ArangoDB blog: latest news from the NoSQL multi-model database

September 2021: What’s the Latest with ArangoDB?

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Estimated reading time: 6 minutes

Hello Community,

Welcome to the ninth ArangoDB newsletter of 2021. 

Nine is fine and that’s how we are feeling about this month’s news, which includes:

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DevDayzLongDelay

ArangoDB Dev Days are Here!

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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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August 2021: What’s the Latest with ArangoDB?

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Estimated reading time: 6 minutes

Hello Community,

Welcome to the eighth ArangoDB newsletter of 2021. Woo hoo! Eight is great 🙌🏻 

In this edition, we are excited to share: 

Our growing avocado grove (aka we are hiring!)

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arangosync

ArangoSync: A Recipe for Reliability

00ArangoML, General, Graphs, how to, Machine LearningTags:

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 info
scatter plot graphsage

A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark

00ArangoML, General, Graphs, how to, Machine LearningTags: , , , , , ,

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
More info
detecting complex fraud patterns with ArangoDB

Detecting Complex Fraud Patterns with ArangoDB

00General, Graphs, how to, Query LanguageTags: , ,

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.
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