Systems | Development | Analytics | API | Testing

The industry is rapidly moving towards deeper AI integration than ever before. What was once simply focused on chatbots or recommendation engines has pivoted significantly to AI systems communicating with other AI systems. These AI tools are leveraging multi-agent workflows to accomplish complex tasks that traditional systems have struggled with. Innovation without validation is a liability. Any developer worth their salt will know that these systems require ample testability and validation.

Kubernetes has become the backbone of many modern application deployment pipelines, and for good reason as a container orchestration platform, Kubernetes automates the scaling, deployment, and management of workloads, allowing developers to make their applications easier to manage and deploy at scale without worrying about their service’s dependencies, their user’s operating system, or the intricacies of their data center or infrastructure provider.

At least, the old version of it, the painstaking, build-it-all-yourself, duct-tape-and-dashboards era. If you’ve been around long enough, you’ve seen this movie before. Remember when system engineers were the backbone of IT? Then AWS came along, and in a matter of years, many system engineers evolved into cloud engineers, shifting from racking servers to designing scalable cloud architectures. The role didn’t disappear. It transformed.

If you’ve spent any time building cloud-native systems, you’ve probably tripped over the tricky beast that is gRPC streaming. It’s powerful, flexible, and feels like magic when it works. But the minute you need to test it? Suddenly, you’re in “hold my coffee, I need a week” territory. One of the most common places we see gRPC streams in the wild is when clients connect to asynchronous message buses like Google Pub/Sub.

Continuous integration and continuous delivery/deployment (CI/CD) in API management refers to the automation of building, testing, publishing, and promoting APIs across environments (development, staging, and production) using CI/CD practices. It brings DevOps principles to the API lifecycle, enabling faster and more reliable API releases.

DevOps stands for Development and Operations. The term is a combination of: DevOps originated from the growing need to connect the silos between software development and IT operations. The adoption of agile practices in software development teams enabled developers to release software faster and more reliably. As developers sped up their processes, the Operations side of the organization started to struggle with the impact of agile on deployment, maintenance and stability.

Traditional performance testing often comes late in the delivery cycle, typically just before release. By then, performance issues are usually quite expensive to fix, can delay deployments, and frustrate development velocity. A Shift Left testing approach addresses this by integrating performance testing early in the development cycle so issues surface while they’re still easy and cheap to fix.

It’s been almost a year since we released our Konnect Terraform provider. In that time we’ve seen over 300,000 installs, have 1.7 times as many resources available, and have expanded the provider to include data sources to enable federated management of your Konnect organization. There have been many changes in the last year, but there are some changes that we’ve been holding off on as they would break your CI/CD pipelines.

As enterprises scale their DevOps initiatives, DevOps bottlenecks are emerging from an unexpected source: inadequate test data management practices. But what can you do about it? In this blog, we will explore the common drawbacks in traditional test data management practices. Then, let’s dive into how real-life enterprises are overcoming DevOps bottlenecks with proven solutions that accelerate delivery and reduce operational risks.