Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and ...

Graph databases have been around in one form or another since the early oughts, but they were generally slower, more complex to work with, and more limited in terms of their applicability than ...

Graph databases are general-purpose data technology. They can be used by a wide variety of domains, from healthcare to finance, and energy to disaster response. The key to understanding when to use a ...

This is where graph databases and NoSQL come into play. Unlike relational databases, which work particularly well with structured data, graph databases are designed to model and store data as ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

Graph querying of data housed in massive data lakes and data warehouses has been part of the big data and analytics scene for many years, but it hasn’t always been a particularly easy process.

One, graph is here to stay. Two, there's still some way to go to make the benefits of graph databases and analytics widely available and accessible.

For example, multi-hop queries can now be executed up to 1000x faster than Neo4j 4. These improvements are above and beyond the already exponentially faster Neo4j’s graph results over traditional ...

SAN MATEO, Calif. – November 9, 2022 – Neo4j, a graph technology company, announced today the general availability of Neo4j 5, the cloud-ready graph database. The company said Neo4j 5 widens the ...

A graph database can help you discover connections in your data you never imagined; here’s how to get started Alaa Mahmoud is an advisory software engineer and master inventor at IBM Analytics ...

As graph database adoption accelerates, new data infrastructures will emerge to eliminate many of the scale struggles of graph database models. Written by eWEEK content and product recommendations ...

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.