Today we are excited to invite everybody to take the first public preview of Azure on ArangoDB Oasis for a test ride. In case you haven’t joined Oasis yet, please find more details about our offering and a 14-day free trial on cloud.arangodb.com. Just choose Microsoft Azure as your cloud provider and choose from the many regions we already support.
You can share all feedback with us about regions you’d love to see added or other improvements on slack. Please use the #oasis channel on Community Slack or raise an issue via the “Request Help” button in the bottom right corner of Oasis.
Please note that this is a public preview and not meant to be run in production. Read more
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 ArangoDB, so we can validate our designs and implementations against the reality, and adjust them if it turns out to be necessary.
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 distributing data to different machines. Read more
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)
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. Read more
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?”. Read more