Cloud has become the de-facto standard for new application development. Kubernetes solves many problems of modern-day cloud infrastructure. It has made microservices-based distributed software systems possible, enabling organizations to provide on-demand scaling. But at the same time, Kubernetes has also increased operational complexity.
Observability vs monitoring has been a common topic in DevOps recently. There has been a lot of debate, and I have learned a lot from them when I started my observability journey. Most literature on observability is associated with a particular product or shares a textbook definition.
Cloud computing and containerization have brought many benefits to IT systems, like speed to market and on-demand scaling. But it has also increased operational complexity. Applications built on dynamic and distributed infrastructure are challenging to operate and maintain. A robust observability framework can help application owners stay on top of their software systems.
Wherever you read about observability, you are told that there are "3 pillars" of observability - Metrics, Traces and Logs.
An image that is generally projected is something like this.
