• Introduce bootstrapping and bias-variance concepts
• Estimate and analyze the variance of the model from part 2
• Capture the metadata for this activity with arangopipe

Estimated reading time: 1 minute

This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

• Introduce machine learning concepts
• Demonstrate basic model building
• Log a model building activity with arangopipe

Estimated reading time: 4 minutes

This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases.

Estimated reading time: 7 minutes

When our founders realized that data models can be features, we at ArangoDB set ourselves the big goal of developing the most flexible database. With today’s GA release of ArangoDB 3.7, the project reached an important milestone on this journey.

Watch the the ArangoDB 3.7 Release Webinar.

Estimated reading time: 10 minutes

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see…

In the following, we will define what..

Estimated reading time: 2 minutes

Hi all!

I think this year has thrown us for quite a loop, full of unexpected occurrences, both joyous and difficult. With everything going on in the world at the moment, we feel it’s more important than ever to celebrate what you can .

So with that, just a quick note from us that our Kubernetes Operator has achieved Red Hat OpenShift Operator Certification. And as far as we can tell, this means we are the first graph database to reach full certification for Red Hat OpenShift. Huzzah!

Estimated reading time: 8 minutes

The ArangoDB Query Language(AQL) was designed to accomplish a few important goals, including:

When looking for a solution for your project, it is important to understand what makes each technology unique, what sets it apart. With ArangoDB that is its native multi-model approach including full graph database capabilities and I am going to explain the fundamental pieces of what that means.

At AWS Re:Invent just a few days ago, Andy Jassy, the CEO of AWS, unveiled their newest database product offerings: AWS Neptune. It’s a fully managed, graph database which is capable of storing RDF and property graphs. It allows developers access to data via SPARQL or java-based TinkerPop Gremlin. As versatile and as good as this may sound, one has to wonder if another graph database will solve a key problem in modern application development and give Amazon an edge over its competition.

Use-Case

The graph database use-case we are stepping through in this post is the following: In our web application we have several places where a user is led through a survey, where she decides on details for one of our products. Some of the options within the survey depend on previous decisions and some are independent.

Examples:

• Configure a new car
• Configure a new laptop
• Book extras with your flight (meal, reserve seat etc.)
• Configure a new complete kitchen
• Collect customer feedback via logic-jump surveys

We would like to easily offer a generic page which can be seeded with any..