RDF is a graph data model you've probably either never heard of, or already dismissed. Why is that, could there be value in it, and how does it differ from the most popular graph data model out there?

DataStax Enterprise just released version 6.0, a major upgrade. We take an insider tour with Jonathan Lacefield, Senior Director of Product Management

It's official: AWS has a production-ready graph database. What features are included today, and what will be included in the near future, what use cases are targeted, and what does AWS Neptune's release mean for users and graph database vendors?

Having an entry path, as well as a strong community, is important for any solution, and graph databases are no different. Following latest developments in

GraphQL is a query language mostly used to streamline access to REST APIs. Now, a new breed of GraphQL implementations wants to build an abstraction layer for any database on top of GraphQL, and it seems to be catching up.

What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.

AWS, Google, Neo4j, Oracle. These were just some of the vendors represented in the W3C workshop on web standardization for graph data, and what transcribed is bound to boost adoption of the hottest segment in data management: Graph.

Fifty years after the moon landing, it's not just NASA working on what many consider the final frontier for humanity: space travel. NASA, however, is special, and one of the reasons is that data is at the heart of what it does.

Imagine a graph database that's not aimed at the growing graph database market, selling to Fortune 500 without sales, and claiming to be the fastest without benchmarks. Dgraph is unique in some interesting ways.

Graph databases have been the hottest area of data management for a while now. Today, this landscape shifts once again in the last few months.

Redis becomes available on Google Cloud Platform today, and we take the opportunity to explore its graph-shaped future, its community, and its licensing issues.

Neo4j just launched its cloud offering, which it says will make graph mainstream. We discuss the offering with CEO Emil Eifrem, and take a look at where the graph market is right now.

Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages

In its new release, Neo4j addresses key concerns for enterprise adoption. Scalability, security, management and architectural changes are here. And so is a strange feeling of deja-vu, too.

Actions and words, code and advocacy. DataStax is changing strategy, re-engaging with the Apache Cassandra open source community, and releasing some interesting technical advancements while at it, too.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

A hitherto under the radar graph database that uses blockchain to support data lineage and verification wants to take over the world, starting with the US Department of Defense

GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.

Session presented at Big Data Spain 2018 Conference14th Nov 2018Kinépolis Madrid

Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.

New players, new features, platforms, and ticking boxes. Let's talk graph with Neo4j.

What are the options for querying graphs, and how do we go from that to the equivalent of a virtual machine for databases?

If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.

Graph databases are crossing the chasm to mainstream use cases, adding features such as machine learning to their arsenal and becoming more cloud and developer friendly. Last year was a breakout year, and graph database growth and evolution is well under way in 2019.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo; the former is not. But both are real signs that the graph market and Neo4j are getting huge.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.