¶ Kubernetes
¶ Dev Ops
Kubernetes entered the world quietly at first, almost like another open-source experiment, but it didn’t take long before it began reshaping how people think about building and running software. Its rise isn’t just a story about containers or infrastructure. It’s a story about a shift in mindset—about teams wanting more control, more automation, more predictability, and more freedom from the old habits that made deployment painful. If DevOps is the practice of bringing development and operations closer together, Kubernetes is the platform that makes that partnership feel natural.
When you look at the history of software delivery, you see a long struggle to balance reliability and speed. Traditional deployment models were rigid, slow, and full of manual steps. Servers had personalities. Environments drifted. Scaling required planning and downtime. Every release felt like a mini-crisis. Kubernetes emerged as an answer to these frustrations by offering a new way to manage applications—not as fragile, hand-crafted deployments but as collections of precisely defined, self-healing, scalable building blocks.
This course is an in-depth journey into Kubernetes. By the end of a hundred articles, the goal is not just that you understand the commands or configuration files, but that you come to see Kubernetes the way modern DevOps teams see it: as the backbone of a reliable, automated, fast-moving software ecosystem.
To understand why Kubernetes matters, you have to understand the world it replaced. Before Kubernetes, the move to containers had already begun simplifying packaging and portability. But running containers at scale was still complicated. You needed to manage hosts, networking, service discovery, deployments, rollbacks, and updates—all manually or with a collection of ad-hoc tools. If a container crashed, something had to restart it. If load increased, someone had to scale things up. If you needed isolation, you had to build it yourself. Kubernetes took all these problems and embedded the solutions into a unified system. What used to take dozens of scripts, tribal knowledge, and countless operational hours could now be expressed declaratively.
This declarative nature is at the heart of why Kubernetes feels different. Instead of telling the system how to do something, you tell it what the end state should look like. “I want five replicas of this application.” “I want this service reachable inside the cluster.” “I want this deployment updated gradually.” And Kubernetes figures out the rest. This idea may sound simple, but when you apply it at scale across dozens or hundreds of services, the result is transformative. It removes entire categories of human error and frees teams to focus on building instead of babysitting.
But Kubernetes is more than a platform—it’s a philosophy. It treats everything as a resource, managed through consistent patterns. Once you understand how one resource type works, many others follow the same mental model. You start seeing your infrastructure as a collection of desired states instead of a collection of fragile machines. This shift is exactly what DevOps strives for: predictable systems, automation over manual effort, and processes that scale smoothly as teams and applications grow.
One of the most powerful things Kubernetes introduces is the idea of infrastructure that takes care of itself. Kubernetes watches your workloads constantly. If a container fails, Kubernetes replaces it automatically. If a node becomes unhealthy, Kubernetes reschedules work somewhere else. If traffic spikes, autoscaling can react. If you introduce a new version of your application, Kubernetes can roll it out gradually, giving you safety nets against unexpected failures. This self-healing behavior isn’t just convenient—it changes the way teams think about risk. Deployments become frequent and routine instead of rare and stressful.
Another appealing feature of Kubernetes is how it encourages clear separation of concerns. Developers focus on container images and application definitions. Operations focus on cluster health, scaling strategies, node management, and networking policies. Yet these two worlds connect cleanly through Kubernetes configurations. DevOps thrives on this kind of collaboration. It removes the friction between teams because everyone knows the boundaries and trusts the platform to enforce them.
The rise of microservices is closely tied to the rise of Kubernetes. As organizations broke monolithic applications into smaller services, managing them became painfully complex. Kubernetes offered a stable foundation where hundreds of services could run independently, discover each other, scale on demand, and deploy without affecting unrelated parts of the system. It created a central language for describing distributed systems—a language that developers, operators, and automation tools could all understand.
This course will explore that language deeply. You’ll see how Kubernetes resources behave, how they interact, and how they form patterns that apply across almost any application architecture. But more importantly, you’ll see how Kubernetes helps teams deliver faster without sacrificing reliability.
One thing often overlooked is how much Kubernetes simplifies the everyday life of engineers. Before Kubernetes, spinning up a new environment meant provisioning servers, installing dependencies, configuring networks, and ensuring everything matched production. With Kubernetes, environments become lightweight. A cluster can host dozens of isolated namespaces, each with its own services, secrets, and workloads. Creating a staging environment becomes a matter of applying configuration rather than provisioning infrastructure.
This convenience ripples into the entire software lifecycle. Testing becomes more accurate because environments mirror production closely. Deployments become safer because Kubernetes handles rollback logic. Monitoring and logging become more consistent because workloads follow the same patterns across clusters. And with GitOps practices—where Kubernetes states are stored in version control—teams gain full visibility into what runs where, closing the loop between development, operations, and automation.
Kubernetes also changes the way organizations think about scalability. Instead of planning capacity with guesswork, Kubernetes makes scaling reactive. Services can scale up automatically when load increases and scale down when the pressure drops. This elasticity is crucial for modern applications, where traffic patterns can be unpredictable. DevOps teams appreciate this because it reduces the need for manual scaling and minimizes resource waste.
