Estimated reading time: 6 minutes

We released ArangoDB version 3.6 in January this year, and now we are already 6 weeks into the development of its follow-up version, ArangoDB 3.7. We feel that this is a good point in time to share some of the new features of that upcoming release with you!

We try not to develop new features in a vacuum, but want to solve real-world problems for our end users. To get an idea of how useful the new features are, we would like to make alpha releases available to everyone as soon as possible. Our goal is get early user feedback during the development of..

Estimated reading time: 3 minutes

Neo4j, Inc. is the well-known vendor of the Neo4j Graph Database, which solely supports the property graph model with graphs of previously limited size (single server, replicated).

In early 2020, Neo4j finally released its 4.0 version which promises “unlimited scalability” by the new feature Neo4j Fabric. While the marketing claim of “scalability” is true seen from a very simplistic perspective, developers and their teams should keep a few things in mind – most importantly: True horizontal scalability with graph data is not achieved by just allowing..

Estimated reading time: 4 minutes

We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important.

Consider for example:

• Capture of Lineage Information (e.g., Which dataset influences which Model?)
• Capture of Audit Information (e.g, A given model was trained two months ago with the following training/validation performance)
• Reproducible Model Training
• Model Serving Policy (e.g., Which model should be deployed in production based on training statistics)

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.

After two years of planning, preparation and a few lines of code, you can now enjoy an even more comfortable developers’ life with ArangoDB.

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..