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 have also seen that our customers are keen to implement this in their environments. Mostly because of the secure requirements of having an ArangoDB cluster and all of its data located in multiple locations in case of a severe outage.
This blog post will help you set up and run an ArangoDB DC2DC environment and will guide you through all the necessary steps. By following the steps described you’ll be sure to end up with a production grade deployment of two ArangoDB clusters communicating with each other with datacenter to datacenter replication.(more…)
Estimated reading time: 15 minute
This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:
- What is GraphSage
- Neighbourhood Sampling
- Getting Hands-on Experience with GraphSage and PyTorch Geometric Library
- Open-Graph-Benchmark’s Amazon Product Recommendation Dataset
- Creating and Saving a model
- Generating Graph Embeddings Visualizations and Observations
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 Pros.(more…)
This article presents a case study of using AQL queries for detecting complex money laundering and financial crime patterns. While there have been multiple publications about the advantages of graph databases for fraud detection use cases, few of them provide concrete examples of implementing detection of complex fraud patterns that would work in real-world scenarios.
This case study is based on a third-party transaction data generator, which is designed to simulate realistic transaction graphs of any size. The generator disguises complex financial fraud patterns of two kinds:
- Circular money flows: a big amount of money is going through different nodes and comes back to the source node.
- Indirect money transfers: a big amount of money is sent from source node to a target node over a multi-layered network of intermediate accounts.
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 as new ArangoSearch functionality.(more…)
Estimated reading time: 8 minutes
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:
- give a brief background in Entity Resolution (ER)
- discuss some use-cases for ER
- discuss some techniques for performing ER in ArangoDB
Estimated reading time: 8 minutes
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 working abroad, working remotely, or diving into a new industry? This post covers all of the above topics.(more…)
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