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  2. Cosmos DB - Wikipedia

    en.wikipedia.org/wiki/Cosmos_DB

    Changes are persisted by Cosmos DB, which makes it possible to request changes from any point in time since the creation of the container. A "Time to Live" (or TTL) can be specified at the container level to let Cosmos DB automatically delete items after a certain amount of time expressed in seconds. This countdown starts after the last update ...

  3. Gremlin (query language) - Wikipedia

    en.wikipedia.org/wiki/Gremlin_(query_language)

    The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. [4] The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below.

  4. 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. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. [2]

  5. Azure Cognitive Search - Wikipedia

    en.wikipedia.org/wiki/Azure_Cognitive_Search

    A search string can be specified as one of the query parameters to retrieve matching documents. Azure Search supports search strings using simple query syntax. [6] Supported features include logical operators, the suffix operator, and query with Lucene query syntax. [7] (currently in preview) As an example, white+house

  6. Connection pool - Wikipedia

    en.wikipedia.org/wiki/Connection_pool

    In Azure Cosmos DB, connection pooling is managed at the SDK level rather than by the database service itself. SDKs such as those for .NET, Java, and Python implement connection pooling to reuse HTTP connections to the database endpoint, optimizing resource usage and performance.

  7. PACELC theorem - Wikipedia

    en.wikipedia.org/wiki/PACELC_theorem

    The tradeoff between availability, consistency and latency, as described by the PACELC theorem. In database theory, the PACELC theorem is an extension to the CAP theorem.It states that in case of network partitioning (P) in a distributed computer system, one has to choose between availability (A) and consistency (C) (as per the CAP theorem), but else (E), even when the system is running ...

  8. Bitemporal modeling - Wikipedia

    en.wikipedia.org/wiki/Bitemporal_Modeling

    Bitemporal modeling is a specific case of temporal database information modeling technique designed to handle historical data along two different timelines. [1] This makes it possible to rewind the information to "as it actually was" in combination with "as it was recorded" at some point in time.

  9. Conjunctive query - Wikipedia

    en.wikipedia.org/wiki/Conjunctive_Query

    Given two queries and and a database schema, the query containment problem is the problem of deciding whether for all possible database instances over the input database schema, () (). The main application of query containment is in query optimization: Deciding whether two queries are equivalent is possible by simply checking mutual containment.