Estimated reading time: 9 minutes
Estimated reading time: 4 minutes
We are proud to announce the GA release of ArangoDB 3.12!
Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.
Estimated reading time: 5 minutes
‘Guest Blog’
Estimated reading time: 5 minutes
In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ
The original problem in cloud security is visibility into my assets. If security engineers donʼt know what cloud services are running, they canʼt protect an environment. Unfortunately, first generation cloud security products were built with a list mindset, i.e. “rows and columnsˮ. They generate a list of assets and their configurations – but show no context of..
Estimated reading time: 2 minutes
Estimated reading time: 1 minute
ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.
Estimated reading time: 7 minutes
Estimated reading time: 7 minutes
At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.
Estimated reading time: 1 minutes
Estimated reading time: 20 minutes
In this blog post, we will build a complete movie recommendation application using ArangoDB and PyTorch Geometric. We will tackle the challenge of building a movie recommendation application by transforming it into the task of link prediction. Our goal is to predict missing links between a user and the movies they have not watched yet.
Run this notebook yourself: https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Integrate_ArangoDB_with_PyG.ipynb
Estimated reading time: 1 minutes
Estimated reading time: 15 minute
This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:
Estimated reading time: 2 minutes
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.
Our Interactive Tutorials repository has a library of python notebooks available covering the full spectrum of ArangoDB features. Although we cover a lot of topics, something is missing… YOUR notebook! As announced in our July Newsletter on July 29th and running until October 31st, the ..
Estimated reading time: 9 minutes
Estimated reading time: 8 minutes
Multi-Model Machine Learning
This article looks at how a team collaborating on a real-world machine learning project benefits from using a multi-model database for capturing ML meta-data.
The specific points discussed in this article are how:
Estimated reading time: 4 minutes
Estimated reading time: 3 minutes
This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX.
In this post we:
This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter repository. It is summarized..
Estimated reading time: 4 minutes
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..
Get the latest tutorials,
blog posts and news: