The NoSQL (‘not only SQL’) graph database is a technology for data management designed to handle very large sets of structured, semi-structured or unstructured data. It helps organizations access, integrate and analyze data from various sources, thus helping them with their big data and social media analytics.
The traditional approach to data management, the relational database, was developed in the 1970s to help enterprises store structured information. The relational database needs its schema (the definition how data is organized and how the relations are associated) to be defined before any new information is added.
Today, however, mobile, social and Internet of Things (IoT) data is everywhere, with unstructured real-time data piling up by the minute. Apart from handling a massive amount of data of all kind, the NoSQL graph database does not need its schema re-defined before adding new data.
This makes the graph database much more flexible, dynamic and lower-cost in integrating new data sources than relational databases.
Compared to the moderate data velocity from one or few locations of the relational databases, NoSQL graph databases are able to store, retrieve, integrate and analyze high-velocity data coming from many locations.
The semantic graph database is a type of NoSQL graph database that is capable of integrating heterogeneous data from many sources and making links between datasets.
The semantic graph database, also referred to as an RDF triplestore, focuses on the relationships between entities and is able to infer new knowledge out of existing information. It is a powerful tool to use in relationship-centered analytics and knowledge discovery.
In addition, the capability to handle massive datasets and the schema-less approach support the NoSQL semantic graph database usage in real-time big data analytics.
The semantic graph database stands out from the other types of graph databases with its ability to additionally support rich semantic data schema, the so-called ontologies.
The semantic NoSQL graph database gets the best of both worlds: on the one hand, data is flexible because it does not depend on the schema. On the other hand, ontologies give the semantic graph database the freedom and ability to build logical models any way organizations find it useful for their applications, without having to change the data.
Apart from rich semantic models, semantic graph databases use the globally developed W3C standards for representing data on the Web. The use of standard practices makes data integration, exchange and mapping to other datasets easier and lowers the risk of vendor lock-in while working with a graph database.
One of those standards is the Uniform Resource Identifier (URI), a kind of unique ID for all things linked so that we can distinguish between them or know that one thing from one dataset is the same as another in a different dataset. The use of URIs not only reduces costs in integrating data from disparate sources, it also makes data publishing and sharing easier with mapping to Linked (Open) Data.
Ontotext’s GraphDB is able to use inference, that is, to infer new links out of existing explicit statements in the RDF triplestore. Inference enriches the graph database by creating new knowledge and gives organizations the ability to see all their data highly interlinked. Thus, enterprises have more insights at hand to use in their decision-making processes.
Apart from representing proprietary enterprise data in a linked and meaningful way, the NoSQL graph database makes content management and personalization easier, due to its cost-effective way of integrating and combining huge sets of data.
The rise of IoT and social media on the one hand, and the growing use of big data analytics on the other hand, makes the NoSQL graph database a preferred choice for mastering huge sets of data, integrating heterogeneous data from varied sources, combining and analyzing highly interlinked data, and obtaining meaning and insights to support decisions. Ontotext’s graph database GraphDB adds a virtualization functionality on top and it becomes possible to also create a virtual graph by mapping the columns and rows of a table to entities in the graph. As a result, one can also retrieve information from external relational databases and have it play with the knowledge graph.
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