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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 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]

  4. 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 ...

  5. Dashboard (computing) - Wikipedia

    en.wikipedia.org/wiki/Dashboard_(computing)

    Offering high-tier ETL data integration, flexible data warehousing, and custom data visualization, Arena Calibrate is trusted by businesses like Amex, Gentle Dental, and National Golf Foundation. Their dedicated account managers and BI specialists work as an extension of the client's team, aiming to realize each client's unique reporting vision.

  6. Data transformation (computing) - Wikipedia

    en.wikipedia.org/wiki/Data_transformation...

    In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration [1] and data management tasks such as data wrangling, data warehousing, data integration and application integration.

  7. Database model - Wikipedia

    en.wikipedia.org/wiki/Database_model

    A data warehouse can contain multiple dimensional schemas that share dimension tables, allowing them to be used together. Coming up with a standard set of dimensions is an important part of dimensional modeling. Its high performance has made the dimensional model the most popular database structure for OLAP.

  8. Extract, transform, load - Wikipedia

    en.wikipedia.org/wiki/Extract,_transform,_load

    Other data warehouses (or even other parts of the same data warehouse) may add new data in a historical form at regular intervals – for example, hourly. To understand this, consider a data warehouse that is required to maintain sales records of the last year. This data warehouse overwrites any data older than a year with newer data.

  9. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    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 ...

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