Today’s businesses are looking for ways to better engage customers, improve operational decision making, and capture new value streams. As the world of data continues to grow and its pace of change accelerates, it’s never been more important for businesses to have fast access to actionable data to serve customers with personalized services, in real-time, and at scale. To do this, they need a fast data pipeline.
Depending if you are in the Northern Hemisphere, Spring is upon us. We have been busy at Shipa democratizing developer experience for all and making Kubernetes an afterthought. Grateful for all the community and customer feedback helping us make Shipa better. If you have an idea, check out our new Idea Portal where you can your own ideas. Let’s take a look at what we have delivered in the last pair of sprints.
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.
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.
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.
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.
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.
Going from prototype to production is perilous when it comes to artificial intelligence (AI) and machine learning (ML). However, many organizations struggle moving from a prototype on a single machine to a scalable, production-grade deployment. In fact, research has found that the vast majority—87%—of AI projects never make it into production. And for the few models that are ever deployed, it takes 90 days or more to get there.
When talking about containers, a common confusion for potential users of LXD is that LXD is an alternative to Docker or Kubernetes. However, LXD and Docker are not competing container technologies, and they tend to serve completely different purposes. In this blog, we’ll briefly discuss the differences and the suitable use cases for both.
