A Data Warehouse is a subject-oriented data repository designed with enterprise-wide access in mind. In addition to data query and reporting, a data warehouse provides facility for getting quick, accurate, and often insightful information. A Data Warehouse is designed so that its users can recognize the information they want and access that information using simple tools.A Data Warehouse integrates operational data from various sources into a single and consistent architecture that supports analysis and decision-making within the organization. Operational (legacy) systems create, update and delete production data that "feed" the Data Warehouse.

The key benefits of using Data Warehouse are as follows:

• More cost-effective decision making.
• Better enterprise intelligence.
• Enhanced customer service.
• Allowing business reengineering.
• Information system reengineering.

A data warehouse comprises a computing system used to store information regarding an organization's activities in a database. It is typically a blending of technologies, including relational and multidimensional databases, client/server architecture, extraction/transformation programs, graphical user interfaces, and more.

Data warehouses may hold large amounts of information, sometimes in smaller logical units called Data marts. Often the schemas of data marts are stored in what are known as "star schemas", or dimensional modeling form; however there is no industry standard requiring that the schemas of data marts be in any particular form. Data warehouses are usually accessed (queried) via "data marts", which are purpose-specific access points to or sub-sets of the warehouse. Data marts are designed to answer the probable queries of a given kind of user.

Conventional database systems use highly normalized data formats to ensure consistency of data and minimal use of space. However this often means that transactions and queries against a fully normalized database perform slowly. Data warehouses often use a more de-normalized (relaxed) format. This speeds up queries, and the schema will be more intuitive to non-administrative users as they are exploring it. For example, rather than having a single record in a table contain customer information, that information may be replicated across a whole series of tables.

Computing in data warehouses is often referred to as Online Analytical Processing (OLAP), in contrast to Online Transaction Processing (OLTP). Data from Enterprise resource planning (ERP) systems and other related business software systems is imported into data warehouses periodically for further processing or Data Mining.

Database Warehouse

Related Terms: ERP, OLAP, OLTP, Relational Database, Data Mining, Dimensional Database