The document discusses the components and architecture of data warehouses and data marts. It describes how a data warehouse collects data from multiple operational systems and makes it available for analysis. Data marts contain subsets of data tailored for specific business functions or departments. The document outlines different types of data warehouse architectures including virtual, coarse-grained, central, distributed, and data marts-only. It also discusses challenges like integrating dirty data from multiple sources and prerequisites for a successful data warehouse implementation.

1. Data Warehouse/Data Mart
Components
Concepts
Characteristics

2. Overview
• Operational vs Informational Systems
• Data Warehouse components
• Data Marts

3. Basic Data Warehouse
Architecture
One Version
Source OLTP
of the Truth Subset Data Marts
Systems
Enterprise
Data
Warehouse
Copyright © 1997, Enterprise Group, Ltd.

4. Operational vs. Informational
Systems
Order Operational
Entry Manf.
Systems
Information Access Today

5. Operational vs. Informational
Systems
Operational
Systems
Informational
Systems
Information Access Today

6. Operational vs. Informational
Systems
• Most of the advances in end-user programming have run into
difficulty in actually accessing data that exists in backbone,
operational data bases.
• Operational data bases have a very, very long life. Large operational
systems are converted from one technology to a more advanced one
very infrequently (typically every eight to twenty years).
• Therefore, why not create specific DBs whose role was to make large
scale end user access easy to isolate the operational DBs, i.e. a Data
Warehouse

7. Operational vs. Informational
Systems
Operational
Systems
Information
Delivery System
Informational
Systems

8. Operational vs. Informational
Systems
Operational
Systems
Data
Information
Warehouse
Delivery System
Informational
Systems

9. Operational vs. Informational
Systems
Operational
Systems
Data
Information
Warehouse
Delivery System
Informational
Systems

10. Operational vs. Informational
Systems
Operational
Systems
Data
Information
Warehouse
Delivery System
Informational
Systems

11. Operational vs. Informational
Systems
Notice that one of the big impacts of
Operational
Data Warehousing is to eliminate large
Systems
numbers of existing DSS systems!
Y2000 will make this essential!!!
Data
Information
Warehouse
Delivery System
Informational
Systems

12. Operational vs. Informational
Systems
Operational
Systems
Data
Information
Data Warehouse
Delivery System
Marts
Informational
Systems

13. Data Marts vs Data Warehouses
Internet/Intranet Layer 11
direct queries
virtual queries
ad hoc queries Virtual DW
Coarse DW
Operational Data
Central DW
Layer 2a
Distributed DW
North America Core DW Layer 3 External Data
Layer
United States
$11,000
Sales
United States
2b
by Sales
$10,340to $10,350 (1)
$8,730to $10,340 (2)
$4,320to $8,730 (2)
$1,100to $4,320 (1)
$730to $1,100 (3)
Presentation/ Data Feed/ Data Non-operational
Desktop Access Data Mart Data Mining/ Data Staging and Access Data
Layer 1 Layer 4 Indexing Layer 6 Quality Layer Layer 7 Layer 2c
5
Meta-data Repository Layer 8
Warehouse Management Layer 9
Application Messaging (Transport) Layer 10

14. Central Data Warehouse
Internet/Intranet Layer 11
direct queries
virtual queries
ad hoc queries
Tracking DB
Lawson DB
Operational Data
Central DW
Layer 2a
North America Core DW Layer 3 External Data
Layer
United States
$11,000
Sales
United States
2b
by Sales
$10,340to $10,350 (1)
$8,730to $10,340 (2)
$4,320to $8,730 (2)
$1,100to $4,320 (1)
$730to $1,100 (3)
Presentation/ Data Feed/ Data Non-operational
Desktop Access Data Mart Data Mining/ Data Staging and Access Data
Layer 1 Layer 4 Indexing Layer 6 Quality Layer Layer 7 Layer 2c
5
Meta-data Repository Layer 8
Warehouse Management Layer 9
Application Messaging (Transport) Layer 10

16. Virtual Date Warehouse
• A Virtual Data Warehouse approach is often
chosen when there are infrequent demands for
data and management wants to determine if/how
users will use operational data.
• One of the weaknesses of a Virtual Data
Warehouse approach is that user queries a made
against operational DBs.
• One way to minimize this problem is to build a
“Query Monitor” to check the performance
characteristics of a query before executing it.

