Download Creating Tables

DW-2: Designing a Data
Warehousing System
용 환승
이화여자대학교
http://dblab.ewha.ac.kr/hsyong
ER Modeling

Defines the Detailed Relationships

Defines Entities That Are Fully Normalized

Follows Third Normal Form or Greater

Produces a Complex Database Design

Stores Data at the Lowest Level of Transactional Detail

Increases the Number of Joined Tables in Queries

Is Fixed in Nature
Dimensional Modeling

Defines Entities That Are Denormalized.

Follows the Relational Model but Violates the ER
Modeling Rules

Produces a Simple Database Design That Is Easily
Understood by Users

Stores Data at the Lowest Level of Transactional Detail
or Summarized Data

Decreases the Number of Joined Tables in Queries

Is Extendable
Modeling a Data Warehouse

Using a Star Schema

Components of a Star Schema

Using a Snowflake Schema

Choosing a Schema
Components of a Star Schema
Employee_Dim
EmployeeKey
EmployeeID
...
Time_Dim
TimeKey
Sales_Fact
TheDate
...
TimeKey
TimeKey
EmployeeKey
ProductKey
CustomerKey
ShipperKey
RequiredDate
...
Dimensional Keys
Product_Dim
ProductKey
ProductID
...
Multipart Key
Measures
Shipper_Dim
Customer_Dim
ShipperKey
CustomerKey
ShipperID
...
CustomerID
...
Using a Snowflake Schema
Sales_Fact
TimeKey
EmployeeKey
ProductKey
CustomerKey
ShipperKey
RequiredDate
...
Product_Dim
ProductKey
Product Name
Product Size
Product Brand ID
Product_Brand_ID
Product Brand
Product Category ID
Product_Category_ID
Product Category
Product Category ID
Choosing a Schema
Star schema
Snowflake schema
Overall Row Count
Higher
Lower
Model Understandability
Easier
More Difficult
Number of Tables
Less
More
Query Complexity
Simpler
More Complex
Dimensional Searching
Quicker
Slower
Supports
Inhibits
Bitmapped Indexing
Defining Dimension Characteristics


Applying Characteristics to Dimension Tables

Define a primary key

Use descriptive character data

Include highly correlated description columns
Designing for Usability and Extensibility

Minimize or avoid using codes or abbreviations

Create columns that are useful for levels of aggregation

Minimize the number of rows that will change over time
Identifying Dimension Hierarchies
Consolidated Hierarchy
Separate Hierarchy
Store Location
Continent
Store Location
Continent Country
State
City
Store
...
Continent
Country
...
...
...
...
...
Country
State or Province
...
...
State or Province
City
...
City
Store number
...
Store Number
01
...
Defining Conventional Dimensions

Time Dimension


Year, Quarter, Month, Week, Day, time
Geographic Dimension

Nation, state, city, district, location

Product Dimension

Customer Dimension
Sharing Dimensions Among Other Data Marts
Production
One instance exist and is
shared among data marts
Time
Multiple
instances exist in
individual data marts
Purchasing
Sales
Implementing a Star Schema

Estimating Size of the Data Warehouse

Creating a Database

Creating Tables

Creating Constraints

Creating Indexes
Creating a Database


Using CREATE DATABASE Options

SIZE

MAXSIZE

FILEGROWTH
Setting Database Options

Read-only: No Locking

Trunc. log on chkpt.: No Serious Recovery

SELECT INTO/Bulkcopy : No Logging
Implementing of a Star Schema ( example )
Implementing of a Star Schema ( example )

Creating Tables
CREATE table sales_fact_1997
( product_id int not null,
customer_id int not null,
store_id int not null,
time_id int not null,
store_sales float not null,
store_cost float not null,
unit_sales real not null )
Fact Table
Dimension Tables
CREATE table Product
( product_id int not null,
CREATE table Customer
product_class_id int not null,
( customer_id int not null,
product_family char(50) not null,
country char(50) not null,
product_department char(50) not null,
state_province char(50) not null,
product_category char(50) not null,
city char(50) not null,
product_subcategory char(50) not null,
lname char(100) not null,
brand_name char(255) not null,
product_name char(255) not null,
primary key (customer_id) )
primary key (product_id) )
CREATE table Store
( store_id int not null,
store_country char(50) not null,
store_state char(50) not null,
store_city char(50) not null,
primary key (store_id) )
CREATE table Time
( time_id int not null,
the_month char(15) not null,
quater char(2) not null,
the_year int not null,
primary key (time_id) )
Implementing of a Star Schema ( example )

