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ArangoML Series: Intro to NetworkX Adapter
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…
ArangoML Part 4: Detecting Covariate Shift in Datasets
This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more. In this post we:
ArangoML Part 3: Bootstrapping and Bias-Variance
This post is the third in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:
ArangoML Part 2: Streamlining Machine Learning Workflows
Estimated reading time: 1 minute 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
ArangoML Part 1: Where Graphs and Machine Learning Meet
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.
Fixing a Memory Leak in Go: Understanding time.After
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….
ArangoDB 3.7 – A Big Step Forward for Multi-Model
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.
ArangoDB Assembles 10,000 GitHub Stargazers
Estimated reading time: 3 minutes 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.
A Deep And Fuzzy Dive Into Search
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…
Do Graph Databases Scale? Yes? No? Let’s see!
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.
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…