Typically Data Warehouses are considered to be non-volatile in comparison to traditional databasesdue to the fact that data within the warehouse does not change that often.  In addition, Data Warehouses typically represent data through the use of Multidimensional Conceptual Views that allow data to be extracted based on the view and the current position within the view.

Common Data Warehouse Traits

• Relatively Non-volatile Data
• Supports Data Extraction and Analysis
• Optimized for Data Retrieval and Analysis
• Multidimensional Views of Data
• Flexible Reporting
• Multi User Support
• Generic Dimensionality
• Transparent
• Accessible
• Unlimited Dimensions of Data
• Unlimited Aggregation levels of Data

Normally, Data Warehouses are much larger then there traditional database counterparts due to the fact that they store the basis data along with derived data via Multidimensional Conceptual Views. As companies store larger and larger amounts of data, they will need a way to effectively and accurately extract analysis information that can be used to aide in formulating current and future business decisions. This process can be done currently through data mining within a Data Warehouse.

Data Warehouses provide access to data derived through complex analysis, knowledge discovery and decision making. Secondly, they support the demands for high performance in regards to analyzing an organization’s existing and current data.

Data Warehouses provide support for an organization’s data and acquired business knowledge. 

Within a Data Warehouse multiple types of operations/sub systems are supported.

Common Data Warehouse Sub Systems

• Online Analytical Processing (OLAP)
• Decision –Support Systems (DSS)
• Online Transaction Processing (OLTP)