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This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

• Introduce machine learning concepts
• Demonstrate basic model building
• Log a model building activity with arangopipe

Estimated reading time: 4 minutes

This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases.

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Recently, we decided to investigate why our application ARANGOSYNC for synchronizing two ArangoDB clusters across data centers used up a lot of memory – around 2GB in certain cases. The environment contained ~1500 shards with 5000 GOroutines. Thanks to tools like pprof (to profile CPU and memory usage) it was very easy to identify the issue. The GO profiler showed us that memory was allocated in the function `time.After()` and it accumulated up to nearly 1GB. The memory was not released so it was clear that we had a memory leak. We will explain how memory..

Estimated reading time: 7 minutes

When our founders realized that data models can be features, we at ArangoDB set ourselves the big goal of developing the most flexible database. With today’s GA release of ArangoDB 3.7, the project reached an important milestone on this journey.

Watch the the ArangoDB 3.7 Release Webinar.

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Today is a marvelous day for the ArangoDB project and the community behind it.

A couple of minutes ago, the 10,000th stargazer joined the project on GitHub, and we want to send a really big “Thank You!” to each and everyone of you for showing your support.

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Together with my team, I took a deep dive into the available fuzzy search approaches and algorithms for quite a while, in order to find a performant solution for the various projects ArangoSearch gets used for. 

Since the introduction of ArangoSearch back in 2018, many of our users have asked for fuzzy search support. We worked hard on getting this done, and the whole team is now excited to finally make fuzzy search available with the upcoming ArangoDB 3.7.

In the following, we will share our learnings and hope they are useful for you.

What is Fuzzy..

Estimated reading time: 10 minutes

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see…

In the following, we will define what..

Estimated reading time: 4 minutes

After many weeks of designing, implementing, and testing we are happy to announce that all the exciting features are now in place for ArangoDB 3.7. We are proud to share a 3.7 Beta 1 preview release, which provides an opportunity for you to try out all upcoming features before the final release. On the ArangoDB technical preview page you’ll find the Beta 1 packages for the Community and Enterprise Editions, so you can install, play and test locally. 

Furthermore, we have a number of interactive tutorials online, enabling you to explore these new features..

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Hi all!

I think this year has thrown us for quite a loop, full of unexpected occurrences, both joyous and difficult. With everything going on in the world at the moment, we feel it’s more important than ever to celebrate what you can .

So with that, just a quick note from us that our Kubernetes Operator has achieved Red Hat OpenShift Operator Certification. And as far as we can tell, this means we are the first graph database to reach full certification for Red Hat OpenShift. Huzzah!

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The ArangoDB Query Language(AQL) was designed to accomplish a few important goals, including: