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Dynatrace sponsored this post.
Organizations are feeling mounting pressure to accelerate their digital transformation plans, in order to create new revenue streams, manage customer relationships and deliver exceptional user experiences. As a result, they’re increasingly investing in cloud native applications, containers and microservices-based architectures.
Containers especially have taken off. A 2020 Cloud Native Computing Foundation survey found that 84% of organizations currently use containers in production, with Kubernetes — used by 78% of those enterprises deploying containers — emerging as the de facto solution for managing them. The dynamic nature of Kubernetes and containers, and the speed at which they can be spun up and down, also means that as enterprises speed up digital transformation, they’re also speeding into an extremely complex IT environment. And due to the limits of traditional monitoring, that level of complexity results in little observability or insight across the technology stack. This affects everyone, from ITOps to app developers to the Kubernetes platform operators themselves.
Digital transformation with Kubernetes must go hand-in-hand with an AI-driven approach to infrastructure monitoring. Without AI, you just won’t be able to capture the full picture of what’s happening in your Kubernetes environment and the infrastructure underpinning it.
Kubernetes-based cloud platforms like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS) and Microsoft Azure Kubernetes Service (AKS) reflect just how quickly dynamic containers and microservices have been integrated into organizations’ multicloud environments. The problem is, those same containers and microservices can come and go within seconds. That’s great for making more agile and flexible IT environments, but it’s a nightmare for observability.
Trying to maintain manual observability and configuration for containers, microservices and Kubernetes is time- and labor-intensive. IT teams can’t afford this — especially now, when they’re being tasked with doing more and delivering faster results, with fewer resources and less margin for error at their disposal. Not only is manual observability draining on IT productivity, it also misses the infrastructure layer of Kubernetes environments: containers, pods, nodes, clusters, and the rich digital business analytics these components provide. A lack of observability over these infrastructure components and interdependencies makes it that much harder to have a complete understanding of your environment and, consequently, makes it harder to optimize costs and weed out performance degradations.
All of this adds up to more complex environments with little observability and system anomalies that are potentially running unchecked because there’s no way to find and remediate their root causes. At a time when organizations are being judged more quickly on the speed and quality of their digital services, this can all have potentially severe knock-on effects for the business. Being able to deploy complete observability over not just applications, microservices and Kubernetes, but the infrastructure they run on, is critical to getting a full picture of your environment and optimizing performance and availability accordingly.
Dynamic environments require dynamic solutions. ITOps teams, app developers and Kubernetes platform operators all need to be able to self-discover and automatically instrument changes in their cloud environments — and capture observability data in real-time — in order to keep up with the speed of thousands of containers and microservices in production.
Just as continuous automation and AI-assistance have changed the game for much of how IT environments function, they’re also essential for providing the sort of fast, automatic code-level insights required for getting advanced observability across cloud-native applications, containers and microservices running in Kubernetes. AI-assistance shifts IT’s posture from reactive to proactive and multiplies a team’s productivity, by eliminating the wasted motions of tedious manual work and freeing them up for more innovative, business value-adding tasks.
This AI-driven approach to infrastructure monitoring makes it faster and easier to achieve advanced observability deep into your Kubernetes infrastructure layer, so that teams are not only seeing into every container, pod, node, cluster, and microservice, but also seeing their impact on real users — their customers — and the business.
This is a level of insight that is prohibitively difficult and time-consuming for teams to manually obtain. AI-assistance makes it possible to get advanced observability quickly, automatically, and painlessly — and with it, remedy problems at their root and optimize cost, capacity, and workloads, even when everything is fine.
IT teams are trying to provide best-in-class user experiences while ramping up digital service capacity faster than ever. Organizations cannot afford observability approaches that require manual configuration, scripting, special add-ons — anything that makes it slower, harder and more expensive to achieve observability into Kubernetes environments. They also cannot afford that lack of observability to allow potential issues to go undetected or unresolved, and put the user experience at risk.
This is a solvable problem. Enterprises need to invest in AI-powered advanced observability that digs into the infrastructure data needed to optimize containers, microservices and Kubernetes environments; identify and auto-remediate anomalies; and provide always-accurate insights into the billions of interdependencies running in their technology stacks.
When you don’t have those insights, you can’t deliver for your users, for your business, or for your own teams.
Amazon Web Services is a sponsor of The New Stack.
Feature image via Pixabay.
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