Database Reference
In-Depth Information
24.3.1 Definitive Features
In the introduction, it was established that a data warehouse is an integrated,
subject-oriented, time-variant, nonvolatile database. Let us briefly examine what these
adjectives mean.
Subject-Oriented:
Data warehouses are designed to aid the analysis of data in order
to make decisions. For example, to learn more about your company's sales data, you can
build a warehouse that concentrates on sales. Using this warehouse, you can answer
questions like “Who was our best customer for this item last year?” This ability to define
a data warehouse by subject matter, sales in this case, makes the data warehouse
subject-oriented.
Integrated:
Integration is closely related to subject orientation. Data warehouses
typically contain data from disparate sources into a consistent format. They must resolve
such problems as naming conflicts and inconsistencies among units of measure. When a
data warehouse achieves this, it is said to be integrated.
Nonvolatile:
Nonvolatile means that, once entered into the warehouse, data should
not change. This is logical because the purpose of a warehouse is to enable you to analyze
historical data.
Time-Variant:
A data warehouse's focus may change over time; also, it could grow
(in terms of data volume, data structure and complexity).
24.3.2 Nature of Data Stored
Of importance also, is the nature of data stored in a data warehouse. A data warehouse
differs from an operational database in the nature of data stored. An operational database
consists of a set of normalized relational tables that store atomic data. A data warehouse on
the other hand, stores decision support data, often in non-normalized, aggregated formats.
Three distinctions can be made between operational data and decision support data:
Time Span:
Operational data represent atomic transactions
at specific points in time. Decision support data represent
(aggregated) data over a period of time.
Granularity:
Operational data is atomic; decision support data
is often aggregated. Data warehouses contain decision support
data that have been aggregated from various sources and
transformed to its intended format.
Dimension:
Operational data is instamatic; decision support
data is multi-dimensional, typically involving the dimension of
time as well as other factors of concern.