At the same time, Kubernetes introduces new concepts like services, nodes, pods, controllers, volumes, and network policies—concepts that might feel unfamiliar at first but become intuitive as you work with them. They represent a vocabulary for describing distributed systems in a clear, consistent way. When you learn Kubernetes deeply, you develop a mental map for how modern cloud-native systems behave, even outside Kubernetes environments.
Throughout the course, you’ll also see why Kubernetes encourages automation everywhere. Manual steps don’t survive long in a Kubernetes world because Kubernetes rewards consistency and punishes drift. This pushes teams toward infrastructure-as-code, continuous deployment, automated testing, policy enforcement, and monitoring-as-default. In many organizations, Kubernetes becomes the catalyst that finally aligns development and operations around a shared, automated workflow.
Another powerful thing about Kubernetes is the ecosystem around it. The Cloud Native Computing Foundation (CNCF) has fostered a huge universe of tools—Helm, Argo CD, Prometheus, Envoy, Istio, Flux, and many more—that extend Kubernetes into a complete platform for building, deploying, securing, and observing modern applications. This ecosystem will be part of our journey, because Kubernetes rarely exists in isolation. It thrives when paired with tools that complement its design.
Yet for all its sophistication, Kubernetes isn’t about creating complexity for complexity’s sake. It’s about abstracting away the chaos that inevitably arises when running distributed systems. It gives teams predictability in a landscape that used to be filled with unknowns. It gives developers a consistent target. It gives operators a powerful control plane. It gives organizations a stable foundation for scaling engineering practices as they grow.
As you move through this course, you’ll understand Kubernetes not only as a set of APIs and controllers but as a way of thinking about building resilient, scalable systems. You’ll develop instincts about designing workloads, structuring deployments, securing clusters, debugging issues, and choosing the right patterns for your applications. You’ll see why Kubernetes became the default platform for cloud-native development and why DevOps teams around the world rely on it as their backbone.
By the end of these 100 articles, you’ll feel confident navigating Kubernetes clusters, designing Kubernetes-native applications, integrating pipelines and GitOps workflows, and understanding the deeper architectural principles behind Kubernetes. But more importantly, you’ll understand why Kubernetes became the cornerstone of the modern DevOps movement—because it brings together automation, reliability, collaboration, and scalability in a way no previous platform managed to achieve.
Kubernetes empowers teams to move fast without breaking things, to scale confidently without fear, and to deploy often without stress. It embodies the very spirit of DevOps: improving how teams build, deliver, and operate software together.
And now you’re ready to begin that journey.
