an introduction to dimensional data warehousing design
TRANSCRIPT
8/8/2019 An Introduction to Dimensional Data Warehousing Design
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An Introduction to Dimensional DataWarehouse Design
Presented by
Joseph J. Sarna Jr.
JJS Systems, LLC
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Relational Normal Form
Most relational databases are set to 3rd normal form
1st Normal form ± Tables have unique keys and no repeating
groups or multi-value fields
2nd Normal form ± Every attribute is dependent ont the entire
key of the table
3rd Normal form ± Attributes are dependent only on the key.No derived elements
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Northwind Database Model ± Relational Format
Categories
PK CategoryID
I1 CategoryName
Description
Picture
Territories
PK TerritoryID
TerritoryDescription
FK1 RegionID
Products
PK ProductID
I3 P ro du ctN am e
FK2,I4,I5 SupplierID
FK1,I2,I1 CategoryID
QuantityPerUnit
UnitPrice
UnitsInStock
UnitsOnOrder
ReorderLevel
Discontinued
CustomerCustomerDemo
PK,FK2 CustomerID
PK,FK1 CustomerTypeID
CustomerDemographics
PK CustomerTypeID
CustomerDesc
EmployeeTerritories
FK2 TerritoryID
FK1 EmployeeID
Customers
PK CustomerID
I2 CompanyName
ContactName
ContactTitle
Address
I1 City
I4 Region
I3 PostalCode
Country
Phone
Fax
Region
PK RegionID
RegionDescription
Order Details
PK,FK1,I2,I1 OrderID
PK,FK2,I4,I3 ProductID
UnitPrice
QuantityDiscount
Shippers
PK ShipperID
CompanyName
Phone
Orders
PK OrderID
FK1,I1,I2 CustomerID
FK2,I4,I3 EmployeeID
I5 OrderDate
RequiredDateI6 S hippedDate
FK3,I7 ShipVia
Freight
ShipName
ShipAddress
ShipCity
ShipRegion
I 8 S hi pP os ta lC od e
ShipCountry
Suppliers
PK SupplierID
I1 CompanyName
ContactName
ContactTitle
Address
City
Region
I2 PostalCode
Country
Phone
FaxHomePage
Employees
PK EmployeeID
I1 LastName
FirstName
Title
TitleOfCourtesy
BirthDate
HireDate
Address
City
Region
I2 Pos ta lCode
Country
HomePhone
Extension
PhotoNotes
FK1 ReportsTo
PhotoPath
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Why Dimensional Data Warehouses?
Business needs to analyze data so that it can:
± Understand trends
± Predict future behavior and needs
± Personalize contact with customers
± Be competitive
All of this in a speedy manner, with the ability to do³What if¶s´
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Drawbacks to Relational Data Structures
Data is not structured for analytical usage
Multiple Joins are resource intensive
Missing data from external sources, context history, not
operational sources
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What Is a Dimensional Data Warehouse?
³A structured repository of validated and integrated
historical information accessible to business people to
provide the basis for both tactical and strategic
business decisions.´
Centralized extract and staging
Separate from operational system
Structured for analysis Historically contexted
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Dimensional Data Warehouse Architecture
Relational Data External Data Enterprise Data
Data DistributionAcquisition, Staging, Cleaning,
Transformation
Data Warehouse
Storage
Analytical
Applications
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Levels of Design
Detail Level
± Dimensional Normal form
± Value and feasibility
Analytical Level
± Structured for the required analyses
Summary Level ± Summaries for user requirements
± Better response time
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Dimensional Normal Form
Normalized for maintainability
De-normalized for performance, based on rules
2 level structure, therefore only one level of joins
required for queries
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Northwind Database Model ± Dimensional Format
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Cu stom er Cu stom er D e m o
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lier £
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Dimensional Schema
Fact Tables
± contain related measures
± Usually the largest tables
± Usually appended to ± Can contain detail or summary data
± Measures are usually additive
Dimension Tables
± Contain descriptors
± Utilize business terminology
± Textual and discrete data
± Attributes through which the table measures are analyzed
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Northwind Database ± Star Schema ± rders
d im C u sto m e rs
'
K C u s to m er K e y
C u sto m e rID
C o m p a n yN a m e
C o n ta ctN a m e
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A d d re s s
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P h o n e
F a x
C u st o m e rT yp e ID
C u sto m e rD e sc
d im S h ip p e rs
P K S h ip p e rK e y
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F K 3 P ro d u ctK e y
F K 2 E m p lo ye e K e y
F K 1 C u sto m e rK e y
F K 4 S h ip p e rK e y
F K 6 O rd e rD a te K e y
F K 5 R e q u ire d D a te K e y
F K 7 S h ip p e d D a te K e y
O rd e rIDS h ip V ia
F re ig h t
S h ip N a m e
S h ip A d d re ss
S h ip C ity
S h ip R e g io n
S h ip P o st a lC o d e
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d im E m p lo y e e s
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K E m p lo ye e K e y
E m p lo ye e ID
L a stN a m e
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d im O rd e rD e ta ils
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Q u a n tit yP e rU n it
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U n its O n O rd e r
R e o rd e rL e ve l
D is co n tin u e d
C a te g o ryIDC a te g o ry N a m e
D e scr ip tio n
S u p p lie rID
C o m p a n yN a m e
C o n ta ctN a m e
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d im D a te
'
K D ate K ey
D a yD a te
D a yD a te _ Y Y Y Y M M D D
D a yO fW e e kN a m e
D a yO f W e e kN a m e A b b rv
D a yN u m b e rIn W e e k
D a yN u m b e rIn M o n th
D a yN u m b e rIn Q u a rte
D a y N u m b e rIn Y e a r
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W e e kE n d In d ica to r
W e e k_ Y Y Y Y W W
W e e k N u m b e rIn Y e a r
M o n th _ Y Y Y Y M M
M o n th N a m e
M o n th N a m e A b b rv
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Q u a rte r_ Y Y Y Y QQ u a rte rN a m e
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Q u a rte r N u m b e rIn Y e a r
Y e a r
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Resources
Books
± The Data Warehouse Toolkit, Ralph Kimball
± The Data Warehouse Lifecycle Toolkit, Ralph Kimball, et al
± Data Warehouse Design Solutions, Adamson / Venerable
Websites
± http://www.ralphkimball.com/
² http://www.atre.com
² http://www.microsoft.com/sql/evaluation/overview/dataware.asp