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Chapter 11
The Data Warehouse
Database Systems:
Design, Implementation, and
Management, Seventh Edition, Rob
and Coronel
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Multidimensional Data Analysis
Techniques (continued)
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Multidimensional Data Analysis
Techniques (continued)
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Advanced Database Support
Advanced data access features include:
Access to many different kinds of DBMSs, flat
files, and internal and external data sources
Access to aggregated data warehouse data as
well as to detail data found in operational
databases
Advanced data navigation
Rapid and consistent query response times
Ability to map end-user requests to appropriate
data source and then to proper data access
language (usually SQL)
Support for very large databases
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Easy-to-Use End-User Interface
Many of interface features are
“borrowed” from previous generations of
data analysis tools that are already
familiar to end users
Makes OLAP easily accepted and readily
used
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Client/Server Architecture
Provides framework within which new
systems can be designed, developed,
and implemented
Enables OLAP system to be divided into
several components that define its
architecture
OLAP is designed to meet ease-of-use as
well as system flexibility requirements
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OLAP Architecture
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OLAP Architecture (continued)
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OLAP Architecture (continued)
Designed to use both operational and data
warehouse data
Defined as an “advanced data analysis
environment that supports decision
making, business modeling, and an
operation’s research activities”
In most implementations, data warehouse
and OLAP are interrelated and
complementary environments
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OLAP Architecture (continued)
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OLAP Architecture (continued)
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Relational OLAP
Provides OLAP functionality by using
relational databases and familiar relational
query tools to store and analyze
multidimensional data
Adds following extensions to traditional
RDBMS:
Multidimensional data schema support within
RDBMS
Data access language and query performance
optimized for multi