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Entity Resolution in ArangoDB
Estimated reading time: 8 minutes
This post will dive into the world of Entity Resolution in ArangoDB. This is a companion piece for our Lunch and Learn session, Graph & Beyond Lunch Break #15: Entity Resolution.
In this article we will:
- give a brief background in Entity Resolution (ER)
- discuss some use-cases for ER
- discuss some techniques for performing ER in ArangoDB
Red Hat Certifies ArangoDB Kubernetes Operator
Estimated reading time: 2 minutes
Hi all!
I think this year has thrown us for quite a loop, full of unexpected occurrences, both joyous and difficult. With everything going on in the world at the moment, we feel it’s more important than ever to celebrate what you can .
So with that, just a quick note from us that our Kubernetes Operator has achieved Red Hat OpenShift Operator Certification. And as far as we can tell, this means we are the first graph database to reach full certification for Red Hat OpenShift. Huzzah!
(more…)Efficient Massive Inserts into ArangoDB with Node.js
Estimated reading time: 3 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. Read more
ArangoDB and the Cloud-Native Ecosystem: Integration Insights
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.