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ArangoDB ArangoGraph, the cloud service of ArangoDB, has been available for a few months now and is growing quickly. The ArangoGraph team got a lot of requests to provide more ways to manage deployments, access policies and other aspects of ArangoGraph.

After adding support for Azure earlier this year, we’re now opening up the ArangoGraph API for all supported cloud providers like Google Cloud and AWS.

Estimated reading time: 4 minutes

TL;DR

ArangoDB has supported two storage engines for a while: RocksDB and MMFiles. While ArangoDB started out with just the MMFiles storage engine in its early days, RocksDB became the default storage engine in the 3.4 release. Due to its drawbacks ArangoDB 3.6 deprecated the old MMFiles storage engine and with the upcoming 3.7 release we plan to fully remove support. This blog post will provide the background of why storage engines matter, why we chose to deprecate the MMFiles storage engine, and what you should be aware of when migrating from MMFiles to..

Estimated reading time: 10 minutes

This is a short tutorial to get started with ArangoDB using Node.js. In less than 10 minutes you can learn how to use ArangoDB with Node. This tutorial uses a free ArangoDB Sandbox running on ArangoGraph that requires no sign up.

Estimated reading time: 5 minutes

Please consider monitoring your productive ArangoDB installation as part of the best practices strategy. It is effortlessly done using established services Prometheus for data collection and Grafana for visualisation and alerting.

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Kube-ArangoDB, ArangoDB’s Kubernetes Operator first released two years ago and as of today is operating many ArangoDB production clusters (including ArangoDB’s Managed Service ArangoGraph). With many exciting features we felt kube-arango really deserves to be released as 1.0.

Estimated reading time: 3 minutes

Today we are excited to invite everybody to take the first public preview of Azure on ArangoDB ArangoGraph for a test ride. In case you haven’t joined ArangoGraph yet, please find more details about our offering and a 14-day free trial on cloud.arangodb.com. Just choose Microsoft Azure as your cloud provider and choose from the many regions we already support.

You can share all feedback with us about regions you’d love to see added or other improvements on slack. Please use the #ArangoGraph channel on Community Slack or raise an issue via the “Request..

Estimated reading time: 6 minutes

We released ArangoDB version 3.6 in January this year, and now we are already 6 weeks into the development of its follow-up version, ArangoDB 3.7. We feel that this is a good point in time to share some of the new features of that upcoming release with you!

We try not to develop new features in a vacuum, but want to solve real-world problems for our end users. To get an idea of how useful the new features are, we would like to make alpha releases available to everyone as soon as possible. Our goal is get early user feedback during the development of..

Estimated reading time: 3 minutes

Neo4j, Inc. is the well-known vendor of the Neo4j Graph Database, which solely supports the property graph model with graphs of previously limited size (single server, replicated).

In early 2020, Neo4j finally released its 4.0 version which promises “unlimited scalability” by the new feature Neo4j Fabric. While the marketing claim of “scalability” is true seen from a very simplistic perspective, developers and their teams should keep a few things in mind – most importantly: True horizontal scalability with graph data is not achieved by just allowing..

Estimated reading time: 4 minutes

We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important.

Consider for example:

• Capture of Lineage Information (e.g., Which dataset influences which Model?)
• Capture of Audit Information (e.g, A given model was trained two months ago with the following training/validation performance)
• Reproducible Model Training
• Model Serving Policy (e.g., Which model should be deployed in production based on training statistics)