For example, graph databases excel in environments where relationships drive functionality, offering advantages in developing custom large language models (LLMs) and other advanced AI-driven ...

NEW PRODUCT ANALYSIS: Unlike other graph databases that delve two to three levels deep into the connected data, TigerGraph's pattern analytics is tuned to be efficient and tractable with the ...

For example, TigerGraph recently used these benchmarks to scale its database to support 30 terabytes (TB) of graph data, up from 1 TB in 2019 and 5 TB in 2020.

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.

All the data nodes in a graph database are connected. These are databases that use graph structures for semantic queries with nodes, edges, and properties to represent and store data.

Neo4j is a native graph database that was engineered from the inside out to support large graph structures, as in queries that return hundreds of thousands of relations and more.

A graph is a data structure that holds not only business records but also information about how those records are connected to one another. For example, it can point out if two purchase logs were ...

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.

A good example is the Microsoft Graph, which Microsoft characterizes as "the gateway to data and intelligence in Microsoft 365." ...

Graph platform Neo4j today announced that it raised $325 million at an over $2 billion valuation in a series F round led by Eurazeo, with additional investment from GV. The capital, which brings ...