¶ Kubernetes
¶ Part 1: Introduction to Kubernetes and DevOps
1. Introduction to Kubernetes: The DevOps Game-Changer
2. What is Kubernetes and Why Is It Important in DevOps?
3. Kubernetes Architecture: Master, Node, Pods, and More
4. Core DevOps Concepts: Automation, CI/CD, and Kubernetes’ Role
5. Setting Up Kubernetes: Installing Minikube for Local Development
6. Overview of Kubernetes Clusters: What Are They and How Do They Work?
7. Kubernetes Components: Pods, Deployments, ReplicaSets, and Services
8. Kubernetes Nodes and Clusters: A Detailed Breakdown
9. Using kubectl: Kubernetes Command Line Basics
10. Understanding Namespaces and Resource Isolation in Kubernetes
11. Creating Your First Kubernetes Pod
12. The Concept of Containers and How Kubernetes Manages Them
13. Deploying Your First Application with Kubernetes
14. Understanding Kubernetes Networking: Services and DNS
15. Exploring Kubernetes ConfigMaps and Secrets for Configuration Management
16. Scaling Applications with Kubernetes
17. Kubernetes Scheduling: How Pods Get Placed on Nodes
18. Kubernetes Volumes and Persistent Storage Management
19. Running Multiple Applications in Kubernetes with Multi-Pod Deployments
20. Using Kubernetes for Microservices Architecture
¶ Part 2: Intermediate Kubernetes and DevOps Practices
21. Deep Dive into Kubernetes Deployments
22. Rolling Updates and Rollbacks in Kubernetes
23. Managing and Automating Secrets and Configurations in Kubernetes
24. Kubernetes Health Checks: Liveness and Readiness Probes
25. Monitoring Kubernetes Clusters: Metrics and Logs
26. Using Kubernetes Labels and Annotations for Resource Management
27. Setting Up Kubernetes Horizontal Pod Autoscaling
28. Working with StatefulSets for Stateful Applications
29. Managing Namespaces for Multi-Tenant Deployments
30. Implementing Kubernetes Resource Requests and Limits
31. Using Helm for Managing Kubernetes Applications
32. Configuring Kubernetes Ingress Controllers for Traffic Routing
33. Kubernetes Networking Policies: Security and Traffic Flow
34. Managing and Implementing CI/CD Pipelines with Kubernetes
35. Using Kubernetes CronJobs for Scheduled Tasks
36. Exploring Kubernetes Security Features: RBAC, Network Policies, and More
37. Container Registries and Kubernetes Integration (Docker, ACR, ECR)
38. Logging in Kubernetes: Integration with ELK and EFK Stacks
39. Configuring Persistent Storage with Kubernetes and NFS
40. Understanding Kubernetes Autoscaling: Horizontal vs. Vertical Scaling
¶ Part 3: Advanced Kubernetes for DevOps
41. Advanced Kubernetes Networking: CNI, Network Plugins, and Services
42. Deploying Multi-Region Kubernetes Clusters for High Availability
43. Optimizing Kubernetes Cluster Performance and Efficiency
44. Creating Custom Helm Charts for Kubernetes Applications
45. Kubernetes Operators: Automating Complex Workflows
46. Advanced Scheduling Techniques: Taints, Tolerations, and Affinity
47. Advanced Kubernetes Security: PodSecurityPolicies and Secrets Management
48. Integrating Kubernetes with Continuous Integration (CI) Tools (Jenkins, GitLab, etc.)
49. Securing the Kubernetes API Server and Managing Permissions
50. Using Kubernetes with Service Meshes: Istio and Linkerd
51. Kubernetes Federation: Managing Multiple Clusters at Scale
52. Building a Self-Healing Kubernetes Environment with Autoscaling
53. Using Kubernetes for Edge Computing and IoT Applications
54. Implementing Zero Downtime Deployments with Kubernetes
55. Integrating Kubernetes with Cloud Providers (AWS, GCP, Azure)
56. Deep Dive into Kubernetes Storage: Dynamic Provisioning, Persistent Volumes
57. Using Kubernetes for Serverless Architectures with Knative
58. Understanding the Kubernetes Scheduler and Its Role in Workload Management
59. Advanced Kubernetes Networking: Implementing Network Policies at Scale
60. Creating and Managing Custom Resource Definitions (CRDs) in Kubernetes
61. Kubernetes Event-Driven Automation with EventBridge and Cloud Events
62. Monitoring and Observability in Kubernetes with Prometheus and Grafana
63. Using Kubernetes for Blue-Green and Canary Deployments
64. Disaster Recovery and High Availability with Kubernetes
65. Service Meshes in Kubernetes: Integrating Istio for Advanced Networking
66. Using Kubernetes for Hybrid Cloud Architectures
67. Configuring Kubernetes Logging and Monitoring with Fluentd
68. Kubernetes for Multi-Cloud Deployments: Managing Cloud Resources Seamlessly
69. Building and Managing Kubernetes CI/CD Pipelines with ArgoCD
70. Kubernetes Cost Optimization Strategies for Cloud Environments
71. Integrating Kubernetes with AIOps for Proactive Incident Management
72. Container Security Best Practices in Kubernetes
73. Kubernetes for Machine Learning and Data Science Workflows
74. Building a Kubernetes Dashboard for Real-Time Monitoring
75. Kubernetes and GitOps: Automating Kubernetes Operations with Git Repositories
76. Securing Microservices in Kubernetes with JWT and OIDC
77. Kubernetes Troubleshooting: Techniques and Tools for Problem Diagnosis
78. Managing Kubernetes Cluster Lifecycle with Kops and Kubespray
79. Running Kubernetes on Bare Metal for Maximum Control and Flexibility
80. Kubernetes for Continuous Testing and Quality Assurance
81. Implementing Blue-Green and Canary Deployments with Helm
82. Setting Up Multi-Tenant Kubernetes Clusters with RBAC
83. Leveraging Kubernetes with Terraform for Infrastructure as Code
84. Automating Kubernetes with Ansible for Seamless Operations
85. Kubernetes Networking for Advanced Use Cases (Multi-cluster, Hybrid)
86. Running Legacy Applications on Kubernetes with Kubernetes-on-Demand
87. Integrating Kubernetes with DevSecOps Pipelines for Secure Deployments
88. Custom Kubernetes Operators for Automating Application Lifecycle Management
89. Using Kubernetes for Event-Driven Architectures
90. Scaling Kubernetes Clusters: Best Practices for Cluster and Node Management
91. Kubernetes and Cloud-Native Architecture for Modern DevOps
92. Advanced Cluster Federation and Multi-Cluster Management
93. Implementing Continuous Delivery in Kubernetes Environments
94. Kubernetes as a Platform for PaaS and SaaS Deployments
95. Understanding Kubernetes Networking and Distributed Tracing
96. Kubernetes for High-Performance Computing and AI/ML Workloads
97. Serverless on Kubernetes: Managing Functions and Scaling Automatically
98. Kubernetes Resource Management: Advanced Requests, Limits, and QoS
99. Creating and Managing Kubernetes Clusters on Multiple Cloud Platforms
100. The Future of Kubernetes and DevOps: Evolving Trends and Innovations