17. • A Coarse Data Warehouse is often chosen when the
organization has a relatively clean/new operational
system and management wants to make the operational
data more easily available for just that system.
• A Central Data Warehouse
• is often chosen when the organization has a clear
understanding about it Information Access needs and
wants to provide “quality”, “integrated” , information to
its knowledge workers
• A Distributed Data Warehouse is similar in most respects
to a Central Data Warehouse, except that the data is
distributed to separate mini-Data Warehouses (Data
Marts )on local or specialized servers

18. Central Data Warehouse
Internet/Intranet Layer 11
direct queries
virtual queries
ad hoc queries Virtual DW
Coarse DW
Operational Data
Central DW
Layer 2a
Distributed DW
North America Core DW Layer 3 External Data
Layer
United States
$11,000
Sales
United States
2b
by Sales
$10,340to $10,350 (1)
$8,730to $10,340 (2)
$4,320to $8,730 (2)
$1,100to $4,320 (1)
$730to $1,100 (3)
Presentation/ Data Feed/ Data Non-operational
Desktop Access Data Mart Data Mining/ Data Staging and Access Data
Layer 1 Layer 4 Indexing Layer 6 Quality Layer Layer 7 Layer 2c
5
Meta-data Repository Layer 8
Warehouse Management Layer 9
Application Messaging (Transport) Layer 10

19. Data Marts Only
Internet/Intranet Layer 11
direct queries
virtual queries
ad hoc queries Virtual DW
Coarse DW
Operational Data
Central DW
Layer 2a
Distributed DW
North America Core DW Layer 3 External Data
Layer
United States
$11,000
Sales
United States
2b
by Sales
$10,340to $10,350 (1)
$8,730to $10,340 (2)
$4,320to $8,730 (2)
$1,100to $4,320 (1)
$730to $1,100 (3)
Presentation/ Data Feed/ Data Non-operational
Desktop Access Data Mart Data Mining/ Data Staging and Access Data
Layer 1 Layer 4 Indexing Layer 6 Quality Layer Layer 7 Layer 2c
5
Meta-data Repository Layer 8
Warehouse Management Layer 9
Application Messaging (Transport) Layer 10

20. Heterogeneity - The Reality
i2 Supply Chain Oracle Financials Siebel CRM 3rd Party
Data
Packaged
Custom
Oracle
Marketing
Financial
Data
Data
Warehouse
Warehouse
Packaged
I2 Supply Chain Subset
Non- Architected
Data Mart Data Marts

21. Federated BI Architecture
i2 Supply Chain Oracle Financials Siebel CRM 3rd Party e-commerce
Common
Staging
Area Real Time
ODS
Federated Federated
Financial Marketing
Data Data Real Time
Warehouse Warehouse Data Mining
and Analytics
Federated
Packaged Real Time
I2 Supply Subset
Data Marts Segmentation,
Chain Classification,
Data Marts Qualification,
Analytical Offerings, etc.
Applications

22. Benefits of Data Warehouse
Architecture
• Provides organizing framework
• Gives flexibility for changes and allows
simplified maintenance
• Speeds up future development by aiding
understanding of dw
• Communication tool for roles and
requirements
• Coordinate data marts

23. Primary Technical Challenge Axis
Dirty Data Large Co.
Slow Parallel Near
ERP DW Real
Custom
Monthly VLDB Time
ERP DW
Freq Turnkey
Finance
ERP DW
Multi-Source
Small DB Mid-Size Co.
Marketing
Single Source
Fast Clean Data
Easy Hard

24. Prerequisites for Success
• Pain driven
• Sponsorship at the highest levels
• Sustainable political will
• Iterative methodology
• Manageable scope
• User driven design
• Service business mindset
• Sustainability