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Data warehouse
A data warehouse is, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to for efficient data analysis and reporting (especially OLAP). Data warehousing is not meant for current "live" data.
Data warehouses often hold large amounts of information which are sometimes subdivided into smaller logical units called dependent data marts.
Usually, two basic ideas guide the creation of a data warehouse: Integration of data from distributed and differently structured databases, which facilitates a global overview and prehensive analysis in the data warehouse. Separation of data used in daily operations from data used in the data warehouse for purposes of reporting, decision support, analysis and controlling.
Periodically, one imports data from enterprise resource planning (ERP) systems and other related business software systems into the data warehouse for further processing. It is mon practice to "s
tage" data prior to merging it into a data warehouse. In this sense, to "stage data" means to queue it for preprocessing, usually with an ETL tool. The preprocessing program reads the staged data (often a business's primary OLTP databases), performs qualitative preprocessing or filtering (including denormalization, if deemed necessary), and writes it into the warehouse.
Business Intelligence reports (e.g., MIS reports) may then be generated from the data managed by the warehouse. In this way the data warehouse supplies the data for and supports the business intelligence tools that an anization might use.
Dimensions and Measures
A data warehouse is created by analyzing ways to categorize data using dimension (data warehouse)s and ways to summarize data using measure (data warehouse)s. Dimensions can be used to filter data by excluding results or by displaying data in different cells of a presentation. Measures are used to create erages and totals using preputed aggregates.
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