Estimated reading time: 6 minutes

Hello Community,

Welcome to the eighth ArangoDB newsletter of 2021. Woo hoo! Eight is great 🙌🏻 

In this edition, we are excited to share: 

Estimated reading time: 6 minutes

Hello Community,

Welcome to the seventh ArangoDB newsletter of 2021! We hope you are enjoying summer as safely as you can.

In this edition, we are excited to share: 

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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: 

Estimated reading time: 4 minutes

Hello Community,

Welcome to the fifth ArangoDB newsletter of 2021!

In this edition, we are excited to share: 

Estimated reading time: 4 minutes

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: 

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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.

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:

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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!

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.