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Data Warehouse - An Introduction


A data warehouse is defined as a subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management's decisions. More generally, data warehousing is a collection of decision support technologies, aimed at enabling the knowledge worker, such as executive, manager, and analyst, to arrive at better and faster decisions. Data warehouses provide access to data for complex analysis, knowledge discovery, and decision-making. style="mso-spacerun: They support high performance demands on an organization's data and information. It provides an enormous amount of historical and static data from three tiers:
1.      Relational databases
2.      Multidimensional OLAP applications
3.      Client analysis tools
Several types of applications such as online analytical processing (OLAP), decision-support systems (DSS) and data mining are being supported. OLAP is a term used to describe the analysis of complex data from the data warehouse.
OLAP is a software technology that allows users to easily and quickly analyze and view data from multiple points-of-view. OLAP provides dynamic and multi-dimensional support to executives and managers who need to understand different aspects of the data. Activities that are supported include:
§  Analyzing financial trends
§  Creating slices of data
§  Finding new relationships among the data
§  Drilling down into sales statistics
§  Doing calculations through different dimensions where each category of data (that is, product, location, sales numbers, time period, etc.) is considered a dimension.
There are OLAP tools that use distributed computing capabilities for analyses that require more storage and processing power than can be economically and efficiently located on an individual desktop.
DSS support an organization's leading decision makers with higher-level data for complex and critical decisions. A DSS queries a data warehouse or an OLAP database for relevant information that can be compared in order to make a business decision and predict the impact of that decision.
Finally, data mining is being used for knowledge discovery, the process of searching data for unanticipated new knowledge.
Knowledge workers and decision makers use tools ranging from parametric queries to ad hoc queries to data mining. Thus, the access component of the data warehouse must provide support of structured queries (both parametric and ad hoc). These together make up a managed query environment.

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