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  2. Data management - Wikipedia

    en.wikipedia.org/wiki/Data_management

    Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making. [ 1 ] Concept

  3. Centralized database - Wikipedia

    en.wikipedia.org/wiki/Centralized_database

    The traditional approach for storing data was to use a centralized database, and users would query the data from various points over a network. [ 1 ] An example for a centralized database could be given with the Australian Department of Defense , which centralized their databases in the mid 1970s.

  4. Evolutionary database design - Wikipedia

    en.wikipedia.org/wiki/Evolutionary_database_design

    Cultural impediments: Evolutionary database design approach is relatively a newer concept and many well qualified data professionals still advocate the traditional approach. Therefore, most of the databases are still being designed in a serial fashion and evolutionary database design is yet to gain support and traction among experienced data ...

  5. Relational model - Wikipedia

    en.wikipedia.org/wiki/Relational_model

    The relational model (RM) is an approach to managing data using a structure and language consistent with first-order predicate logic, first described in 1969 by English computer scientist Edgar F. Codd, [1] [2] where all data are represented in terms of tuples, grouped into relations.

  6. Data-centric computing - Wikipedia

    en.wikipedia.org/wiki/Data-centric_computing

    This approach functioned well for decades, but over the past decade, data growth, particularly unstructured data growth, put new pressures on organizations, information architectures and data center infrastructure. 90% of new data is unstructured and, according to a 2018 report, 59% of organizations manage over 10 billion files and objects [3 ...

  7. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design. This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality.

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