Module 1: Introduction
to Data Warehousing
and OLAP
Raw Data vs. Business Information
Capturing Raw Data
z Gathering data recorded in everyday operations
Deriving Business Information
z Deriving meaningful information from raw data
Turning Data into Information
z Implementing a decision support system
OLTP Source Systems
OLTP System Characteristics
zProcesses real-time transactions of a business
zContains data structures optimized for entries and edits
zProvides limited decision support capabilities
OLTP Examples
zOrder tracking
zCustomer service
zPoint-of-sales
zService-based sales
zBanking functions
Data Warehouse Characteristics
Provides Data for Business Analysis Processes
Integrates Data from Heterogeneous Source Systems
Combines Validated Source Data
Organizes Data into Non-Volatile, Subject-Specific Groups
Stores Data in Structures that Are Optimized for Extraction and Querying
Data Warehouse System Components
Data Warehouse
Data Access
User Data Access Data
Sources
Data Input Staging
Area
Data Marts
Defining OLAP Solutions
OLAP Databases
Common OLAP Applications
Relational Data Marts and OLAP Cubes
OLAP in SQL Server 2000
OLAP Databases
Optimized Schema for Fast User Queries
Robust Calculation Engine for Numeric Analysis
Conceptual, Intuitive Data Model
Multidimensional View of Data
zDrill down and drill up
zPivot views of data
Common OLAP Applications
Executive Information Systems
z Performance measures
z Exception reporting
Sales/Marketing Applications
z Booking/billing
z Product analysis
z Customer analysis
Financial Applications
z Reporting
z Planning
z Analysis
Operations Applications
z Manufacturing
z Customer service
z Product cost
Relational Data Marts and OLAP Cubes
Relational Data Mart Relational Relational Data Mart
Data Mart OLAP CubeOLAP CubeOLAP Cube
Data Storage
Data Storage Relational
Data Structure Relational
Data Structure N-dimensional Data structure N-dimensional Data structure
Data Content
Data Content Detailed and
Summarized Data Detailed and
Summarized Data Summarized DataSummarized Data
Data Sources
Data Sources Relational and
Non-relational Sources Relational and
Non-relational Sources Relational and Non-relational Sources
Relational and Non-relational Sources
Data Retrieval
Data Retrieval Fast Performance for Data Extract Queries Fast Performance for
Data Extract Queries Faster Performance for Data Extract Queries Faster Performance for
Data Extract Queries
OLAP in SQL Server 2000
Microsoft Is One of Several OLAP Vendors
Analysis Services Is Bundled with Microsoft SQL Server 2000
Analysis Services Include
zOLAP engine
zData mining technology
Understanding Data Warehouse Design
The Star Schema
Fact Table Components
Dimension Table Characteristics
The Snowflake Schema
The Star Schema
Fact Table Dimension Table
Employee_Dim Employee_Dim Employee_Dim EmployeeKey EmployeeKey EmployeeID ...
EmployeeID ...
Time_Dim Time_Dim Time_Dim TimeKey TimeKey TheDate ...
TheDate ...
Product_Dim Product_Dim Product_Dim ProductKey ProductKey ProductID ...
ProductID ...
Customer_Dim Customer_Dim Customer_Dim CustomerKey CustomerKey CustomerID CustomerID Shipper_Dim
Shipper_Dim Shipper_Dim ShipperKey ShipperKey ShipperID ShipperID
Sales_Fact Sales_Fact TimeKey EmployeeKey ProductKey CustomerKey ShipperKey TimeKey EmployeeKey ProductKey CustomerKey ShipperKey Sales Amount Unit Sales ...
Sales Amount Unit Sales ...
