Announcing ArangoDB Series B Funding
Estimated reading time: 5 minutes
This blog post is to announce ArangoDB’s Series B funding round and provide an update on the progress we have made so far. The company has achieved a lot in the past couple of years since we announced our Series A round of funding in 2019. We are excited for what lies ahead and can’t wait to bring you along for the journey!
ArangoDB Dev Days are Here!
Estimated reading time: 2 minutes
We’ve been spending quite a bit of time preparing something really exciting for our community this year: we’re thrilled to announce ArangoDB’s first-ever virtual developer conference taking place October 18th through the 22nd, 2021.
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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 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.
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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:
- 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
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 Pros.
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Detecting Complex Fraud Patterns with ArangoDB
Introduction
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.
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 as new ArangoSearch functionality.
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