Operations | Monitoring | ITSM | DevOps | Cloud

Alerting infrastructure is often complex, with many pieces of the pipeline that often live in different places. Scaling this across many teams and organizations is an especially challenging task. As organizations grow in size, the observability component tends to grow along with it. For example, you may have many components, each of which needs a different set of alerts. You may have several teams, each with a different channel where notifications should be delivered.

Enterprises struggle to bring AI and automation to the edge due to strict requirements and regulations across verticals. Long-term support, zero-trust security, and built-in functional safety are only a few challenges faced by players who wish to accelerate their technology adoption.

In this era where applications are taking over the world, delivering the service to your customer with scalability and security is of the utmost importance. The software delivery platform helps to manage the data flow, traffic management, and security of the data from both sides of the application. If you are studying software delivery platforms, then most of you must have heard about the Codefresh software delivery platform for continuous integration and continuous deployment of the application.

Mickael Alliel 5 Min read September 20th, 2022 DevOps Kubernetes

Digital twins have become somewhat of a buzzword in the past couple of years. But what exactly are they? A digital twin, as its name indicates, is a non-physical copy of a physical object. Just like a digital scan of a physical picture. This virtual element enables a real-time view of all relevant data coming from said object. Depending on the system being studied, specific sensors can be tracked and monitored.

It’s not surprising that most failures are caused by a change somewhere in a system, such as a new code deployment, configuration change, auto-scaling activity or auto-healing event. As you investigate the root cause of an incident, the best place to start is to find what changed. To understand what change caused a problem and what effects propagated across your stack, you need to be able to see how the relationships between stack components have changed over time.