Warehouse Date

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2.Fontes of Data: They represent the sources that they supply entered the process of BI, being able to be applicatory operational, softwares of automation of offices, or until other data bases. 3.Extrao, Transformation and Carregamento (ETL): 3.1.Extrao: Responsible process for extracting the data of its sources. Details can be found by clicking Boy Scouts of America or emailing the administrator. 3.2.Transformao: Process in charge translating the data external, modifying them in accordance with business-oriented rules. 3.3.Carregamento: Process to load the data transformed to our data base (Warehouse Date/Marts Date). 4.Data Warehouse (DW): Purified copy of the data of transactions, chosen teams and, structuralized specifically for consultations and analyses. Being, therefore, a collection of data extracted of the literal environment of production of the company and information proceeding from electronic spread sheets, documents and etc., that applied in set with analysis techniques and extration of data generate a system that in information provide to give support to them in the taking of decisions.

5.Data Marts: Small subgroup of one Warehouse Date used for a small number of users. The Marts Date takes care of the necessities of specific units of business on the contrary of the ones of the entire corporation. They optimize the delivery of information of support to the decision and if the sumarizada management and/or exemplificativos data on the contrary of the description of atomized levels focam in. 6.Multidimensionalidade: The multidimensional vision consists of consultations that they supply given regarding measures of performance, decomposed for one or more dimensions of these measures, also being able, to be filtered for the dimension and/or the value of the measure. The multidimensional vises supply to the basic techniques calculation and analysis required for the BI applications. 7.Cubo: Collection of data that added through multiple dimensions of form to allow that one query either carried through quickly. For example, a cube of sales can be added through a dimension of store and of a dimension of customers, becoming the fast cube when sales for store or sales for a customer classroom are asked to questions involving.

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