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Module 02—Introduction to Dimensional Modeling Techniques Name Title Microsoft Corporation

Disclaimer The information contained in this slide deck represents the current view of Microsoft Corporation on the issues discussed as of the date of publication. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of publication. This slide deck is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT. Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this slide deck may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Microsoft Corporation. Microsoft may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this slide deck. Except as expressly provided in any written license agreement from Microsoft, the furnishing of this slide deck does not give you any license to these patents, trademarks, copyrights, or other intellectual property. Unless otherwise noted, the example companies, organizations, products, domain names, addresses, logos, people, places and events depicted herein are fictitious, and no association with any real company, organization, product, domain name, address, logo, person, place or event is intended or should be inferred. © 2008 Microsoft Corporation. All rights reserved. Microsoft, SQL Server, Office System, Visual Studio, SharePoint Server, Office PerformancePoint Server,.NET Framework, ProClarity Desktop Professional are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. The names of actual companies and products mentioned herein may be the trademarks of their respective owners. 4

Where Are We? Data Sources Staging Area Manual Cleansing Data Marts Data Warehouse Client Access 5

Module Overview Designing the Dimensional Model Introducing Additional Design Concepts 6

Designing the Dimensional Model Introducing the Star Schema Reviewing Star Schema Benefits Describing Dimension Tables Describing Fact Tables 7

Introducing the Star Schema 8

Reviewing Star Schema Benefits Transforms normalized data into a simpler model Delivers high-performance queries Delivers higher performing queries using Star Join Query Optimization Uses mature modeling techniques that are widely supported by many BI tools Requires low maintenance as the data warehouse design evolves 9

Describing Dimension Tables Reviewing Dimension Fundamentals Using Snowflake Dimension Tables Defining Hierarchies Defining Primary Keys in Dimensions 10

Reviewing Dimension Fundamentals Describe business entities Contain attributes that provide context to numeric data Present data organized into hierarchies 11

Using Snowflake Dimension Tables Define hierarchies using multiple dimension tables Support fact tables with varying granularity Simplify consolidation of data from multiple sources 12 Potential for slower query performance in relational reporting No difference in performance in Analysis Services database Potential for slower query performance in relational reporting No difference in performance in Analysis Services database

Defining Hierarchies Benefits −View of data at different levels of summarization −Path to drill down or drill up Implementation −Denormalized star schema dimension −Normalized snowflake dimension −Self-referencing relationship 13 Parent-child hierarchies will be covered later in this module

Defining Primary Keys in Dimensions Primary key purpose −Identifies uniqueness −Relates to foreign keys in a fact table Two candidates −Business key −Represents source primary key −Surrogate key −Consolidates multiple data sources −Consolidates multi-value business keys −Allows tracking of dimension history −Limits fact table width for optimization 14 Using a surrogate key is considered best practice

Describing Fact Tables Reviewing Fact Table Fundamentals Exploring Fact Table Examples Defining Foreign Keys in Fact Tables 15

Reviewing Fact Table Fundamentals Collection of measurements associated with a specific business process Specific column types −Foreign keys to dimensions −Measures – numeric and additive −Metadata and lineage Consistent granularity – the most atomic level by which the facts can be defined Optional primary key based on the dimension key columns 16

Exploring Fact Table Examples 17 Day Grain Quarter Grain Reseller sales data by: Product Order Date Reseller Employee Sales Territory Sales quota data by: Employee Time

Defining Foreign Keys in Fact Tables Enforce referential integrity between the dimension and fact tables May be disabled (or omitted) to optimize load performance 18

Introducing Additional Design Concepts Date Dimension Table Parent-Child Hierarchy Slowly Changing Dimensions 19

Date Dimension Table Most common dimension used in analysis (aka Time dimension) Conformed for consistent use across fact tables More efficient and flexible analysis than a date value in the fact table Useful common attributes – Year, Quarter, Month, Day −Time series analysis support −Navigation and summarization enabled with hierarchies, such as calendar or fiscal Single table design (typically not snowflake design) 20 Tip: Format the key of the dimension as yyyymmdd (e.g ) to make it readily understandable

Parent-Child Hierarchy A dimension that contains a parent attribute A parent attribute describes a self- referencing relationship, or a self- join, within a dimension table Common examples −Organizational charts −General Ledger structures −Bill of Materials 21

Parent-Child Hierarchy Example Brian Amy Stacia Stephen ShuMichael Peter José Syed 22

Slowly Changing Dimensions Support primary role of data warehouse to describe the past accurately Maintain historical context as new or changed data is loaded into dimension tables Implement changes by Slowly Changing Dimension (SCD) type −Type 1: Overwrite the existing dimension record −Type 2: Insert a new ‘versioned’ dimension record −Type 3: Track limited history with attributes 23 The concept of Slowly Changing Dimensions was introduced by Ralph Kimball

SCD Type 1 Existing record is updated History is not preserved 24

SCD Type 2 Existing record is ‘expired’ and new record inserted History is preserved Most common form of SCD 25

SCD Type 3 Existing record is updated Limited history is preserved Implementation is rare 26 SalesTerritoryKey update to 10

Introducing the AdventureWorksDW2008 Dimensional Model

Integrated data mart schema Deployment code Management tools Data Platform Information Worker Platform Microsoft BI Platform Integration – Data Marts Visio 2007 Database Model Office 2007 Data Dictionary SharePoint Dashboard 28 Performance Management Integrated BI Solution

Resources The Microsoft Data Warehouse Toolkit by Joy Mundy, Warren Thornthwaite, and Ralph Kimball The Data Warehouse Lifecycle Toolkit, 2 nd Edition by Ralph Kimball, et al. 29

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© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.