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.
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.
Data Warehousing
A "cell" is the basic unit of data
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Virtual data providers allow programs developed at one site to be run against data at another site. It is virtual in that there is no centrally located store of data, thus site personnel retain complete control over their own local data.
There are three basic principles of data processing. These are ETL that is extraction, transformations and loading.
Flint solutions is one of the major institutes in Bangalore to teach data warehousing. Big data also now competes with Data warehousing.
what role should HIM professionals play in data warehousing development
Because businesses wanted to integrate their data, data warehousing was born. Dating back to the late 1980s, data warehousing is simply a single system that stores all of a company's data.
Data Warehousing
Interested in learning about Data Warehousing? Attend a virtual seminar on Data Warehousing given by our AI bot, Tom. http://www.keysoft.co.in/virtualcourse.aspx (note: you will need audio output)
Data warehousing software is used to catalog and record data for analysis and reporting. You can learn more about data warehousing from the Wikipedia. Once on the page, type "Data warehouse" into the search field at the top of the page and press enter to bring up the information.
show the various stages in data warehousing and business analytics
There are many companies that offer Business Intelligence and Data Warehousing services. Some examples of companies who offer Business Intelligence and Data Warehousing services includes Plasma Comp and iDashboards.
You can learn about data warehousing concepts through online courses on platforms like Coursera, Udemy, and LinkedIn Learning. Additionally, you can read books on data warehousing by authors such as Ralph Kimball and Inmon. Industry conferences and workshops may also provide insights into the latest trends and practices in data warehousing.
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Data Warehousing is the process of unifying data from multiple data sources under a single unified schema. A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.