Data Importing using DTS ( Data Transform Services )

Using the Query result to specify the data
Implementing of a Star Schema ( example )

Specify the Table
Implementing of a Star Schema ( example )

Define FOREIGN KEY Constraints
• ALTER TABLE sales_fact_1997
ADD foreign key (customer_id) references Customer
• ALTER TABLE sales_fact_1997
ADD foreign key (product_id) references Product
• ALTER TABLE sales_fact_1997
ADD foreign key (time_id) references Time
• ALTER TABLE sales_fact_1997
ADD foreign key (store_id) references store

Creating Composite Indexes
create index fact
on sales_fact_1997 ( product_id, customer_id, store_id, time_id )
Transforming Data
Convert
Transform
buyer_name reg_id Item_id total_sales
Adam Barr
II
32
17.60
Sean Chai
IV
48
52.80
Erin O’Melia VI
9
8.82
...
...
...
...
buyer_name reg_id Item_id total_sales
Adam Barr
2
32
17.60
Sean Chai
4
48
52.80
Erin O’Melia 6
9
8.82
...
...
...
...
Combine
buyer_first
Adam
Sean
Erin
...
buyer_last reg_id Item_id total_sales
Barr
2
32
17.60
Chai
4
48
52.80
O’Melia
6
9
8.82
...
...
...
...
buyer_name reg_id Item_id total_sales
Adam Barr
2
32
17.60
Sean Chai
4
48
52.80
Erin O’Melia 6
9
8.82
...
...
...
...
Calculate
buyer_name reg_id Item_id price_id qty_id
Adam Barr
2
1052
.55
32
Sean Chai
4
1023 1.10
48
Erin O’Melia 6
1069
.98
9
...
...
...
...
...
buyer_name reg_id Item_id total_sales
Adam Barr
2
32
17.60
Sean Chai
4
48
52.80
Erin O’Melia 6
9
8.82
...
...
...
...
Transforming Data with a Lookup Query
Data Warehouse
Look up Table
State_lookup
ST Abr.. State
Source Data
FL
Florida
WY
Wyoming
AR
Arkansas
Destination
Customer_source
Customer_dim
Name
State
Name
State
D. Smith
FL
D. Smith
Florida
L. Wilson
WY
L. Wilson
Wyoming
P. Salinger
Arkansas
P. Salinger AR
Transform
Components of a Cube
Location
Atlanta
Denver
Detroit
Member
Grapes
Product
Cherries
Dimension
Melons
Cell
Apples
Sales
Pears
Day 1
Day 2
Week 1
Properties
...
Week 2
...
January February ...
Levels
Q1
Q2 Q3
Period
Q4
Time
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Start
July 1
October 1
January 1
April 1
End
September 30
December 31
March 31
June 30
Storing Cubes

Storing in a ROLAP Structure

Storing in a MOLAP Structure

Storing in a HOLAP Structure

Comparing Storage Structures
Storing in a ROLAP Structure
Data Mart or
Data Warehouse
SQL Server
Oracle
Other
Data in OLAP
Environment
Relationship
Database
Relationship
Database
Relational
Database
ROLAP Data
ROLAP Aggregations
Storing in a MOLAP Structure
Data Mart or
Data Warehouse
SQL Server
Oracle
Data in OLAP
Environment
MOLAP
Data
MOLAP Data
MOLAP Aggregations
Other
Storing in a HOLAP Structure
Data Mart or
Data Warehouse
SQL Server
Data in OLAP
Environment
MOLAP
Data
MOLAP
ROLAP Data
Data
Oracle
Other
MOLAP Aggregations
Comparing Storage Structures
ROLAP
HOLAP
MOLAP
Base Data Storage
Relational
Table
Relational
Table
Cube
Aggregation Storage
Relational
Table
Cube
Cube
Query Performance
Fast
Faster
Fastest
Storage Consumption
Low
Medium
High
Maintenance
Low
Medium
High