Introducing ArangoDB 3.8 – Graph Analytics at Scale

00General, Releases

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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Inside the Avocado Grove: From Canada to Germany and the Digital Marketing of Avocados

00GeneralTags:

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.

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Word Embeddings in ArangoDB

02General

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:

  • Discuss the background of word embeddings
  • Introduce the current state-of-the-art models for embedding text
  • Apply a model to produce embeddings of movie descriptions in an IMDb dataset
  • Perform similarity search in ArangoDB using these embeddings
  • Show you how to query the movie description embeddings in ArangoDB with custom search terms
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ArangoBnB: Data Preparation Case Study

00GeneralTags: , , , ,

This case study covers a data exploration and analysis scenario about modeling data when migrating to ArangoDB. The topics covered in this case study include:

  • Importing data into ArangoDB
  • Developing Application Requirements before modeling
  • Data Analysis and Exploration with AQL

This case study can hopefully be used as a guide as it shows step-by-step instructions and discusses the motivations in exploring and transforming data in preparation for a real-world application.
The information contained in this case study is derived from the development of the ArangoBnB project; a community project developed in JavaScript that is always open to new contributors. The project is an Airbnb clone with a Vue frontend and a React frontend being developed in parallel by the community. It is not necessary to download the project or be familiar with JavaScript for this guide. To see how we are using the data in a real-world project, check out the repository.

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C++ Memory Model: Migrating from X86 to ARM

02ARM, C++, General, x86Tags: , , , , ,

Over the past years, we have seen a shift in processors from the previously dominant x86 architecture to the more energy-efficient (and often cheaper) ARM architecture. This trend is true for both consumer hardware, e.g., Apple’s M1 SoC and also server hardware, e.g. AWS’s Gravitron processor. Given that the ARM architecture has a more relaxed memory model, this might have some subtle impact on C++ programs…

With the introduction of multi-threaded execution in C++11, the standard also introduced a memory model. For many developers, this memory model is one of the least well-understood parts of the C++ standard and a common source of confusion. While an in-depth explanation of the C++ memory model is beyond this blog post’s scope, we will cover some basics and take a brief look at two of the most common hardware memory models and how the C++ memory model maps to these hardware models.

But first, let’s take a step back and consider why we need a memory model in the first place.

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ArangoML Series: Multi-Model Collaboration

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

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:

  • The graph data model is superior to relational for ML meta-data storage.
  • Storing ML experiment objects is natural with multi-model.
  • ArangoML promotes collaboration due to the flexibility of multi-model.
  • ArangoML provides ops logging and performance analysis.
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning
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