Kubernetes observability challenges in cloud-native architecture
Kubernetes is the de-facto platform for orchestrating containerized workloads and microservices, which are the building blocks of cloud-native applications. Kubernetes workloads are highly dynamic, ephemeral, and are deployed on a distributed and agile infrastructure. Although the benefits of cloud-native applications managed by Kubernetes are plenty, Kubernetes presents a new set of observability challenges in cloud-native applications.
Let’s consider some observability challenges:
- Data silos – Traditional monitoring tools specialize in collecting metrics at the application and infrastructure level. Given the highly dynamic, distributed, and ephemeral nature of cloud-native applications, this style of metrics collection creates data in silos that need to be stitched together in the context of a service in order to enable DevOps and SREs to debug service issues (e.g. slow response time, downtime, etc.). Further, if DevOps or service owners add new metrics for observation, data silos can cause broken cross-references and data misinterpretation, leading to data misalignment, slower communication, and incorrect analysis.
- Data volume and granular components – Kubernetes deployments have granular components such as pods, containers, and microservices that are running on top of distributed and ephemeral infrastructure. An incredibly high volume of granular data is generated at each layer as alerts, logs, and Continue reading


