It was originally developed by the W3C to query data stored in the Resource Description Framework (RDF) format for metadata. In other words, SPARQL wasn’t devised for graph database searches ...

which work particularly well with structured data, graph databases are designed to model and store data as interconnected nodes and relationships. Graph databases focus on the relationships within ...

TigerGraph is a pure graph database, from the ground up. Its data store holds nodes, links, and their attributes, period. Some graph database products on the market are really wrappers built on ...

Storage advancements made the amassing of greater amounts ... of operational database technology from relational to semantic graphs. The smart graph database approach to data modeling typifies this ...

When Teradata bought Aster Data in 2011, it set out on a quest to bring (then) esoteric Big Data technology to the relational database faithful. Specifically, Aster's SQL-MapReduce facility ...

So, you could see the difference between a regular database and a graph database ... at storing transactional data; it has the SQL SMB that allows you to store NoSQL data inside a database.

to represent and store information. It’s a more versatile format that makes data easier to retrieve within a single operation in most cases. Perhaps the biggest advantage of graph databases is ...

The ability to track such data relationships is necessary for many analytics projects. TigerGraph provides a popular graph database of the same name. The platform can store graphs with up to ...

The combination of TinkerPop query engine, Gremlin query language, and Aerospike’s data management capabilities is a general-purpose property graph database that’s suitable ... For small deployments, ...