Introduction: Why Kubernetes is a Must-Have for DevOps in 2026
Hello, colleagues! My name is [Name], and I am a methodologist and instructor on the asibiont.com platform. Today, July 14, 2026, I want to talk about a course that has become a real career springboard for many of our students: CKA + CKAD — Kubernetes Administrator & Developer. If you follow DevOps trends, you know that Kubernetes is not just a trendy technology but the de facto standard for container orchestration. According to the CNCF 2025 report, over 90% of large companies use Kubernetes in production, and demand for specialists with CKA and CKAD certifications has grown by 40% over the past two years. This is not my speculation—the data comes from the official Cloud Native Computing Foundation survey (cncf.io/reports).
But here's the problem: preparing for these exams is a challenge. CKA (Certified Kubernetes Administrator) and CKAD (Certified Kubernetes Application Developer) require not just theory but deep practical skills. The exams are conducted in a real cluster, where you need to complete 24 tasks (CKA) or 19 tasks (CKAD) in 2 hours. A mistake in RBAC configuration or an incorrectly set up Ingress—and you lose points. Many students come to me with the question: "How can I prepare effectively if I have little time?" The answer is simple: you need a personalized program that adapts to your level. This is exactly how our course on asibiont.com works.
What is the CKA + CKAD — Kubernetes Administrator & Developer Course?
This is a comprehensive program that prepares you for two key CNCF certifications: CKA (administration) and CKAD (development). You don't just study theory—you dive into real work with Kubernetes: from setting up a cluster with kubeadm to configuring Service Mesh with Istio and GitOps via ArgoCD. The course consists of 12 modules covering all exam topics, including:
- Kubernetes architecture: etcd, API server, scheduler, controller manager.
- Cluster installation and configuration: kubeadm, kops, cloud solutions (EKS, AKS, GKE).
- Working with Pods, Deployments, StatefulSets, DaemonSets.
- Networking: Services, Ingress, Network Policies.
- Storage: Persistent Volumes, Storage Classes.
- Security: RBAC, Service Accounts, Pod Security Policies, OPA/Gatekeeper.
- Monitoring and logging: Prometheus, Grafana, EFK, Loki.
- CI/CD: ArgoCD, Jenkins X, GitOps.
- Advanced topics: Custom Resources, Operators, Admission Webhooks, Autoscalers.
Each module includes practical tasks in a real Kubernetes cluster, troubleshooting sessions, and mock exams. For example, you will set up a cluster from scratch and then "break" it to learn how to diagnose problems—this is my favorite block because it simulates real incidents.
Who is this course for?
- DevOps engineers who want to validate their skills with certification.
- System administrators transitioning to cloud infrastructure.
- Developers who want to deepen their understanding of Kubernetes for CI/CD and microservices.
- Team leads and architects who need to design scalable systems.
What You Will Learn: Specific Skills and Outcomes
After completing the course, you will be able to:
- Install and configure Kubernetes clusters of any complexity (from single-node to multi-cloud).
- Manage Pods, Deployments, StatefulSets, and other resources via kubectl and the API.
- Configure network policies for microservice isolation.
- Implement security with RBAC, Service Accounts, and Pod Security Standards.
- Implement GitOps with ArgoCD and automate deployments.
- Monitor clusters with Prometheus and log with EFK/Loki.
- Optimize resources using Horizontal and Vertical Pod Autoscalers.
Let me give a practical example. One of our students, Alexander, worked as a system administrator at a bank. After the course, he set up a GitOps pipeline for his department, reducing deployment time from 3 hours to 15 minutes. His case is not unique—according to the DORA report (2024), teams using GitOps deploy code 5 times faster and with fewer errors.
How Learning Works on asibiont.com: AI-Generated Lessons
Our platform uses a unique approach: a neural network generates personalized lessons for each student. How does it work? You take an introductory test, and the AI tutor determines your knowledge level. Then it selects a program: if you are a beginner in Linux, you get more materials on basics; if you are an experienced administrator, the focus is on advanced topics like Operators and Admission Webhooks.
The learning format is text-based, without video. This is intentional: you can read lessons at any time, revisit complex topics, copy commands and configs. The neural network explains complex concepts in simple language, provides real-life examples, and asks practical questions. For example, when studying RBAC, the AI might generate a task: "Configure a Service Account for Jenkins so it can create Pods only in the namespace
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