Containerization and Kubernetes have taken the DevOps world by storm in the past decade. More and more companies have turned to this technology to enhance their deployment workflows and cut costs. Examples like Pokemon Go and OpenAI would not have been feasible without Kubernetes. While Kubernetes is a growing technology in the current times, it comes with a relatively steep learning curve.
Leading organizations around the world are adopting cloud-native technologies to build next-generation products because cloud native gives them the agility that they need to stay ahead of their competition. Although cloud native and Kubernetes are very disruptive technologies, there is another technology that is probably the most disruptive technology of our generation, and that is artificial intelligence (AI) and its subset, machine learning (ML).
Over the weekend, I visited one of my favorite grocery stores to pick up one item, my favorite fruit e.g star fruit. Because of the location, the grocery star started to implement parking validation so folks would not abuse their free parking deck for extended periods of time. As I just had a handful of star fruits, I decided to use the self-checkout. This was my first time buying produce via self-checkout.
Picture this… You just got your second cup of coffee and you’re walking back to your desk. The phone in your pocket begins vibrating as a flurry of emails show services are bouncing. The notifications say services were down but are now back up. You hustle back to your desk, spill a little coffee on your notepad, and open the VMware vSphere client to see a host in a disconnected state.
Enterprises lately are understanding the need to move their business applications to cloud. Well, one of the major contributing reasons for this move is the rising OpEx and the heavy debt of managing applications in their own environment. Cloud providers are waiting for such opportunities where enterprises are planning to migrate to cloud, the question is will it help or not?
Prometheus 2.35 was released last month, focusing on a better integration with cloud providers. It also improved the service discovery, performance, and resources usage. One key change was the migration to Go v1.18. It has brought some changes in the support for TLS 1.0, 1.1, and certificates signed with the SHA1 hash function. Welcome to this first edition of What’s new in Prometheus. We love Prometheus, the de-facto open source standard monitoring tool!
For an application developer, there is certainly a long road between an idea/feature and getting deployed into production with Kubernetes. From a development perspective, having a low barrier of entry and the ability to iterate is key. From a platform engineering/DevOps perspective, creating gains in engineering efficiency all while creating and enforcing policies that do not stifle innovation is key.
Kubernetes, a Greek word meaning pilot, has found its way into the center stage of modern software engineering. Its in-built observability, monitoring, metrics, and self-healing make it an outstanding toolset out of the box, but its core offering has a glaring problem. The Kubernetes logging challenge is its ephemeral resources disappearing into the ether, and without some 2005-style SSHing into the correct server to find the rolled over log files, you’ll never see the log data again.
With recent releases, the Kubewarden stack supports verifying the integrity and authenticity of content using the Sigstore project. In this post, we focus on Kubewarden Policies and how to create a Secure Supply Chain for them.
