Operations | Monitoring | ITSM | DevOps | Cloud

One of the greatest challenges you may face when creating Kubernetes dashboards is getting the full picture of your cluster. Kubernetes is the de-facto standard for container orchestration, but it also has a very steep learning curve. We, at Sysdig, use Kubernetes ourselves, and also help hundreds of customers dealing with their clusters every day. We are happy to share all that expertise with you in the Kubernetes Dashboards.

When we launched Qovery in January 2020, our product was still a prototype, and we onboarded 53 developers to help them deploy their apps in the cloud. At the time, we were only 2 on the team, and our first employee (Patryk Jeziorowski) decided to join us after being one of our first users. 18 months later, 3004 developers from more than 110 countries use Qovery to deploy their apps on their AWS and Digital Ocean account.

Testing in production simply means testing new code changes in production, with live traffic, in order to test the system’s reliability, resiliency, and stability. It helps teams solve bugs and other issues faster, as well as effectively analyze the performance of newly released changes. Its overall purpose is to expose problems that can’t be identified in non-production environments for reasons that may include not being able to mimic the concurrency, load, or user behavior.

Kubernetes enables teams to deploy and manage their own services, but this can lead to gaps in visibility as different teams create systems with varying configurations and resources. Without an established method for provisioning infrastructure, keeping track of these services becomes more challenging. Implementing infrastructure as code solves this problem by optimizing the process for provisioning and updating production-ready resources.

Infrastructure monitoring was difficult enough when entire businesses ran off a few bare metal servers in a dusty, forgotten closet. Other IT infrastructure monitoring tools fell short, unable to provide complete and granular-enough metrics in real time, even when we were only dealing with a handful of systems responsible for running every part of the application stack.