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2. Template:Data warehouses - Wikipedia

   en.wikipedia.org/wiki/Template:Data_warehouses

   To change this template's initial visibility, the |state= parameter may be used: {{Data warehouses | state = collapsed}} will show the template collapsed, i.e. hidden apart from its title bar. {{Data warehouses | state = expanded}} will show the template expanded, i.e. fully visible.

3. Data warehouse - Wikipedia

   en.wikipedia.org/wiki/Data_warehouse

   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 ...

4. Data mart - Wikipedia

   en.wikipedia.org/wiki/Data_mart

   Data Warehouse and Data Mart overview, with Data Marts shown in the top right.. A data mart is a structure/access pattern specific to data warehouse environments. The data mart is a subset of the data warehouse that focuses on a specific business line, department, subject area, or team. [1]

5. Teradata - Wikipedia

   en.wikipedia.org/wiki/Teradata

   Teradata offers three primary services to its customers: cloud and hardware-based data warehousing, business analytics, and consulting services. [51] In September 2016, the company launched Teradata Everywhere, which allows users to submit queries against public and private databases.

6. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]

7. Dimension (data warehouse) - Wikipedia

   en.wikipedia.org/wiki/Dimension_(data_warehouse)

   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.