Concepts 2

Periodic Snapshots Semi-additive facts Accumulating Snapshots Bus Architecture

Conformed Dimensions Slowly Changing Dimensions

Overwriting the value Adding Dimension Row Adding Dimension Column

(The Data Warehouse Toolkit)

Design Concepts 3

Role Playing Dimensions Junk Dimension (Indicators) Fact Normalisation Multiple Currencies

Currency Conversion Fact Header & Line Facts (different granularity) Multiple UOM

(The Data Warehouse Toolkit)

Design Concepts 4

(The Data Warehouse Toolkit)

Design Concepts 5

(The Data Warehouse Toolkit)

Aggregated Facts as Attributes Age Groups Volume Buckets Spend Buckets etc

Dimension Outriggers Category Dimension (Start Date)

Time Intelligence YTD, QTD, CY, LY, CM, LM, etc

More Design Concepts…

Partitioning Rapidly changing dimensions Bridge Tables (Variable Depth Hierarchies) ClickStream Analysis Audit Dimensions Building Data Warehouse Basket Analysis

(The Data Warehouse Toolkit)

References

Books: The Data Warehouse Toolkit (Ralph Kimball, Margy Ross) Mastering Data Warehouse Design (Wiley Press) Building the Data Warehouse (W. H. Inmon) Data Modeling Techniques for Data Warehousing (IBM Press)

Internet:http://www.kimballgroup.com/html/designtips.htmlhttp://www.inmoncif.com/home/http://inmoninstitute.com/