Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

Teradata Aster's take on this is that embedding a graph database store inside the product and allowing functions that perform graph data manipulation to be callable from SQL queries (much as SQL ...

TigerGraph stores all data sources in a single, unified multiple-graph store that can scale out and up easily and efficiently to explore, discover and predict relationships.

A semantic graph database technology vendor is supporting a key specification designed to validate graph-based data against a set of conditions that specify the “shape” of data. The goal is a more ...

Some graph database products on the market are really wrappers built on top of a more generic NoSQL data store. This virtual graph strategy has a double penalty when it comes to performance.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Graph databases have matured into mainstream information technology and delivered value to organizations in a wide range of applications. Here's how you can expect them to evolve.

Unlike relational databases, which work particularly well with structured data, graph databases are designed to model and store data as interconnected nodes and relationships.

The graph database landscape has been fragmented, with property graphs & RDF representing different ways to model, store, and query data, with no standard way of interoperability.