Kimball DimensionalModeling

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2004 Kimball University All rights reserved.

Contact information:

www.kimballgroup.com

Kimball Group

14898 Boulder Pointe RoadEden Prairie, MN 55347

Bob [email protected]

952.974.0434

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Our goal is that you walk away from this class armed with a process andsome basic techniques to begin to model your data dimensionally.

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2004 Kimball University. All rights reserved.

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Introductions and Fundamentals Page 1Basics Case Study Page 16

4-step processDegenerate dimensionsSurrogate keys

Factless fact tablesBeyond the 1st Data Mart Case Study Page 29

Star vs. snowflake variationsData warehouse bus matrixConformed dimensions

More Dimensional Fundamentals Page 40Slowly changing dimensions

Dimension and fact table indexingTransaction Detail Case Study Page 47

Dimension table role playing

Invoicing complicationsDesign Workshop Page 55

Completed Worksheets Page 61

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2004 Kimball University. All rights reserved.

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2004 Kimball University. All rights reserved.

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Data Mart #...Data Mart #...

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Data StagingArea

Data Mart Bus:Conformed facts anddimensions

Data Mart Bus:Conformed facts anddimensions

Services:Transform from

source-to-targetMaintain conform

dimensionsNo user query

support

Data Store:Flat files or

relational tables

Design Goals:Staging

throughputIntegrity /

consistency

Services:Transform from

source-to-targetMaintain conform

dimensionsNo user query

support

Data Store:Flat files or

relational tables

Design Goals:Staging

throughputIntegrity /

consistency

PresentationArea

Ad Hoc Query Tools

Report Writers

AnalyticApplications

Modeling:ForecastingScoringData mining

Ad Hoc Query Tools

Report Writers

AnalyticApplications

Modeling:ForecastingScoringData mining

Data AccessTools

SourceSystems

Extract

Load

Extract

Extract

Data Mart #2Data Mart #2

Data Mart #1DimensionalAtomic AND

summary dataBusiness-process

centric

Design Goals:Ease-of-useQuery

performance

Data Mart #1DimensionalAtomic AND

summary dataBusiness-process

centric

Design Goals:Ease-of-useQuery

performance

Access

Data Staging Area:Portion of the data warehouse restricted to EXTRACTING, CLEANING, andLOADING data from legacy systems into destination data marts.

The data staging area is the back room and is explicitly off limits to the endusers, akin to the kitchen area of a restaurant.The data staging area DOES NOT SUPPORT QUERY OR PRESENTATION

SERVICES.The data staging area is dominated by sorting and sequential processing. Thepre-cleaned data is almost always in a flat file format, and the post cleaneddata is either in a flat file format or third normal form.

Presentation Area:Portion of the data warehouse restricted to PRESENTING data, not cleansingor transforming data.Organized into data marts, focused on a a specific BUSINESS PROCESS (notbusiness function, business function or business unit) subject area.

A data mart often contains ATOMIC DATA, as well as more summarized

data.

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Data StagingArea

Data AccessTools

SourceSystems

Extract

Extract

Extract

Enterprise DataWarehouse

Normalizedtables

Atomic dataUser query

support toatomic data

Enterprise DataWarehouse

Normalizedtables

Atomic dataUser query

support toatomic data

Access

Presentation Area

Extract,transform &

loadLoad

centriccentric

Data Mart #1DimensionalSummary dataDepartmental-

centric

Data Mart #1DimensionalSummary dataDepartmental-

centric

Access

There are a number of similarities between the two approaches:Integrated data with common, conformed dimensions.

Dimensional data marts.Unique characteristics of this approach include the following:

Mandatory, persistent normalized data structures result from stagingprocess. Some ETL occurs to load the data warehouse, and more ETLoccurs to load the marts.Dimensional structures are for summary data only.Users are given limited access to the detailed data in ER structures.

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2004 Kimball University. All rights reserved.

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2004 Kimball University. All rights reserved.

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2004 Kimball University. All rights reserved.

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Although well be focused on designing dimensional models for a relationaldatabase, much of what well be discussing is relevant to multidimensionaldatabases, too.

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2004 Kimball University. All rights reserved.

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The star schema is a dimensional design for relational databases.Dimensional models implemented in multidimensional OLAP databases(such as Hyperions Essbase) are referred to as data cubes.A star schema is characterized by a single data table or fact table, surroundedby descriptive tables or dimension tables. The data warehouse will ultimatelyconsist of many star schemas.The retail industry, the first to adopt this design technique, typically had fouror five dimensions, hence the term star was coined.

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PRODUCT KEY

Product Desc.SKU #SizeBrand Desc.Class Desc.

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Dimensions or