Estimated reading time: 6 minutes

Nothing performs faster than arangoimport and arangorestore for bulk loading or massive inserts into ArangoDB. However, if you need to do additional processing on each row inserted, this blog will help with that type of functionality.

If the data source is a streaming solution (such as Kafka, Spark, Flink, etc), where there is a need to transform data before inserting into ArangoDB, this solution will provide insight into that scenario as well.

Let’s delve into the specifics.

Estimated reading time: 9 minutes

Welcome 2020! To kick off this new year, we are pleased to announce the next version of our native multi-model database. So here is ArangoDB 3.6, a release that focuses heavily on improving overall performance and adds a powerful new feature that combines the performance characteristics of a single server with the fault tolerance of clusters.

If you would like to learn more about the released features in a live demo, join our Product Manager, Ingo Friepoertner, on January 22, 2020 – 10am PT/ 1pm ET/ 7pm CET for a webinar on “What’s new in ArangoDB 3.6?”.

We are working on the release of ArangoDB 3.6 and today, just in time for the holiday season, we reached the milestone of RC2. You can download and take the RC2 for a spin: Community Edition and Enterprise Edition.

The next version of the multi-model database will be primarily focused on major performance improvements. We have improved on many fronts of speeding up AQL and worked on things like:

• Subquery performance
• Parallel execution of AQL queries that allows to significantly reduce gathering time of data distributed over several nodes
• Late document materialization that reduces the need..

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.

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 ArangoGraph.

ArangoDB ArangoGraph is a managed..

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.”

If you do not care for an abstract definition and would rather directly see a working example, simply scroll down to Section “A full cycle” below.

Snapshots of arbitrary sized complex raw datasets, be it file systems, databases, etc.: they are extremely useful for ultra-fast..

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..

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..

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.

We are closing in on the general availability of ArangoDB 3.5. With this (hopefully) last release candidate for the new version, we want to highlight a pretty neat new feature, which many of you requested – a much simpler way to use ACID transactions without the need to write any Javascript code.

Get the latest Releaseof ArangoDB 3.5: Community and Enterprise.

Before we dive in, we want to send another big THANK YOU to the many Avocaderas and Avocaderos who took the release candidates for a spin and shared their findings with us. Immensely helpful for the whole team to learn where we were..