The principal notion behind data warehousing is
that the data stored for business analysis can most efficiently be accessed by
dividing it from the data in the operational systems. A data warehouse,
therefore, is a collection of data gathered from one or more data repositories
to create a new, central database. For example an industry may create a data
warehouse by extracting the operational data it has accumulated concerning the
workers information, products they are working on, material they are using for
making the particular product, output production and etc,. Data Warehousing is
not just the data in the warehouse, but also the architecture and tools to
collect, query, analyze and present information.
The characteristics of a data warehouse were
first defined by W.H. Inmon who stated, “A data warehouse is subject-oriented,
integrated, time-variant and non-volatile [data] collection in support of
management decision making processes”. Let’s discuss that definition
down:
·
Subject-oriented: all relevant data
concerning a subject
·
Integrated: all data in the warehouse
must be compatible with each other regardless of type or location.
·
Time-variant: all data contains a
reference to time so that the age of each piece of data can be determined.
·
Non-volatile: the data does not change
once it has been collected.
