How Organizations Are Using Kubernetes to Manage Their Big Data
Kubernetes has become the cloud-native standard. However, it is a complex technology, and companies are still struggling to use it properly and effectively. Adding engineers is one way to solve the problem but doesn’t always address performance problems.
When IT doesn’t have visibility into Kubernetes performance, optimal performance and cost containment are hard to achieve. Automation is key to optimizing performance.
77% of respondents are embracing Kubernetes because they want to improve resource utilization to reduce cloud costs.
The biggest challenges with moving to Kubernetes are “initial deployment and migration.”
The largest percentage of companies (27%) monitor and measure application/workload performance using a manual method or homegrown solution.
Companies are eager to reduce cloud costs, or at least control them. Kubernetes allows you to meld workloads and share resources more efficiently.
Companies also see Kubernetes as a way to smooth out their adoption of the cloud. Kubernetes can be a common plane where teams don’t have to learn three different cloud vendors’ sets of utilities or resource management frameworks. The biggest challenge identified was improving resource utilization with the goal of reducing cloud costs.
Kubernetes is extremely complicated. Manual monitoring cannot keep up, and proprietary solutions are unlikely to be up to the task. It’s always tempting, faced with a new tool like Kubernetes, to use a homegrown solution that already exists.
But with Kubernetes, more custom solutions are required. General-purpose APM won’t cut it; companies need tools purpose built for big data workloads on Kubernetes.