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Containers

Understanding the new Kubernetes Gateway API vs Ingress

Kubernetes Ingress is one of the most widely used resources across Kubernetes. It helps to expose your applications and services to the outside world. However, the networking landscape in Kubernetes has significantly evolved. Many modern use cases very quickly exposed the limitations of Ingress API. This led to the creation of the Gateway API, a collection of new resources and APIs which improve and will potentially supersede Ingress in the future. What does this mean for you? Join us and learn everything you need to know about what Gateway API brings to the future of Kubernetes networking.

Five Myths of Kubernetes

The December 2021 report from the CNCF showed that 5.6 million developers use Kubernetes today. We adopted Kubernetes a little over three years ago and felt this is a good time to reflect on what we thought we were getting into and what we have actually experienced. Here are five areas where the prevailing wisdom did not match our expectations.
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Kubernetes Load Test Tutorial

In this blog post we use podtato-head to demonstrate how to load test kubernetes microservices and how Speedscale can help understand the relationships between them. No, that's not a typo, podtato-head is an example microservices app from the CNCF Technical Advisory Group for Application Delivery, along with instructions on how to deploy it in numerous different ways. There are more than 10 delivery examples, you will surely learn something by going through the project. We liked it so much we forked the repo to contribute our improvements.

Kong API Gateway on Kubernetes with Pulumi

The quest for resilience and agility has driven us into the modern age of microservices. Bringing services to market on a microservice architecture demands utilization of sprawling technology offerings and tooling. While daunting at first glance, we can break down the process into 3 major categories: In this hands on series, we will use.

Orchestrating ML Pipelines at Scale with Kubeflow

Still waiting for ML training to be over? Tired of running experiments manually? Not sure how to reproduce results? Wasting too much of your time on devops and data wrangling? Spending lots of time tinkering around with data science is okay if you’re a hobbyist, but data science models are meant to be incorporated into real business applications. Businesses won’t invest in data science if they don’t see a positive ROI.