8 copyright © 2009, oracle. all rights reserved. modeling multidimensional olap dimensions and...
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Copyright © 2009, Oracle. All rights reserved.8 - 2
Objectives
After completing this lesson, you should be able to:
• Use Warehouse Builder MOLAP dimension modeling capabilities– Value-based and skip-level hierarchies– Default hierarchy for multidimensional query tools
• Use Warehouse Builder MOLAP cube modeling capabilities– Conformed dimensions– Defining multiple cubes using the same dimensions at
different levels– Custom measures– Sparsity– Preaggregation
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
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What Is OLAP?
OLAP stands for online analytical processing.
• Online: You have access to live data (rather than static data).
• Analytical: You can analyze your data for reporting. You can create reports that are:– Multidimensional– Calculation-rich– Supported by time-based analysis– Ideal for applications with unpredictable, ad hoc query
requirements
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Examining an OLAP Question
• An OLAP question is a multidimensional query, as in the following:
“What was the percentage change in revenuefor a grouping of our top 20% products one year ago over a rolling three-monthtime period compared to the current periodthis year, for each region of the world?”
• This is a simple business question, but the actual query can be quite complex.
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
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Sales dimensioned by product, customer, and time
SALES cube
Product Customer
Time
Multidimensional Data Types
Data is stored in multidimensional cubes in the analytic workspace.
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Implementing a Dimensional Data Model with Multidimensional Data Types
You can use multidimensional data types when:
• Ad hoc usage patterns are unpredictable
• Query performance requirements are high
• The analytic requirements of the business include extended analytic, forecasting, and planning functionality
• Calculation requirementsare more extensive
• A data model that supports what-if analysis is required
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Dimensional Model
The multidimensional logical model has the following elements:
• Measures
• Dimensions
• Hierarchies
• Levels
• Attributes
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Measures
• Represent factual data (sometimes called “facts”; OWB groups measures in “cubes”)
• Are organized by one or more dimensions
• Populate the cells of a logical cube
• Can be numeric data, text, date, Boolean, and so on
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Measure Types
Measures are of two types:
• Stored measures:– Can store the result in a variable
• Calculated measures:– Can evaluate calculated data in a formula at query time
Stored Calculated
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Example of Measures in a Report
Storedmeasure
Calculatedmeasure
Crosstab report containing four measures:
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Dimensions
Dimensions:
• Form the “edges” ofthe measure
• Provide pointers tothe actual cellsinside themultidimensionalmeasures
Q1 Q2 Q3 Q4
Time
ProductAfrica
Europe
AsiaAmericas
SALES cube
RegionsLaptop
Camcorder
Camera
Monitor
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Example of Dimensions in a Report
Product
Time Customer Channel
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Quiz
Which of the following elements does the multidimensional logical model have?
a. Measures
b. Dimensions
c. Columns
d. Hierarchies
e. Levels
f. Attributes
g. Tables
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Hierarchies
• A hierarchy is a parent-child relationship among the members of a dimension.
• Hierarchies enable logical groupings of dimension members for the purposes of:– Navigation of data– Aggregation of measures– Allocation of data in a planning and budgeting application
• Dimensions usually have hierarchies.
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Hierarchy: Example
• Hierarchies enable you to navigate from the lowest level to the highest level, or from the highest to the lowest.
• You can aggregate data from the lowest level to the highest level.
SoftwareHardware
PCs Laptops Monitors X MY Z
L1 L2 L3 Y1 Y2 Y3
Total Product
…
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Levels
Levels
All Products
Category
Subcategory
Product
SoftwareHardware
PCs Laptops Monitors X MY Z
L1 L2 L3 Y1 Y2 Y3
Total Product
…
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Types of Hierarchy
Director
VP Admin
Admin
Director
VP
President
Day
Month
Quarter
Year
Level-basedhierarchy
Value-basedhierarchy
Director
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Attributes
• Attributes provide descriptive information about the dimension members.
• Attributes are also useful when you are selecting dimension members for analysis:– Select the products whose color (attribute) is “Blue.”– Select the customers who have two children. – Select the promotions that are of type “Multipack.”– Select all time periods whose description contains “January.”
