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Added ArcadeDB (OrientDB fork and successor) as Multi-Model DBMS |
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For some time, it was all but forgotten (or considered irrelevant) that there were any other database models besides Relational. The Relational model and notion of [[third normal form]] were the default standard for all data storage. However, prior to the dominance of Relational data modeling, from about 1980 to 2005, the [[hierarchical database model]] was commonly used. Since 2000 or 2010, many [[NoSQL]] models that are non-relational, including documents, triples, key–value stores and graphs are popular. Arguably, geospatial data, temporal data, and text data are also separate models, though indexed, queryable text data is generally termed a "[[search engine]]" rather than a database.{{Citation needed|date=March 2021}}
The first time the word "multi-model" has been associated to the databases was on May 30, 2012 in Cologne, Germany, during the [[Luca Garulli]]'s key note "''NoSQL Adoption – What’s the Next Step?''".<ref>{{Cite web|date=2012-06-01|title=Multi-Model storage 1/2 one product|url=http://www.slideshare.net/lvca/no-sql-matters2012keynote/47-MultiModel_storage_12_one_product}}</ref><ref>{{Cite web|url=https://2012.nosql-matters.org/cgn/wp-content/uploads/2012/06/KeyNote-Luca-Garulli.pdf|title=Nosql Matters Conference 2012 {{!}} NoSQL Matters CGN 2012|website=2012.nosql-matters.org|access-date=2017-01-12}}</ref> Luca Garulli envisioned the evolution of the 1st generation NoSQL products into new products with more features able to be used by multiple use cases.
The idea of multi-model databases can be traced back to [[object–relational database|Object–Relational Data Management Systems (ORDBMS)]] in the early 1990s and in a more broader scope even to federated and integrated DBMSs in the early 1980s. An ORDBMS system manages different types of data such as relational, object, text and spatial by plugging ___domain specific data types, functions and index implementations into the DBMS kernels. A Multi-model database is most directly a response to the "[[polyglot persistence]]" approach of knitting together multiple database products, each handing a different model, to achieve a multi-model capability as described by Martin Fowler.<ref name="polyglot">[http://martinfowler.com/bliki/PolyglotPersistence.html Polyglot Persistence]</ref> This strategy has two major disadvantages: it leads to a significant increase in operational complexity, and there is no support for maintaining data consistency across the separate data stores, so multi-model databases have begun to fill in this gap.
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* [[AllegroGraph]] – document (JSON, JSON-LD), graph
* [[ArangoDB]] – document (JSON), graph, key–value
* [[ArcadeDB]] – document (JSON), graph, key–value, time-series, [[SQL]], [[Cypher (query language)|Cypher query language]], [[Gremlin (query language)]]
* [[Cosmos DB]] – document (JSON), graph,<ref>{{Cite web|url=https://docs.microsoft.com/en-us/azure/cosmos-db/create-graph-dotnet|title = Build an Azure Cosmos DB .NET Framework, Core application using the Gremlin API}}</ref> key–value, SQL
* [[Couchbase]] – document (JSON), key–value, [[N1QL]]
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