The data warehousing market consists of tools, technologies, and methodologies that allow for the construction, usage, management, and maintenance of the hardware and software used for a data warehouse, as well as the actual data itself.
In order to clear up some of the confusion that is rampant in the market, definitionplus provides you with fact:
The term Data Warehouse was coined by Bill Inmon in 1990, which he defined in the following way: “A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process”. He defined the terms in the sentence as follows:
Data that gives information about a particular subject instead of about a company’s ongoing operations.
Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.
All data in the data warehouse is identified with a particular time period.
Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.
(Source: “What is a Data Warehouse?” W.H. Inmon, Prism, Volume 1, Number 1, 1995).
This definition remains reasonably accurate almost ten years later. However, a single-subject data warehouse is typically referred to as a data mart, while data warehouses are generally enterprise in scope. Also, data warehouses can be volatile. Due to the large amount of storage required for a data warehouse, (multi-terabyte data warehouses are not uncommon), only a certain number of periods of history are kept in the warehouse.
Ralph Kimball provided a much simpler definition of a data warehouse. As stated in his book, “The Data Warehouse Toolkit”, on page 310, a data warehouse is “a copy of transaction data specifically structured for query and analysis”. This definition provides less insight and depth than Mr. Inmon’s, but is no less accurate.