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Attributes: Examples
Total
Karl
Karl
Bruce
Mary
John
John
Mary
Manager
Yellow sheets
Red sheets
White pillows
Red shirt
Green pants
Red pants
Blue shirt
Product
Yellow
Red
White
Red
Green
Red
Blue
Color
Sheets
Pillows
Bedding
Women’s
Men’s
Kids’
Clothing
SubcategoryCategory
Levels Attributes
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Dimensional Model Summarized
The multidimensional logical model has the following elements:
• Measures
• Dimensions– Hierarchies – Levels– Attributes
Time
Product
Customer
Item
Brand
Manufacturer
Month Quarter Year
Sales
Product Share
Sales Year to Date
Profit
Average Selling Price
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
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Analytic Workspace
• Is a container that holds multidimensional data and objects
• Is designed for efficient processing of multidimensional calculations
• Supports advanced calculations and rapid query performance
• Can be temporary or persistent
• Is a special table (LOB) in a tablespace (AW$tablename)
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Analytic Workspace
• Analytic workspaces are built using:– Oracle Warehouse Builder– Analytic Workspace Manager (AWM 11g)– AW XML API (for 10g form AWs)– OLAP Java API (for 11g form AWs)– OLAP DML
• An analytic workspace can be accessed by:– Business intelligence tools from Oracle– Business intelligence tools from partners– SQL– OLAP APIs
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Analytic Workspace: Creation and Maintenance Privileges
To create and maintain an analytic workspace, you must have the following:
• OLAP_USER role (automatically granted to an OWB run-time user when that user is created with OWB Repository Assistant)
• SELECT privileges on the source schema tables
• Sufficient quota on the tablespace in which the workspace is created
• SELECT, INSERT, and UPDATE privileges for the analyticworkspace
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OLAP DML
• Is a very powerful, multidimensional-aware language that enables developers to truly exploit the power of the OLAP option
• Supports the definition of multidimensional calculations, including customized calculations unique to your organization
• Contains an inventory of several hundred analytic functions
• Enables application developers to extend theanalytical capabilities of the AW
• Is accessible from the OWB Design Centeruser interface
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
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Tools to Build an Analytic Workspace
Cubebuilding
Extract, transform,load (ETL)
Oracle Warehouse Builder
Analytic Workspace Manager (AWM)
• OWB: Advanced ETL and AW deployment
• AWM: Builds an analytic workspace from clean data
Cleandata
Analyticworkspace
Source systems
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Quiz
An analytic workspace:
• Is a container that holds multidimensional data and objects
• Is designed for efficient processing of multidimensional calculations
• Supports advanced calculations and rapid query performance
• Is a special table (LOB) in a tablespace
a. True
b. False
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
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Dimensional Modeling
• Customers should need to think only about designing the dimension and cube, less about how the Oracle database stores data and performs extract, transform, load (ETL).
• Customers want to focus on product hierarchy; Oracle will figure out how and where to store data and perform ETL.
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Enabling OLAP Solutions
MOLAP?
SchemaLogicaldesign
AnalyticWorkspace
ETL
ETL
ROLAP?
AW created usingXML API to the
OLAP option
• Warehouse Builder 11g is capable of directly loading any data into the analytic workspace, allowing, for the first time, the wealth of transformation power on OLAP data loads.
• Create a logical design describing your OLAP cubes in dimensions, hierarchies, measures, calculated measures, and other components.
• Warehouse Builder uses the XML API to the OLAP option to directly create the AW and its objects and the metadata required in the database catalogs.
• After you have created your cubes and dimensions, use the Warehouse Builder ETL modelers to create the load programs, independent of storage decisions.
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Storage Management
• If you enter the name of an analytic workspace (AW) that does not exist, it will be created automatically when you deploy this object.
• If you do not specify an AW name, OWB will create an AW using the module name, as soon as you generate the dimensional object.
• If table space name is left blank, AW goes into the tablespace of the user.
