Kong DevopsSpeakeasy at Kong Summit 2022, 29 Sep 2022

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From YouTube: #DevOpsSpeakeasy at #kongsummit22 with Fabian Hardt (@fabian_hardt)

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A

2022 and now I have the honor to host Fabian heart. Thank you who is also a con Champion yeah.

B

A

So we just learned what con Champion platform program is, and it looks exciting that you get to share with the company insights, from working with customers and with the part of solution, architecture and advisory team. Yeah.

B

That's absolutely right and we are doing some very exciting com, stuff and building architectures with Kong for many customers, and we have a community with the Kong Champions to exchange our experience with with the work with home and Human Service mesh implementations and yeah. It's a really great Community, excellent.

A

Excellent and you you're also a speaker here yep. So what was your talk about? I.

B

Have talked about the combination of modern data stack, which is analytics architecture and the qmo service mesh, implemented in this architecture and embedded in this whole analytics architecture. Yeah.

A

So common service mesh- that's something that we just lightly touched on devops because they didn't really dig into it. Tell me: what's why Puma service mesh? What's the difference between Kuma and other service mesh right there, maybe even more like well-known or more popular or so what makes Human Service mesh different so.

B

It's a very lightweight solution. You can start very quickly and easily I think and the big Advantage is. You can combine the kubernetes workloads and vmbase workloads which could be Legacy services and your old Legacy applications in your company.

A

Yeah so um card Morgan mentioned that as the biggest advantage of Kuma uh Kuma mesh that it can uh be done for hybrid architectures.

B

But I'm wondering from you.

A

Like from the field from working with multiple customers, uh how popular this topology is how many in the industry really combine kubernetes workloads with non-cubernetes of workloads, I.

B

Think when you started at the Green Field, it's not so often the use case scene. But if you have a modernization use cases, then it's uh probably a solution to just integrate existing VM, Solutions and apis on based on VMS with the modern kubernetes workloads, which is the yeah more modern implementation. Stick I think.

A

So it sounds to me like this is the way to go when you modernize and the existing technology- and this is obviously like a very very popular- should be very, very popular use case. There are most of the industry are trying to modernize the technology. Absolutely.

B

A

Then this is where uh Kuma mesh has the advantage over other purely Cloud native ones. I would say.

B

Yeah, so you can integrate the Legacy, uh the m-based apis.

B

To the kubernetes workloads and just use Kumar to modernize modernize, these existing application landscape, yeah, excellent.

A

And this is what you spoke about yesterday,.

B

I spoke to you yesterday about an analytics architecture, especially for analytics, analytics workloads and securing these analytics platforms with the advantages of tumor, because you can use the mtls security features and the observability features just to trace the data. Flows In Your architecture and secure the API calls in your architecture.

A

Whole analytics architecture differs from any other type of application architecture. What's what makes it like an entire different talk about specifically analytics architecture.

B

I think analytics architecture is normally very monolithic architecture because you have a database in Central and just maybe a front-end Analytics tool on top of it. But with the modern data, stick architectures, which is seen in the market, say a volative one year, it's a decentralized and decoupled and it's nearly close to microservice architectures, and this is a big advantage and there came the idea to combine it with the service mesh implementation.

A

A

Analytics architecture is to actually how to make it more microservices oriented as opposite to monolithic what it used to be and how con helps in doubling this particular program.

B

So Cuba helps us to trace all the um the traffic flow in between these components, and this is very good for searching for errors and the mistakes in your analytics architecture as well, and it's uh it had just to secure these traffic flow in between these components, and this is very paid for it, because you have to don't have to do this on your own. I. Think.

A

That makes sense that makes sense. So here we go folks today you look today, we all learned uh faden. Thank you very much for being a devastating and good luck. Thank you. Thank you.