In the dynamic world of container orchestration, Kubernetes stands out as the undisputed champion, empowering organizations to scale and deploy applications seamlessly. Yet, as the deployment scope increases, so do the associated Kubernetes workload costs, and the need for effective resource capacity planning becomes more critical than ever. When dealing with containers and Kubernetes you can find yourself facing multiple challenges that can affect your cluster stability and your business performance.

Every software-driven business strives for optimum performance and user experience. Observability—which allows engineering and IT Ops teams to understand the internal state of their cloud applications and infrastructure based on available telemetry data —has emerged as a crucial practice to help engage this process. For years, application performance monitoring (APM) was the de facto practice and tooling that organizations have used to keep tabs on their critical systems.

Data volumes are soaring. Environments are increasingly intricate. The risk of applications and systems encountering breakdowns is sky-high, and the mean time to recovery (MTTR) for production incidents is moving in the wrong direction. Disruptions not only jeopardize critical infrastructure but also have a direct impact on the bottom line of organizations. Swift recovery of affected services becomes paramount, as it directly correlates with business continuity and resilience.

The topic of continuous profiling has been an ongoing discussion in the observability world for some time. I said back in 2021 that profiling was set to be the next major telemetry signal in observability, and in fact, since then there’s been growing interest in profiles. Startups and large observability vendors have gotten into this domain. A significant recent step was when the OpenTelemetry project decided to add profiles to its core signals and formalized the open unified specification for that.

Kubernetes has changed the way many organizations approach the deployment of their applications. But despite its benefits, the additional layers of abstraction and reams of data can cause complexity around Kubernetes monitoring. We’ve seen so much of these challenges borne out in the results of the 2024 Observability Pulse survey. In the survey report, 36% of respondents say Kubernetes poses a challenge, and just 10% of organizations say they have full observability into their environments.

The more data you have, the harder it becomes to read through it, let alone identify trends or crucial patterns. Couple that with a shortage of time, and the ability not only to visualize but also to communicate with your data becomes paramount.

Organizations like yours are increasingly reliant on complex IT infrastructures to support their operations. Pervasive use of Kubernetes and microservices architectures continues to up the ante. Amidst this complexity, achieving comprehensive visibility into systems and applications has become both imperative for ensuring performance, reliability, and security, while also becoming ever-more challenging to achieve.

The.NET programming language is taking cloud native deployment and observability seriously, and most notably with the recent announcement of.NET Aspire stack unveiled at the recent.NET Conf 2023. In the latest episode of OpenObservability Talks, we reviewed the journey to making.NET a “by default, out of the box observable platform,” as ASP.NET Core creator David Fowler put it.

There’s no debate — in our increasingly AI-driven, lean and data-heavy world, automating key tasks to increase effectiveness and efficiency is the ultimate name of the game. No matter what job you hold today, you’re likely being pushed to not only do more with less, but also perform your work with a tighter focus on specific outcomes and SLOs.

Application performance monitoring (APM) has been around for a long time. Odds are if you’re tasked with overseeing app performance, you’ve had to deal with this technology to get an understanding of your applications and any issues that can arise in the name of troubleshooting. But there’s a new approach you should consider: application observability.