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  2. Azure Data Lake - Wikipedia

    en.wikipedia.org/wiki/Azure_Data_Lake

    Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, [ 2 ] which is used to store and process data for applications such as Azure, AdCenter , Bing , MSN , Skype and Windows Live .

  3. Data lake - Wikipedia

    en.wikipedia.org/wiki/Data_lake

    Example of a database that can be used by a data lake (in this case structured data) A data lake is a system or repository of data stored in its natural/raw format, [1] usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., [2] and transformed data ...

  4. Drive letter assignment - Wikipedia

    en.wikipedia.org/wiki/Drive_letter_assignment

    In computer data storage, drive letter assignment is the process of assigning alphabetical identifiers to volumes. Unlike the concept of UNIX mount points, where volumes are named and located arbitrarily in a single hierarchical namespace, drive letter assignment allows multiple highest-level namespaces. Drive letter assignment is thus a ...

  5. Cosmos DB - Wikipedia

    en.wikipedia.org/wiki/Cosmos_DB

    Cosmos DB databases can be configured to be available in any of the Microsoft Azure regions (54 regions as of December 2018), letting application developers place their data closer to where their users are. [13] Each container's data gets transparently replicated across all configured regions.

  6. Namespace - Wikipedia

    en.wikipedia.org/wiki/Namespace

    In Python, namespaces are defined by the individual modules, and since modules can be contained in hierarchical packages, then namespaces are hierarchical too. [ 12 ] [ 13 ] In general when a module is imported then the names defined in the module are defined via that module's namespace, and are accessed in from the calling modules by using the ...

  7. Azure Data Explorer - Wikipedia

    en.wikipedia.org/wiki/Azure_Data_Explorer

    In Azure Data Explorer, unlike a typical relational database management systems (RDBMS), there are no constraints like key uniqueness, primary and foreign key. [26] The necessary relationships are established at the query time. [27] The data in Azure Data Explorer generally follows this pattern: [28] Creating Database, Ingesting data, Query the ...

  8. Extract, load, transform - Wikipedia

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

    Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format. This enables faster loading times.

  9. X.500 - Wikipedia

    en.wikipedia.org/wiki/X.500

    X.500 data models [ edit ] The primary concept of X.500 is that there is a single Directory Information Tree (DIT), a hierarchical organization of entries which are distributed across one or more servers, called Directory System Agents (DSA).