Data Warehousing
“An Introduction”
Dr. Akhtar Ali
School of Computing, Engineering and
Information Sciences
Database Administration (CG168) – Lecture 10a: Introduction to Data Warehousing
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Lecture Outline

New Trends for data/information management
 Background
 Two Approaches

Data Warehousing (DW)
 Definitions and History

DW Architectures
 Strategies for building data warehouses

Problems and Issues
 Maintenance and Performance

DW Support in database management systems
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1: New Trends for
data/information management

Secondary storage is becoming more and more affordable.
 So enterprises keep more and more data
 Data replication is becoming widespread to avoid single point
of failure

What to do with large volumes of data ?
 Decision makers want to get more of data
 Decision support systems (DSSs)
» Have long execution time
» Are CPU-intensive
» Involve Statistical Analysis/Analytical queries

Transaction-oriented databases are not suitable for DSSs.
 Transactional data usually change rapidly
 Database and application servers are already at peak loads
 Transactional data is usually normalized while DSSs require
summarised and highly aggregated data – and possibly denormalized
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Data Management
Past, Present and Future

Past
 File Processing (e.g. COBOL)
 Network and Hierarchical Databases

Present
 Relational, Object-Relational and Object-Oriented Databases
 Fragmentation of Information Systems
» Subject/User/Application-Driven Transaction Processing Systems
» Stand-alone systems e.g.




Manufacturing (Inventory Control)
Finance (Payroll, Stock Management)
Sales Administration (Planning, Suppliers, Daily Sales)
Future
 Integration of Data and Applications
 Data Exchange, Interoperability and Homogeneity in the
presence of Heterogeneity.
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Surviving in the Information
Jungle



Different interfaces and protocols
Different data models and representations
Duplicate and Inconsistent Information
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Solution
Integrated Information Store

Integration Systems
 Collect and combine information from multiple sources
 Provide integrated view and uniform user interface
 Support sharing of data and processing capabilities
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Two Approaches
1: On-Demand/Query-Driven

On-Demand (Lazy) Data Integration is a kind of Virtual Data
Warehouse
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Disadvantages of On-Demand
Approach

Poor response time due to delay in query processing
 Slow or unavailable data sources
 Time consuming and complex filtering and integration



Inefficient and potentially expensive for frequent queries
Wrappers compete on resources with local applications at
data sources
There are only few notable systems based on this approach
e.g.
 TAMBIS: Transparent Access to Multiple Bio-informatics
Information Systems
 SRS: Sequence Retrieval System
 OPM (Object Protocol Model) based multi-database tools and
query language (OPM-QL)
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Two Approaches
2: Data Warehousing


In advance/
Eager data
integration
Integrated
data is
persistently
stored in a
database –
data
warehouse
for direct
querying and
analysis
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Advantages of Data Warehousing
Approach

High performance query processing
 Though the information returned may not be most up-to-date

Does not interfere with local data processing at sources
 Analytical Querying/Statistical Analysis or On-Line Analytical
Processing (OLAP) at warehouse
 On-Line Transaction Processing (OLTP) at data sources

Data Persistently Stored at Warehouse
 Data at the warehouse can be further re-structured,
aggregated, summarized and modified if necessary.
 A DW may store historical/archive data.

Data warehousing approach has been widely used e.g.





The Maryland ADMS Project
Supporting Data Integration and Warehousing Using H2O
The Stanford Data Warehousing Project
GIMS: Genome Information Management System
Marks & Spencer Data Warehouse
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Trade-off between Query-Driven
and
Data Warehousing Approaches

Query-driven approach is still better for:
 Rapidly changing information/data sources;
 Accessing very large amounts of data from many sources;
 Clients with unpredictable and dynamic requirements

Data Warehousing is more suitable when:
 Data sources on which a data warehouse is based are not
frequently changing;
 Data up-to-dateness is not crucially important;
 Querying and Analysis is complex;
 Data needs to be highly summarized and aggregated;
 Fast access to integrated and derived data is vital; and
 Keeping data warehouse consistent with the underlying data
sources is efficient and does not compromise on expected
performance.
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What is a Data Warehouse?
(a practitioner’s viewpoint)

“A data warehouse is simply a single, complete,
and consistent store of data obtained from a
variety of sources and made available to end
users in a way they can understand and use it in
a business context” – Barry Devlin, IBM
Consultant

“A data warehouse is a database of data gathered
from many systems and intended to support
management reporting and decision making” –
Michael Corey et al, CTO of OneWarranty.com
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What is a Data Warehouse?
(classical viewpoint)
Subject
Oriented
Integrated
Data
Warehouse
Non Volatile
Time Variant
According to W. H. Inmon
(Building a Data
Warehouse, 1992)
“A DW is a subjectoriented, integrated,
time-varying, non-volatile
collection of data that is
used primarily in
organizational decision
making.”
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In a Nutshell, a DW is

A persistent collection of diverse data
 Generally speaking, an efficient solution to data
integration
 A single repository of information

Subject-Oriented
 Organized by subject (not by application)
 Used for analysis, reporting, data mining, etc.


Structured and optimized differently from transactionoriented databases
User interface aimed at executive – decision makers
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Data Warehouse History
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Standard DB v. DW
Standard Database
Data Warehouse

Mix of updates and querying

Many small-medium
transactions



MBs to GBs in size
Most Current snapshot

Heavily indexed


Raw Data
Thousands of users (e.g.
clerical to mid-level-mangers)






Mostly reads (infrequent
updates, append-only – very
rarely data is deleted)
Queries are complex and longrunning
GBs to TBs in size
Not the most current
snapshot/Historical
Lots of scans (as data is
readily accessible)
Summarized/Aggregated
Hundreds of users (e.g.
decision-makers, analysts)
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Architectures (I)
Simple

Metadata and raw data of a traditional OLTP system is present, as is an
additional type of data, summary data. Summaries are very valuable in
data warehouses because they pre-compute long operations in advance.
For example, a typical data warehouse query is to retrieve something like
December sales.
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Architectures (II)
With Staging Area
We need to clean and process operational data before putting it into the
warehouse. We can do this programmatically, although most data
warehouses use a staging area instead. A staging area simplifies building
summaries and general warehouse management.
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Architectures (III)
With Staging Area + Data Marts
This is a customized warehouse architecture for different groups within
an organization. By adding data marts, which are systems designed for a
particular line of business, we can build a more customized DW.
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Problems and Issues

Warehouse Maintenance
 Data sources (DSs) on which a DW is based may change over
time.
 Changes at DSs may require changes at a DW.
 How often to propagate changes to a DW?
» At night, weekly/fortnightly/monthly, immediately, etc.
 How to propagate changes to a DW?
» Completely re-build all affected tables at the DW (easy but
inefficient)
» Apply changes to affected tables incrementally (efficient but
difficult)

Performance
 How to assess if a DW is performing well?
 How to improve performance?

Miscellaneous Issues
 Data Quality Assurance (How good is data in a DW?)
 How to cope with data warehouse evolution?
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Data Systems Supporting DW
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Oracle 8i, 9i
IBM DB2
Sybase
RedBrick Data Warehouse/Informix
MS SQL Server
Tandem (HP)
Teradata
MicroStrategy
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Bibliography



Advanced Topics in Database Systems by Sharma
Chakravarthy, 2001, University of Texas at Arlington, USA.
Oracle9i Data Warehousing Guide Release 2 (9.2), 2002.
Oracle 8i Data Warehousing by Michael Corey, Michael
Abbey, Ian Abramson, Ben Taub, Oracle Press, 2001.
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