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Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...
Example of a star schema; the central table is the fact table. In data warehousing, a fact table consists of the measurements, metrics or facts of a business process.It is located at the center of a star schema or a snowflake schema surrounded by dimension tables.
The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which ...
The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements.
The dimensional fact model (DFM) [1] is an ad hoc and graphical formalism specifically devised to support the conceptual modeling phase in a data warehouse project. DFM can be used by analysts and non-technical users as well.
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.
This category lists products, mostly software, that are used in data warehousing. It can also contain specialised producers of these products, including those producing open source products. Subcategories
The bus matrix purpose is one of high abstraction and visionary planning on the data warehouse architectural level. By dictating coherency in the development and implementation of an overall data warehouse the bus architecture approach enables an overall vision of the broader enterprise integration and consistency while at the same time dividing the problem into more manageable parts [2 ...
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