Data Warehousing Overview

Data warehousing is the process of integrating enterprise-wide corporate data into a single repository. The resulting data warehouse may then support a variety of decision analysis functions as well as strategic operational functions.

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

According to Bill Inmon:

A Warehouse is a Subject-oriented, integrated, time-variant and non-Volatile collection of data in support of management's decision making process.

• Subject Oriented: Data that represents a particular subject Area like sales, Mktg etc instead of a company's ongoing operations.
• Integrated: Data that is collected from multiple source systems integrated into a user readable unique format. Ex: male, female, 0, 1, M, F.
• Non Volatile: The data stores historical, but the data is never removed.
• Time Variant: The data stores in time wise like weekly, monthly, quarterly, yearly.

The approaches in constructing a Datawarehouse and the DataMart:

1. Top-down approaches: In top-down approach to data warehouse design, in which the data warehouse is using a normalized enterprise data model. 
2. Bottom-up approaches: In the bottom-up approach data marts are first created to provide reporting and analytical capabilities for specific business processes.

In term of design data warehousing and data mart are almost same.

In general a Datawarehousing is used on enterprise level and DataMart is used on a business unit/department level.