The DevOps and Platform Engineering space certainly is one that evolves fast. As new development paradigms get consumed, supporting the development pipeline is crucial. Pushing a public release of v0.2.x on March 30th, 2022, Dagger, from the creators of Docker, is another approach in portability and consistency in CI/CD pipelines. What the Docker Container has done applications, Dagger is hoping to achieve that with CI/CD pipelines.

Having multiple environments that can be dynamically configured has become akin to modern software development. This is especially true in an enterprise context where the software release cycles typically consist of separate compute environments like dev, stage and production. These environments are usually distinguished by data that drives the specific behavior of the application.

In previous blog posts, my colleagues and I have introduced and explored the Calico eBPF data plane in detail, including learning how to validate that it is configured and running correctly. If you have the time, those are still a great read; you could dive in with the Calico eBPF Data Plane Deep-Dive.

Shipping complex applications at high velocity lead to increased failures. Longer pipelines, scattered microservices, and more code inherently lead to bigger complexity where small mistakes may cost you big time.

Event ingestion is one of the most mission-critical components at Sentry, so it’s only natural that we constantly strive to improve its scalability and efficiency. In this blog post, we want to share our journey of designing and building a distributed ingestion infrastructure—Sentry Points of Presence— that handles billions of events per day and helps thousands of organizations see what actually matters and solve critical issues quickly.

Continuous integration is a DevOps practice, where developers continuously integrate the code changes into a central repository. It most often refers to the build or the integration stage of the software release process.A continuous integration service helps to automatically build and run unit tests on the new code changes to find any errors instantly.

In response to the explosive growth of Internet of Things (IoT) devices, organizations are embracing edge computing systems to better access and understand the enormous amount of data produced by these devices. As the name suggests, edge computing moves some storage and computing resources out of the central data center and closer to where the data is generated at the edge of the network, whether that’s a factory floor, retail store, or automated car.