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ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
Graph databases are commonly referred to as a NoSQL database. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction [3] and lack easy traversal over a chain of edges. [4] The underlying storage mechanism of graph databases can vary.
NoSQL (originally referring to "non-SQL" or "non-relational") [1] is an approach to database design that focuses on providing a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Instead of the typical tabular structure of a relational database, NoSQL databases ...
Ontotext GraphDB (previously known as BigOWLIM) is a graph-based database [6] capable of working with knowledge graphs [7] produced by Ontotext, compliant with the RDF graph data model [8] and the SPARQL query language. [9] Some categorize it as a NoSQL database, meaning that it does not use tables like some other databases. [10]
Firstly, the graph model can be a natural fit for data sets that have hierarchical, complex, or even arbitrary structures. Such structures can be easily encoded into the graph model as edges. This can be more convenient than the relational model, which requires the normalization of the data set into a set of tables with fixed row types.
ArangoDB is a graph database system developed by ArangoDB Inc. ArangoDB is a multi-model database system since it supports three data models (graphs, JSON documents, key/value) [1] with one database core and a unified query language AQL (ArangoDB Query Language).
TerminusDB is an in-memory graph database management system with a rich query language. The design of the underlying data structure , which is implemented in a Rust library , uses a succinct data structures and delta encoding approach drawing inspiration from software source control systems like Git . [ 21 ]
Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]