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 ...

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 ...

As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

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.

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.

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.

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 ...