The NoSQL graph database is a technology for data management designed to handle very large sets of structured, semi-structured or unstructured data. NoSQL (‘not only SQL’) graph databases serve organizations to access, integrate and analyze both unstructured data and data stored in the cloud, 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 IoT data is everywhere, with unstructured real-time data piling up by the minute. Apart from handling 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.
NoSQL graph databases are able to store, retrieve, integrate and analyze high-velocity data coming from many locations, compared to the moderate data velocity from one or few locations of the relational databases.
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 NoSQL graph database gets the best of both worlds: on the one hand, data is flexible because it does not depend on schema. On the other hand, ontologies give the semantic graph database the freedom and opportunity to build logical models the way organizations like it and 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 of 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 db.
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 those things, integrate them without confusion, or know that one thing from one dataset is the same as another in a different dataset because they have one and the same URI. 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.
The Ontotext 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. Content management, personalization and text mining for Publishers, Life Sciences and Healthcare benefit from the NoSQL approach to data management.
Semantic technology and NoSQL also help organizations with social media analytics, just take a look at this report on how Twitter users felt about Brexit a few weeks before the vote in the UK.
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.