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Reviewing Data Warehouse Basics
Lessons
1. Reviewing Data Warehouse Basics
2. Defining the Business and Logical Models
3. Creating the Dimensional Model
4. Creating the Physical Model
5. Storage Considerations for the Physical Model
6. Strategies for Extracting, Transforming, and
Loading
7. Summary Management
8. Analytical Capabilities
Definition of a Data Warehouse
“A data warehouse is a subject-oriented, integrated,
nonvolatile, time-variant collection of data in support
of management’s decisions.”
- Bill Inmon
“A system that extracts cleans, conforms, and
delivers source data into a dimensional data store
and then supports and implements querying and
analysis for the purpose of decision making.”
- Ralph Kimball
Basic Elements of the Data Warehouse
• Source: Source database or other source form
• Data staging area: Intermediate area
• Target: Presentation server for the new data
warehouse or data mart
Source
Data
staging
area
Target
Diagram of a Data Warehouse System
Basic Form of the Data Warehouse
Customer
Location
Sales
Supplier
Product
Star schema (Dimensional model)
Data Warehouse and OLTP Database
Design Differences
Unlike an OLTP database design, a warehouse
database design must:
• Focus on queries
• Allow incremental development
• Be a nonvolatile structure
• Provide historical data
Data Warehouse Features
A data warehouse:
• Is a repository for information
• Improves access to integrated data
• Ensures integrity and quality
• Provides an historical perspective
• Records results
• Is used by a broad spectrum of end users for a
variety of purposes
• Reduces the reporting and analysis impact on
operational systems
• Requires a major systems integration effort
Exploring Data Warehouse
Characteristics
• Subject-oriented
• Nonvolatile
• Integrated
• Time-variant
Subject-Oriented
Data is categorized and stored by business subject
rather than by application.
OLTP applications
Equity
plans
Loans
Insurance
Savings
Data warehouse subject
Shares
Customer
financial
information
Integrated
Data on a given subject is integrated.
Savings
Customer
Current
account
Loans
Nonvolatile
Warehouse
Operational
Load
Insert
Update
Delete
Read
Read
Time-Variant
Time
Data
01/01
January
02/01
February
03/01
March
Data warehouse
Load from Many Sources
Internal
data
External
data
Nonrelational
systems
External
formats
Relational
databases
Archive data
Decision Support System (DSS)
DSS
ODS
DW
Storage
OLAP
Analytic
Profile of DSS Queries
DM
Data Warehousing Process
Extraction
Transformation/Load
Data marts
ETL
DDS
DDS
NDS
Server log
files
Metadata Repository
DDS
Portal
Access layer(s)
Transformations
ETL
Staging area(s)
Operational
External
Subscribe
Federated Data Warehouse
Flat files
RDBMS
Publish
Comparing Warehouses and
Data Marts
Data
warehouse
Property
Scope
Subjects
Data source
Implementation time
Versus
Data Warehouse
Enterprise
Multiple
Many
Months to years
Data
mart
Data Mart
Department
Single, LOB
Few
Months
Flow of Data
Feed
Operational
data
Store
Access
Relational
tools
Summary
data
Raw data
Metadata
OLAP
tools
External
data
Applications
Dependent Data Mart Model
Systems
Legacy
Data mart
ODS
Operational
Internal
Data mart
External
Enterprise
Data mart
Independent Data Mart Model
Systems
Legacy
Data mart
Data mart
Data mart
Data mart
Data mart
Data mart
ODS
Operational
Internal
External
Enterprise
Data Warehousing Today
• Business Intelligence
– To help business users understand their
business better
– To help them make better operational, tactical,
and strategic business decisions
– To help them improve business performance
Data Warehousing Today
• Customer Relationship Management
– Consists of applications that support CRM activities
– Single customer view
– Campaign segmentation
– Customer analysis
– Personalization
– Customer loyalty scheme
Data Warehousing Today
• Data Mining
– Known as Knowledge Discovery
– Trying to find meaningful and useful information from
a large amount of data
– Interactive or automated process to find patterns
describing the data and to predict the future behavior
of the data based on these patterns
• Usage
– Analyzing the shopping data
– Finding out the pattern between crime and location
– Customer scoring in CRM in terms of loyalty
– Credit Scoring in the credit card industry
Data Warehousing Today
• Master Data Management (MDM)
– Consolidates the master data and processes the data
through predefined data quality rules.
– Any changes on master data in OLTP are sent to
MDM
– Publishes data to other systems
• Customer Data Integration
– Is a MDM for customer data
– The process of retrieving, cleaning, storing,
maintaining and distributing customer data
Future Trends in Data Warehousing
• Unstructured Data
– Documents, images, audio, video, e-mails
• Search
– Search engine
• Service-Oriented Architecture (SOA)
• Real-Time Data Warehouse