ArangoDB and the Cloud Native Ecosystem

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ArangoDB is joining CNCF to continue its focus on providing a scalable native multi-model database, supporting Graph, Document, and Key-Value data models in the Cloud Native ecosystem.

ArangoDB

ArangoDB is a scalable multi-model model database. What does that mean?

You might have already encountered different NoSQL databases specialized for different data models e.g., graph or document databases. However most real-life use-cases actually require a combination of different data models like Single View of Everything, Machine Learning or even Case Management projects to name but a few.

In such scenarios, single data model databases typically require merging data from different databases and often even reimplementing some database logic in the application layer as well as the effort to operate multiple database in a production environment.

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How we built our managed service on Kubernetes

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Running distributed databases on-prem or in the cloud is always a challenge. Over the past years, we have invested a lot to make cluster deployments as simple as possible, both on traditional (virtual) machines (using the ArangoDB Starter) as well as on modern orchestration systems such as Kubernetes (using Kube-ArangoDB).

However, as long as teams have to run databases themselves, the burden of deploying, securing, monitoring, maintaining & upgrading can only be reduced to a certain extent but not avoided.

For this reason, we built ArangoDB Oasis.
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ArangoDB Hot Backup – Creating consistent cluster-wide snapshots

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Introduction

“Better to have, and not need, than to need, and not have.”
Franz Kafka

Franz Kafka’s talents wouldn’t have been wasted as DBA. Well, reasonable people might disagree.

With this article, we are shouting out a new enterprise feature for ArangoDB: consistent online single server or cluster-wide “hot backups.”

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ArangoML Pipeline – A Common Metadata Layer for Machine Learning Pipelines

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Over the past two years, many of our customers have productionized their machine learning pipelines. Most pipeline components create some kind of metadata which is important to learn from.

This metadata is often unstructured (e.g. Tensorflow’s training metadata is different from PyTorch), which fits nicely into the flexibility of JSON, but what creates the highest value for DataOps & Data Scientists is when connections between this metadata is brought into context using graph technology…. so, we had this idea… and made the result open-source.

We are excited to share ArangoML Pipeline with everybody today – A common and extensible metadata layer for ML pipelines which allows Data Scientists and DataOps to manage all information related to their ML pipelines in one place.

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libgcc: When exceptions collide

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This is a story of an excursion to the bottom of a deep rabbit hole, where I discovered a foot gun in gcc‘s libgcc. The investigation has cost me several days and I hope that by writing this up I can entertain others and save them the journey.

TL;DR

If a C++ application is compiled with GCC on Linux and statically linked against a non-GLibC C-library (like libmusl), then there is a danger of a data race which leads to a busy loop happening after main() and all static destructors have finished. The race happens, if the application does not use pthread_cancel explicitly and if the very first exception which is thrown in the processes’ life is thrown in two different threads at the same time.
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Multi-Model Database ArangoDB 3.5 released – Distributed Joins, Streaming Transactions, extended GraphDB & Search Capabilities

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With this new release we didn’t wait until Christmas again 🙂

No seriously, we are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog.

Need to know more about multi-model?

Get our technical White Paper

Maybe good to know: Our Managed Service offering, ArangoDB Oasis, will run the full Enterprise Edition of ArangoDB 3.5 including all security as well as special features. You can find more about ArangoDB Oasis and join the Early Bird list on our Managed Service page.

Join the upcoming ArangoDB 3.5 Feature Overview Webinar to have a detailed walkthrough on the release with our Head of Engineering and Machine Learning, Jörg Schad. Read more

Welcome Matt Ekstrom, CRO, and Jörg Schad, Head of Engineering & Machine Learning!

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We are super excited to share the great news of two highly-experienced minds joining team ArangoDB to shape and grow the multi-model vision with us.

Matt Ekstrom is an accomplished enterprise sales leader and joins ArangoDB as Chief Revenue Officer. He brings over 20 years of sales and leadership experience with him and will lead our global sales efforts from San Francisco.

Our new Head of Engineering & Machine Learning is Jörg Schad who brings nearly a decade of experience of researching, designing and developing distributed systems and machine learning pipelines to our team. Read more

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