Dynatrace sponsored this post.
To keep up with the pace of digital transformation, organizations across every industry are having to ramp up their efforts to accelerate innovation. To power this charge, they have shifted from traditional on-premises data centers to multicloud environments, with Kubernetes as their application and innovation platform. Adopting microservices, containers and other cloud native technologies allows teams to build new digital services and capabilities faster, so they can adapt to rapidly evolving business needs and continue driving customer success.
Yet, maintaining visibility into these cloud native environments can be a real challenge. Kubernetes is great at automating and managing containerized workloads and applications. However, the dynamic abstraction layer that makes it so flexible and portable across environments can lead to new types of errors that are difficult to find, troubleshoot and prevent. Connecting the complex web of data that monitoring tools generate back to business outcomes is even more difficult. Research shows over two-thirds of CIOs believe the rise of Kubernetes has resulted in too many moving parts for IT teams to manage, and that they need a radically different approach to IT and cloud operations management.
Here are three key reasons why gaining observability into Kubernetes environments is so difficult, along with ways organizations can overcome these challenges.
1. Kubernetes Is Highly Dynamic, so AIOps and Automation Are Essential
While distributed platforms such as Kubernetes enable faster innovation and better scalability, they are also highly dynamic and complex. Clusters, nodes and pods change continuously, so there’s no time to manually configure and instrument monitoring capabilities. IT teams are left scrambling to gain insight into the health of their applications and keep up with the rate at which their Kubernetes environments are changing, time that could be spent launching new services that drive business success.
The only way to maintain visibility into such a dynamic environment is for teams to have the ability to automatically discover services as new ones come online and existing ones scale, and instrument them on the fly. Harnessing continuous automation assisted by AIOps enables platform and application teams to operate large-scale environments with millions of changes in real-time and constantly monitor the full stack for system degradation and performance anomalies.
Not only does this give teams a full view of their Kubernetes environments, but it also enables them to better prioritize tasks by determining which technical changes will have the greatest business impact. With this insight, teams can prevent issues that affect user experience before they occur and refocus on continually optimizing services to deliver the best outcomes for the business and its customers.
2. Kubernetes Runs in Many Places, So a Full-Stack Approach Is Key
In addition to keeping track of microservices and workloads that are constantly changing, the challenge of maintaining observability becomes even more complicated when you consider that organizations often deploy Kubernetes across multiple environments.
This is because Kubernetes can run on any cloud, giving organizations the flexibility of deploying their microservices across many platforms and through managed services such as EKS, AKS and GKE, as well as their own on-premises servers. As such, many organizations use different monitoring tools and cloud platform metrics to manage their Kubernetes environments.
However, manually collecting and correlating the observability data from all these sources, to get the bigger picture and full context, is very time-consuming. Siloed teams with point monitoring solutions further obstruct this and can break down cross-team collaboration.
An effective approach to observability should foster collaboration across the organization by helping to break down silos between teams. As such, it needs to unify all Kubernetes metrics, logs and traces into a single platform with a common data model. It also needs to include data from the traditional services and technology stacks that run alongside Kubernetes deployments, to ensure platform and application teams have a unified view across their entire environment. This end-to-end approach to observability provides greater context that these teams can use to optimize Kubernetes workloads and applications more successfully.
3. With So Much Data, Seeing It in Context of the User Is Critical
It’s also important to remember that observability is not just about accessing more data – it’s also about how organizations can use that data to identify areas of their technology stack that need improving. Metrics, logs and traces are important, but they don’t tell the whole story and indeed often limit developers and application owners by only allowing them to gain a backend perspective. To understand the effect of Kubernetes performance on business outcomes, organizations need the ability to connect the dots between the code they push into production, the underlying cloud platform on the back end and user experience on the front end. This means they need to combine Kubernetes monitoring data with real-time business metrics such as user experience insights and conversion rates.
Teams can achieve these insights more easily with application topology mapping capabilities that automatically visualize all relationships and dependencies within a Kubernetes environment and across the wider cloud technology stack, including user experience data, in real-time. Mapping dependencies vertically between clusters, hosts, pods and workloads — as well as horizontally between data centers, applications and services — allows IT teams to identify which issues are having the greatest overall impact on the business. Correlating user experience with backend performance in this way gives business leaders and digital teams the information they need to make better decisions about how to optimize their systems and where to further invest in their digital infrastructure to improve services and deliver better user experiences.
Ultimately, the most effective way to tackle the observability challenges that arise with a Kubernetes architecture is to embrace the benefits of automation and AI. Combining observability with AIOps and automation allows teams to extend their insight beyond metrics, logs and traces, and incorporate other valuable data, such as user behavior and business KPIs. By rethinking their approach to Kubernetes monitoring in this way, organizations can eliminate silos so their teams spend less time troubleshooting and more time optimizing services to drive better business outcomes.
Lead image via Pixabay.