Organizations are moving to micro-services and container-based architectures because these modern environments enable speed, efficiency, availability, and the power to innovate and scale more quickly. However, when it comes to troubleshooting distributed cloud native applications, teams face a unique set of challenges due to the dynamic and decentralized nature of these systems.

Maybe you’re still using monolithic applications, built and refined over many years. You understand that shifting to microservices or containerized architectures is a huge and daunting task. You’re probably grappling with the limitations of legacy systems—maybe they’re slow, tough to update, or can’t scale as you’d like. And you’re likely using more traditional IT monitoring tools or even some cloud observability tools.

In today's digital landscape, Observability and telemetry data play a crucial role in ensuring the performance, reliability, and security of modern applications and services. However, as data volumes explode due to the proliferation of micro-services, cloud-based applications, and connected devices, existing architectures are hitting a scalability wall.

Who is software for? It’s an interesting question, because there’s an obvious answer. It’s for the users, right? If your job is to write software, then it’s implied that the most important thing you should care about is the experience people have when they use your software.

OpenTelemetry can be used for generating and collecting telemetry signals like logs, metrics, and traces. The advantage of using OpenTelemetry for observability is that it is open-source and frees you from vendor lock-in. You can use OpenTelemetry for multiple use cases OpenTelemetry is an open-source project which has emerged as the standard for achieving comprehensive observability in modern applications.

Metrics are closely associated with cloud infrastructure monitoring or application performance monitoring – we monitor metrics like infrastructure CPU and request latency to understand how our services are responding to changes in the system, which is a good way to surface new production issues. As many teams transition to observability, collecting metric data isn’t enough.

Kubernetes has been around for nearly 10 years now. In the past five years, we’ve seen a drastic increase in adoption by engineering teams of all sizes. The promise of standardization of deployments and scaling across different types of applications, from static websites to full-blown microservice solutions, has fueled this sharp increase.