The property graph database space has been dominated by a handful of names who on balance are not that big in the software marketplace generally speaking.

The graph database space is rapidly expanding as more and more companies identify potential use cases that require the traversal of highly connected data sets.

Speed up your JanusGraph queries with these tuning tips and the new CQL storage adapter.

Find dissatisfied customer cohorts and evaluate intervention measures using graph machine learning.

A practical guide on some of the main players in the graph database marketplace.

How to detect both credit card fraud and money laundering using graph and machine learning.

Leveraging the natural structure of data, graph convolutional networks produce optimal predictions of node properties.

Explore your tuning options for increasing JanusGraph write throughput and lowering latencies.

FBI’s use of machine learning based entity resolution to catch criminals and fraudulent activity.

Messages coming into your Spark stream processor may not arrive in the order you expect. Learn how to handle the unexpected.

Support for database schemas is at its nascent stages in the evolving property graph space.

Learn how to connect to connect your next Dropwizard app to Microsoft’s new Cosmos DB graph layer.

Can an AI learn to write better looking websites than a human? Frighteningly, yes.

Graphs are rich data structures that can be used to improve the accuracy and effectiveness of machine learning workflows.

Learn how Graph Technology can help to identify risk and fraud patterns in order to quickly respond.

Dave and Ted discuss Graphday and their thoughts on DSE, Neo4j, Microsoft's CosmosDB, and JanusGraph.

I'll discuss the process of building a micro service that is backed by a graph database and the technologies leveraged to accomplish it.

We get asked that question a lot given our early customer work with Titan evaluations, participation in the JanusGraph project and usage of Apache TinkerPop.

DataStax released 5.1, Neo4j released 3.2, Microsoft announces CosmosDB; there’s a lot of stuff happening in the graph database world. Looks like a prime time for some Gremlin training.

When it comes to data-driven user interfaces, like those utilizing graph data, it’s vital to understand your users and what they’re trying to achieve.

A supernode is a vertex with a large number of edges. In graph theory, these high-degree vertices are known as hubs.

OrientDB is one of several popular graph data stores on the market today. It provides a multi-model approach with the powerful nature of a graph database and the flexibility of a document data store. If you have decided to build out your multi-tenant application on top of OrientDB, you are in luck as it has several built-in, out-of-the-box methods for handling multi-tenancy.

How do you handle customer #2? You delivered an MVP of some hosted software for customer #1. Your brother-in-law knows a guy who has a similar problem and after a lunch meeting, now you need to add customer #2 to your incubating SaaS tool. Of course customer #1 and customer #2 shouldn’t be able to see each other’s data, but you don’t necessarily want to install and configure everything all over again just because you added another customer.