THE DATA WAREHOUSE - Fundamentals of Database Management Systems - page 362

potential value of the data warehouse and are impatient to have it set up the way
that will help them help the company the most—even if that means that the design
of the data warehouse and the data in it are perpetually moving targets.

SUMMARY

A data warehouse is a historical database used for applications that require the
analysis of data collected over a period of time. A data warehouse is a database
whose data is subject oriented, integrated, non-volatile, time variant, high quality,
aggregated, possibly denormalized, and not necessarily absolutely current. There
are two types of data warehouses: the enterprise data warehouse and the data mart.
Some companies maintain one type, some the other, and some both.
Data warehouses are multidimensional databases. They are often designed
around the star schema concept. Building a data warehouse is a multi-step process
that includes data extraction, data cleaning, data transformation, and data loading.
There are several methodologies for using a data warehouse, including on-line
analytic processing and data mining. Data warehouses have become so large and so
important that it takes special skills to administer them.

KEY TERMS

Aggregated data
Data cleaning
Data enrichment
Data extraction
Data loading
Data mart
Data mining
Data transformation
Data warehouse
Data warehouse administrator

Decision support system (DSS)
Dimension
Drill-down
Enterprise data warehouse
Historic data
Integrated
Market basket analysis
Multidimensional database
Non-volatile
On-line analytic processing (OLAP)

Pivot or rotation
Slice
Snowflake design
Star schema
Subject oriented
Time variant
Transaction processing system
(TPS)

QUESTIONS

1. What is the difference between transactional pro-
cessing systems and decision support systems?
2. Decision support applications have been around for
many years, typically using captive files that belong
to each individual application. What factors led to
the movement from this environment towards the
data warehouse?
3. What is a data warehouse? What is a data warehouse
used for?
4. Explain each of the following concepts. The data in
a data warehouse:
a. Is subject oriented.
b. Is integrated.

c. Is non-volatile.
d. Is time variant.
e. Must be high quality.
f. May be aggregated.
g. Is often denormalized.
h. Is not necessarily absolutely current.
5. What is the difference between an enterprise data
warehouse and a data mart?
6. Under what circumstances would a company build
data marts from an enterprise data warehouse? Build
an enterprise data warehouse from data marts?
7. What is a multidimensional database?