Fact Table Components
Dimension Tables Dimension
Tables
customer_dim customer_dim customer_dim 201 ALFI Alfreds 201 ALFI Alfreds
product_dim product_dim product_dim 25 123 Chai 25 123 Chai
sales_fact Table
Measures Measures
customer_key
customer_keyproduct_keyproduct_keytime_keytime_keyquantity_salesquantity_salesamount_salesamount_sales Foreign Keys
Foreign Keys
201 25 134 400 10,789
The grainof the sales_fact table is defined by the lowest level of detail stored in each dimension
134 1/1/2000 134 1/1/2000time_dim time_dim time_dim
Dimension Table Characteristics
Describes Business Entities
Contains Attributes That Provide Context to Numeric Data
Presents Data Organized into Hierarchies
The Snowflake Schema
Defines Hierarchies by Using Multiple Dimension Tables
Is More Normalized than a Single Table Dimension
Is Supported within Analysis Services
Understanding OLAP Models
OLAP Database Components
OLAP Dimensions vs. Relational Dimensions
Dimension Fundamentals
Dimension Family Relationships
Cube Measures
Relational Data Sources
OLAP Database Components
Numeric Measures
Dimensions
Cubes
REGION
West CA
East OR
MA NY
REGION
West East
STATE REGION
CA West
OR West
MA East
NY East
OLAP Dimensions vs. Relational Dimensions
OLAP Relational
Dimension Fundamentals
USANorth West Oregon Washington South West
California USANorth West Oregon Washington South West
California USANorth West Oregon Washington South West
California USANorth West Oregon Washington South West
California USANorth West Oregon Washington South West
California USANorth West Oregon Washington South West
California
Dimension Family Relationships
USA is the parent of North West and South West
North West and South West are children of USA
North West and South West are siblings
North West and California are descendants of USA
North West and USA are ancestors of Washington
Oregon and California are cousins
All are dimension members USANorth West
Oregon Washington South West
California USANorth West Oregon Washington South West
California
Cube Measures
Are the Numeric Values of Principle Interest
Correspond to Fact Table Facts
Intersect All Dimensions at All Levels
Are Aggregated at All Levels of Detail
Form a Dimension
Relational Data Sources
Star and Snowflake Schemas
zAre required to build a cube with Analysis Services
Fact Table
zContains measures
zContains keys that join to dimension tables
Dimension Tables
zMust exist in same database as fact table
zContain primary keys that identify each member
Applying OLAP Cubes
Defining a Cube
Querying a Cube
Defining a Cube Slice
Working with Dimensions and Hierarchies
Visualizing Cube Dimensions
Connecting to an OLAP Cube
Defining a Cube
Q1 Q2 Q3 Q4
Time Dimension Products Dimension Detroit
Denver Chicago
Market Dimension
Apples CherriesGrapes Atlanta
Melons
Q1 Q2 Q3 Q4 Time Dimension
ProductsDimension Dallas
Denver Chicago
MarketsDimension
Apples CherriesGrapes
Atlanta Sales
Fact
Melons
Querying a Cube
Apples Q1 Q2 Q3 Q4
Time Dimension
ProductsDimension Detroit
Denver Chicago Atlanta
Markets Dimension
MelonsCherriesGrapes
Defining a Cube Slice
Working with Dimensions and Hierarchies
Dimensions Allow You to
zSlice
zDice
Hierarchies Allow You to
zDrill Down
zDrill Up
Visualizing Cube Dimensions
Connecting to an OLAP Cube
0 1000 2000 3000 4000 5000 6000
Bagels Muffins Sliced Bread Cheese Deli Meats Frozen Chicken
Bread Dairy Meat
Sheri Now mer - 2001 - Quarter 4 Sheri Now mer - 2001 - Quarter 3 Sheri Now mer - 2001 - Quarter 2 Sheri Now mer - 2001 - Quarter 1 Sheri Now mer - 2000 - Quarter 4 Sheri Now mer - 2000 - Quarter 3 Sheri Now mer - 2000 - Quarter 2 Sheri Now mer - 2000 - Quarter 1 State USA
Sales Units
Category Subcategory
Level 02 Year Quarter
Review
Introducing Data Warehousing
Defining OLAP Solutions
Understanding Data Warehouse Design
Understanding OLAP Models
Applying OLAP Cubes