Systems | Development | Analytics | API | Testing

In my Service Mesh 101 article, I talked about some of the basics behind a service mesh: what it is, what it does and where Envoy fits into a service mesh. Having now covered those basics, I’d like to dig into some more in-depth content focused on the basics of Envoy configuration in a service mesh. Recall from the previous article that several different service meshes use Envoy. Istio is an example of a service mesh that leverages Envoy for its data planes.

If you’ve ever gotten sticker shock after receiving a surprisingly large cloud bill, this might have been your reaction… I think we’ve all been there. While the cloud makes flexible scaling possible, it has also introduced many new services one can use, resulting in increased cloud costs. In this article, I’ll go over the eight ways you can reduce your Kubernetes cloud costs. I use Amazon Web Services (AWS) as an example, but the lessons can also apply to other cloud providers.

What a year we have had! Hypergrowth, a huge community, and lots of recognition. But what we are most proud of is how Kong’s technology has impacted the world, thanks to our users. We can’t thank these users and the community enough for making Kong the most adopted open source API gateway out there. In fact, our technology just crossed 30,000+ stars on Github . And with over 2M instances and more than 257M downloads our OSS community has more than doubled over the last year.

If you’ve done any reading about service meshes, you’ve probably come across mentions of an open source project named Envoy. And if you’ve done any reading about Envoy, you’ve probably seen references to service meshes. How are these two technologies related? How are they different? Do they work together? I’ll attempt to answer all those questions in this blog post’s first and second parts, plus possibly a few more.

We are happy to announce a new major release of Kuma, and a new major release of Kong Mesh built on Kuma! Kuma 1.3 ships with 10+ new features and countless improvements. Kong Mesh ships we enterprise capabilities for large scale service mesh deployments. We strongly suggest to upgrade, in order to take advantage of the latest and greatest when it comes to service mesh.

Here at Kong, we’re advocates for architecting your application as a group of microservices . In this design style, individual services are responsible for handling one aspect of your application, and they communicate with other services within your network to share data. Systems like Kubernetes or the Kuma service mesh help orchestrate traffic and manage network policies so that your microservices can function together as a unified whole.

For most of my software engineering career, my experience with DevOps was all about configuration management. But after many years of experience with some key mentors, I began to see DevOps as so much more. In this article, I’ll summarize my DevOps evolution from a high level. If you’d like to go further in-depth, check out the recording from my Destination: Automation presentation below. ​​

For the DevOps-averse developer, lambdas are heaven. They can focus on writing self-contained and modularized pieces of code, deploying these functions for on-demand execution without being concerned about resource management or infrastructure. Lambda execution , however, can be tricky. Serverless security with the AWS API Gateway can feel daunting, especially when all you want to do is call a simple function as an API endpoint. For this, there’s the ease of Kong Gateway .

The best way to appreciate key concepts involving digital transformation is to look at real-world examples. In a recent Kong webinar, I sat down with Solutions Engineer Ahmed Koshok as he reviewed several real-world case studies that help illuminate the role of microservices in making digital transformation successful for organizations. The case studies included Papa John’s, NextJ Systems, and Yahoo! Japan.

Apps of today differ from those of the past. Evolving organizations like Cargill need to scale quickly to support millions of users, have global availability, manage petabytes or more of data and respond in milliseconds. That’s why modern apps now leverage API automation. ​​