In Ralph Kimball’s book “The Data Warehouse Toolkit” he describes the basic elements of a data warehouse. There are essentially four main components: Operational Source Systems, Data Staging Area, Data Presentation Area, and Data Access Tools. Although Operational Source Systems is part of the model, Kimball states “The source systems should really be thought of as outside the warehouse”
Operational Source Systems
Operational Source Systems are those databases that hold all the transactional data. There is nearly no interaction with the Data Presentation Area and the Data Access Tools. The main priorities are processing, performance, and availability.
Data Staging Area
The Data Staging Area is temporary location where data from source systems is copied. A staging area is mainly required in a Data Warehousing Architecture for timing reasons. In short, all required data must be available before data can be integrated into the Data Warehouse.
Due to varying business cycles, data processing cycles, hardware and network resource limitations and geographical factors, it is not feasible to extract all the data from all Operational databases at exactly the same time. (Source: data-warehouses.net)
Data Presentation Area
The data presentation area is considered to be a set of integrated data marts. A data mart is a subset of the data warehouse and represents select data regarding a specific business function (Inmon, 1999). An organization can have multiple data marts, each one relevant to the department for which it was designed. For example, the English department may have a data mart reflecting historical student data including demographics, placement scores, academic performance, and class schedules. The data contained in the data presentation area must be detailed and logically organized. (Source: research.mtsac.edu)
Data Access Tools
Data Access Tools are really all the software that can query the data in the data warehouse’s presentation area. Data Access Tools are those like Cognos 10, SAP BeX, MicroStrategy, and Roambi. A data access tool can be as simple as an ad hoc query tool or as complex as a sophisticated data mining or modeling application.
Joshua is working to become a Data Scientist with focus on Analytics, Big Data, Machine Learning, and Statistics. His passion for Data and Information are second to none. He is a certified IBM Cognos Expert with more than 10 years experience in Business Intelligence & Data Warehousing, Analtyics, IT Management, Software Engineering and Supply Chain Performance Management with Fortune 500 companies. He has specializations in Analytics, Mobile Reporting, Performance Management, and Business Analysis.
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