Two choices for keys
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Dimensional Modeling Using OWB
Advanced modeling features enables full spectrum of dimensional capabilities, including:
• Value-based hierarchies
• Skip-level hierarchies
• And more
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Hierarchy Support: Value-Based
Day
Month
Quarter
Year
Level-basedhierarchy
Value-basedhierarchy
Adam
(VP)Smith
(Admin)
James
(Admin)
Bruce
(Director)
Jones
(VP)
KING (President)
Lex
(Director)Den
(Director)
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Create a Value-Based Hierarchy
Value-based hierarchies areonly for MOLAP dimensions.
Copyright © 2009, Oracle. All rights reserved.8 - 39
Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
Copyright © 2009, Oracle. All rights reserved.8 - 40
Calculated Measures
CUSTOMER
PRODUCT
TIME
Quantity Sold measure
X =
CUSTOMER
PRODUCT
TIME
PRODUCT
TIME
Unit Price measure
Revenue calculated measure
Quantity Price RevenueX =
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Generating Calculated Measures
Time Series
Share/Index
Rank
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Sparsity
If you mark a dimension as sparse, Warehouse Buildercreates a composite dimensionto manage sparse data.
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Cost-Based Aggregation
Cube wizard defaults to 20% of the lowest level of data to be precomputed.
Pre-aggregation slows the build, but speeds up queries.
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Level-Based Aggregation
Level-based aggregationis available for cubesdeploying to an Oracle release prior to 11g.
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Lesson Agenda
• OLAP concepts: Introduction
• Multidimensional data model– Measures– Dimensions– Hierarchies– Levels
• Analytic workspace
• Oracle Database 11g OLAP
• Warehouse Builder MOLAP dimension modeling
• Warehouse Builder MOLAP cube modeling
• Deploying MOLAP mappings
Copyright © 2009, Oracle. All rights reserved.8 - 49
Differences Between OLAP and Relational Loading
Differences between OLAP and relational deployment and loading:
Not much!(from the user perspective)
1. Deploy the data objects (dimensions and cubes).
2. Deploy the mappings.
3. Run the mappings to load data into an OLAP cube.
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No Relational Tables to Bind
Dimension Synchronize torepository object,
data stored inrelational table
Implementingtable
Dimension
Bind
ROLAP
MOLAP Deploy to DB,data stored in AW
Cube-organizedmaterialized view- Relational fact table- Summaries stored in AW
Schematable
AW
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Partially Predefined OLAP Module
Target module: SALES_AW
Target user: SALES_AW
• All dimensions are predefined.• All dimension-loading and cube-loading mappings are predefined.• Create the SALES cube.• Create the target user SALES_AW (test connecting with it in the SALES_AW_LOCATION).• Register SALES_AW_LOCATION with the DEFAULT_CONTROL_CENTER.• Deploy dimensions, cube, and mappings.• Execute mappings.
Sales
Customers Channels
Promotions
Times
Products
DB source module: XSALES
Channels, Promotions,Products, Addresses,Categories, Cities,Countries, Customers,Regions, Promo-subcategories, Promo-categories, Orders,Order_items
Mappings
Items in italicare predefined
SALES_AW module(mostly predefined)
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Examine the Predefined Dimensions and Mappings
You define the SALES cube.
Notice… no implementationtables!
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Define the Sales Cube
1. Define quickly and easily using a wizard.
2. Make further specifications using an editor.
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Associate the Module with the Target LocationModuleconfiguration
Controlcenter
Location
User
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Deploying OLAP Objects with Control Center Manager
Deploying OLAPobjects to AW ismuch like deployingrelational objects.
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Executing OLAP Mappings
“Set based fail overto row based” is beingchosen as the defaultoperating mode.
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View Cube Data in the Data Viewer
CHANNELS dimension
AMOUNT, COST, QUANTITY measures
PRODUCTS dimension
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Quiz
In cubes, preaggregating slows the build, but speeds up the queries.
a. True
b. False
Copyright © 2009, Oracle. All rights reserved.8 - 63
Summary
In this lesson, you should have learned how to:
• Use Warehouse Builder MOLAP dimension modeling capabilities– Value-based and skip-level hierarchies– Default hierarchy for multidimensional query tools
• Use Warehouse Builder MOLAP cube modeling capabilities– Conformed dimensions– Defining multiple cubes using the same dimensions at
different levels– Custom measures– Sparsity– Preaggregation
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