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
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June 2021: What’s the Latest with ArangoDB?

Estimated reading time: 5 minutes

Hello Community,

Welcome to the sixth ArangoDB newsletter of 2021! Hard to believe we are already half-way through 2021 🤯

In this edition, we are excited to share: 

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May 2021: What’s the Latest with ArangoDB?

Estimated reading time: 4 minutes

Hello Community,

Welcome to the fifth ArangoDB newsletter of 2021!

In this edition, we are excited to share: 

We hope you enjoy it!

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April 2021: What’s the Latest with ArangoDB?

Estimated reading time: 4 minutes

Hello Community,

Welcome to the fourth ArangoDB newsletter of 2021!

In this edition, we share details about: our latest and greatest lunch breaks, part four of our ArangoML blog series, as well as a guest article featured in DZone about the C++ memory model.

We hope you enjoy!

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March 2021: What’s the Latest with ArangoDB?

Estimated reading time: 4 minutes

We’ve officially entered Spring this month, so perhaps it’s time for a little spring cleaning? Here’s a few suggestions from us: 

Anything else you’d like to learn about? Drop us a line anytime at community [at] arangodb [dot] com.

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February 2021: What’s the Latest with ArangoDB?

Estimated reading time: 4 minutes

Hello community!

This month we post on the blog bearing gifts up upcoming events, content we think you might enjoy, as well as suggestions around how to get involved with the community. Read on!

We’d also love to hear from *you*! Drop us a line anytime at community [at] arangodb [dot] com.

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January 2021: What’s the Latest with ArangoDB?

Estimated reading time: 4 minutes

Happy New Year to our amazing community!

We hope your year is off to a good start. Even though things don’t seem too different (yet), we’re definitely feeling a fresh mindset and doing our best to stay hopeful and healthy. 

Here’s what we’re excited about so far:

Read on for more!

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Red Hat Certifies ArangoDB Kubernetes Operator

Estimated reading time: 2 minutes

ArangoDB Kubernetes Operator ach

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!

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Massive Inserts into ArangoDB With NodeJS

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. Read more

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ArangoDB and the Cloud Native Ecosystem

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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