Estimated reading time: 4 minutes
Welcome to the fourth ArangoDB newsletter of 2021!
In this edition, we share details about: our latest and greatest lunch breaks, part four of our ArangoML blog series, as well as a guest article featured in DZone about the C++ memory model.
We hope you enjoy!
Graph & Beyond Lunch Break #9: ArangoML
In this upcoming Graph & Beyond Lunch Break, our Head of Engineering and Machine Learning Jörg Schad will give an overview of different parts of the ML pipeline and how ArangoDB fits in. In particular, he will touch upon feature engineering, Graph ML, Embeddings, MLOps, and Metadata.
Want to have the video delivered to your inbox during your ‘lunch hour’ on Wednesday, May 5th? Register here.
ArangoDB 3.8 Beta (and ArangoDB 3.6 EOL)
We’ve been working hard on ArangoDB 3.8, and are excited to share the Beta release is here! To check out the latest features, such as Weighted Traversals, k Paths, and ArangoSearch Pipeline Analyzer, download the technical preview here.
Also, per our EOL policy, support for ArangoDB 3.6 will end as of August 31, 2021 (ArangoDB 3.7 was released for General Availability on August 27, 2020). Current ArangoDB 3.6 users are encouraged to upgrade to ArangoDB 3.7.
AQL Query Performance Optimization 101 (Part II)
Last month, we shared part one of this two-part series about how to optimize your AQL queries. Check out part two, where ArangoDB Senior Developer Jan Steemann covers some factors that contribute to cluster AQL query performance, namely sharding and replication factor.
He also explains some features of the Enterprise Edition for co-locating data in a way that minimizes cluster-internal roundtrips and improves performance.
ArangoML Blog Series: Detecting Covariate Shift in Datasets
This post is the fourth in a series about machine learning and the benefits ArangoML can bring to your machine learning pipelines. In this post, we:
- Introduce the concept of covariate shift in datasets
- Showcase ArangoML’s built-in dataset shift detection API
In the News:
DZone: C++ Memory Model – Migrating From X86 to ARM
In this article published by DZone, ArangoDB Senior Software Engineer Manuel Pöter shares his knowledge of the C++ memory model – one of the least well-understood parts of the C++ standard, yet it is indispensable when writing high-performant code using atomic operations.
Where else can you find ArangoDB?
- May 7: O’Reilly Live Training – Graph-Powered Machine Learning First Steps
- May 19: Graph & Beyond Lunch Break #10 – Oasisctl: Providing Full Control of your Oasis Cluster
- June 9: AI@Enterprise Summit – Production-grade ML Pipelines – From Data To Metadata
- June 11: AI@Enterprise Summit – Building and Operating an Open Source Data Science Platform
Wanted: Community Pioneers
We are continuing our Community Pioneer program, the goal of which is to give back to our vast open-source community around the world by spreading the word about your amazing contributions.
Interested in sharing something cool you’re building with ArangoDB? We’d love, love, love to hear from you. Get in touch at community [at] arangodb [dot] com.
ArangoDB recognized as Leader on G2
We couldn’t be more thrilled to be recognized by our users as a Graph Database Leader on G2 Crowd.
Are you happy with how things are going with ArangoDB so far, and have a few minutes to spare? Then take a break from coding and write a review about your experience on G2.
We hope you enjoyed our latest news!
Until next time 🖖
The ArangoDB Team
Check Out Our Previous Newsletters
March 2021: What’s the Latest with ArangoDB?