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  2. Microsoft Power BI - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Power_BI

    The first release of Power BI was based on the Microsoft Excel-based add-ins: Power Query, Power Pivot and Power View. With time, Microsoft also added many additional features like question and answers, enterprise-level data connectivity, and security options via Power BI Gateways. [10] Power BI was first released to the general public on 24 ...

  3. Power Query - Wikipedia

    en.wikipedia.org/wiki/Power_Query

    Power Query is built on what was then [when?] a new query language called M.It is a mashup language (hence the letter M) designed to create queries that mix together data. It is similar to the F# programming language, and according to Microsoft it is a "mostly pure, higher-order, dynamically typed, partially lazy, functional language."

  4. Value-stream mapping - Wikipedia

    en.wikipedia.org/wiki/Value-stream_mapping

    Value-stream mapping, also known as material- and information-flow mapping, [1] is a lean [2]-management method for analyzing the current state and designing a future state for the series of events that take a product or service from the beginning of the specific process until it reaches the customer.

  5. Ralph Kimball - Wikipedia

    en.wikipedia.org/wiki/Ralph_Kimball

    Ralph Kimball (born July 18, 1944 [1]) is an author on the subject of data warehousing and business intelligence.He is one of the original architects of data warehousing and is known for long-term convictions that data warehouses must be designed to be understandable and fast.

  6. Value-stream-mapping software - Wikipedia

    en.wikipedia.org/wiki/Value-stream-mapping_software

    Value-stream-mapping software is a type of software that helps prepare and/or analyze value stream maps. The software typically helps design maps through utilizing a series of symbols representing activity and information/material flow, and as a supplement to manual calculations [ 1 ]

  7. Data mapper pattern - Wikipedia

    en.wikipedia.org/wiki/Data_mapper_pattern

    The goal of the pattern is to keep the in-memory representation and the persistent data store independent of each other and the data mapper itself. This is useful when one needs to model and enforce strict business processes on the data in the domain layer that do not map neatly to the persistent data store. [2]

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