Data warehouse Vs. Data warehousing Vs. Enterprise datawarehouse

Often while discussing technologies we use many terms as synonyms, in reality, many such terminologies have different meaning. This page briefs about three such terminologies that are commonly referred in the world of business intelligence:

Data warehouse (DW)
Data warehousing (DWH)
Enterprise Data warehouse (EDW)

Data Warehouse

As per Bill Inmon "A warehouse is a Historical, subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process".

By Historical we mean, the data is continuously collected from sources and loaded in the warehouse. The previously loaded data is not deleted for long period of time. This results in building historical data in the warehouse.
By Subject Oriented we mean data grouped into a particular business area instead of the business as a whole.
By Integrated we mean, collecting and merging data from various sources. These sources could be disparate in nature.
By Time-variant we mean that all data in the data warehouse is identified with a particular time period.
By Non-volatile we mean, data that is loaded in the warehouse is based on business transactions in the past, hence it is not expected to change over time.

Data Warehousing (DWH)

Data warehousing is the vast field that includes the processes and methodologies involved in the creating, populating, querying and maintaining a data warehouse. The objective of DWH is to help users make better business decisions.

Enterprise Data Warehouse (EDW)

Enterprise Data Warehouse (EDW) is a central normalized repository containing multiple subject area data for an organization. EDW are generally used for feeding data to subject area specific data marts. We must consider building an EDW when