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 ...

Exploring Graph Database Basics A graph database uses highly inter-linked data structures built from nodes, relationships, and properties. In turn, these graph structures support sophisticated, ...

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.

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.

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.

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 ...

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.

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 ...

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 ...

Graph database pioneer Neo4j announced the latest update of its namesake product this week, and the company isn't holding back on the superlatives. Neo4j 4.0 is "the most significant product release ...