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The data extracted from diverse sources will have to be checked for integrity and will have to be cleaned and then loaded into warehouse for meaningful analysis. Therefore, harnessing efficient data cleaning and loading technologies (ETL—Extraction, Transformation and Loading) to warehousing system will be another objective of data warehouse. This process is known as Data Transformation service or Data preparation and staging.
The cleaned and stored data will have to be partitioned, summarized and stored for efficient query and analysis. Creating of subject oriented data marts, dimensional models of data and use of data mining technologies would follow, as next objective of data warehousing. This process is called Data Storage.
Finally tools necessary for query, analysis and reporting on data would have to be built into system to process to deliver a rich end user experience. This process is known as Data Presentation.
Users need to understand what rules applied while cleaning and transforming data before storage. This information needs to be stored separately in a relational database called Metadata.
Metadata is “data about data”. Mapping rules and maps between data sources and warehouse; Translation, transformation and cleaning rules; date and time stamps, system of origin, type of filtering, matching; Pre-calculated or derived fields and rules thereof are all stored in this database. In addition metadata database contains a description of data in data warehouse; navigation paths and rules for browsing data in data warehouse; data directory; list of pre-designed and built in queries available to users. For more visualization of this article along with screen shots and more visit http://www.exforsys.com/content/view/1295/332/
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