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ArangoSync: A Recipe for Reliability
Estimated reading time: 18 minutes A detailed journey into deploying a DC2DC replicated environment When we thought about all the things we wanted to share with our users there were obviously a lot of topics to choose from. Our Enterprise feature; ArangoSync was one of the topics that we have talked about frequently and we…
A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark
Estimated reading time: 15 minute This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:
Community Notebook Challenge
Calling all Community Members! 🥑 Today we are excited to announce our Community Notebook Challenge. What is our Notebook Challenge you ask? Well, this blog post is going to catch you up to speed and get you excited to participate and have the chance to win the grand prize: a pair of custom Apple Airpod…
Introducing ArangoDB 3.8 – Graph Analytics at Scale
Estimated reading time: 5 minutes We are proud to announce the GA release of ArangoDB 3.8! With this release, we improve many analytics use cases we have been seeing – both from our customers and open-source users – with the addition of new features such as AQL window operations, graph and Geo analytics, as well…
Entity Resolution in ArangoDB
This post will dive into the world of Entity Resolution in ArangoDB. This is a companion piece for our Lunch and Learn session, Graph & Beyond Lunch Break #15: Entity Resolution. In this article we will:
Inside the Avocado Grove: From Canada to Germany and the Digital Marketing of Avocados
My name is Laura, and I am responsible for digital marketing here at ArangoDB. In the following post, I will dive into my own experience working at ArangoDB and how I ended up from Northern Ontario, Canada to work in Germany at a native multi-model graph database company. Are you interested in learning more about…
Word Embeddings in ArangoDB
Estimated reading time: 12 minute This post will dive into the world of Natural Language Processing by using word embeddings to search movie descriptions in ArangoDB. In this post we: