¶ Kubernetes (Open Source)
¶ Cloud Technologies
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Kubernetes has become one of those rare technologies that reshaped the way teams think about building, deploying, and operating software—so much so that it’s hard to remember what the world felt like before it. It didn’t arrive quietly. It emerged in a landscape already bursting with tools, ideas, and attempts to solve container orchestration, yet somehow it managed to stand out and gradually become the backbone of modern cloud-native infrastructure. Today, when people talk about scalability, resilience, portability, or the freedom to run workloads anywhere, it's almost always Kubernetes that sits behind the scenes making those possibilities real.
This course of a hundred articles is meant to be more than a technical guide. It’s a journey into the mindset that Kubernetes encourages—a way of thinking that blends software engineering with distributed systems theory, cloud principles, and a deep appreciation for automation. Most people encounter Kubernetes through necessity: applications become too large to manage manually, environments grow inconsistent, or teams realize they need a more reliable and standardized way to deploy software at scale. When that moment comes, Kubernetes feels like both a solution and a new frontier. It solves problems you’ve struggled with, but it also introduces questions you never had to think about before. That’s part of what makes learning it so rewarding.
Before diving deeper into what the next hundred articles will explore, it's important to understand why Kubernetes matters so profoundly in the world of cloud technologies. The shift to microservices, distributed systems, and container-based deployments fundamentally changed how organizations develop and run software. Containers made it possible to package applications consistently, but once you had hundreds—or thousands—of those containers running across clusters of machines, managing them manually became impossible. Kubernetes emerged as the system that could take responsibility for orchestrating this entire universe of workloads: scheduling them intelligently, healing them automatically, scaling them predictively, and updating them gracefully without interrupting users.
At its core, Kubernetes is a declarative system. Instead of telling it how to do everything step by step, you describe the state you want, and Kubernetes figures out how to achieve and maintain that state. This approach feels almost magical the first time you see it in action. You define how many instances of a service should be running, what resources they require, how they should restart, what conditions they must meet, and how traffic should reach them. Kubernetes takes those definitions and watches your cluster continuously, correcting any drift from the desired state. A node crashes? Kubernetes reschedules the workloads somewhere else. A container misbehaves? It restarts it automatically. Demand spikes? It scales your services up as needed. This constant reconciliation loop is one of the most transformative concepts you encounter when working with Kubernetes, and understanding it well changes how you design systems overall.
But Kubernetes isn't only a technology—it’s a philosophy of building systems that are resilient by default, portable across environments, and managed through automation rather than manual intervention. This philosophy fits naturally within the broader world of cloud technologies, where elasticity and statelessness aren’t just preferences but necessities. Cloud infrastructure is inherently dynamic. Machines appear and disappear. Networks shift. Workloads scale unpredictably. Kubernetes embraces this volatility and provides a stable layer on top of it, giving teams confidence that their applications will keep running smoothly even in the midst of constant changes.
Working with Kubernetes in cloud environments unlocks even more potential. Whether you deploy on Google Kubernetes Engine, Amazon EKS, Azure AKS, or a custom cluster on your own hardware, the behavior remains consistent. This level of portability is one of Kubernetes’ strongest advantages. It gives organizations the freedom to choose where their workloads run without being locked into a single vendor. It also allows teams to build hybrid workloads that span on-premises datacenters and cloud environments, or multi-cloud environments that leverage the strengths of multiple providers. In a world where flexibility often determines long-term success, Kubernetes plays a crucial role in enabling that flexibility.
The open-source nature of Kubernetes is another reason it has grown so rapidly. It isn’t controlled by a single company’s roadmap or restricted by proprietary APIs. Instead, it thrives on contributions from engineers, companies, and communities around the world. That openness fuels rapid innovation. New features emerge regularly, improvements are driven by real-world needs, and the ecosystem surrounding Kubernetes grows stronger with every passing year. Tools like Helm, Istio, Kustomize, Prometheus, ArgoCD, and Flux have all become part of that ecosystem, helping teams manage applications, secure clusters, automate deployments, observe workloads, and extend Kubernetes in countless ways. By learning Kubernetes, you also learn how to navigate this ecosystem and choose the tools that best support your goals.
One of the most fascinating aspects of Kubernetes is how it changes your understanding of application architecture. You start thinking more deeply about stateless and stateful design, service discovery, horizontal scaling, pod lifecycles, health checks, and rolling updates. You learn to appreciate concepts like reconciliation loops, immutability, container runtime behavior, and resource limitations. Those ideas influence everything—from how you write applications to how you reason about traffic flow or failure modes. Over time, Kubernetes encourages you to design applications that embrace cloud-native principles rather than resist them.
As you move further into this course, you’ll see how Kubernetes teaches discipline without being restrictive. It nudges you toward best practices: using manifests instead of ad-hoc deployments, treating infrastructure as code, separating configuration from code, enforcing resource limits, and building CI/CD workflows that automate deployment pipelines. These practices aren’t just technical niceties—they’re foundations for reliable software systems. Kubernetes becomes the orchestrator, but your engineering habits become the real engine behind the systems you build.
Kubernetes also introduces you to the idea of clusters as living systems. A cluster isn’t static. It evolves. Nodes come and go. Pods are created and destroyed. Controllers constantly react to changes. The system is always in motion. Observing this movement can be mesmerizing at first—you watch pods spin up, get rescheduled, pass readiness checks, and start receiving traffic within moments. It’s a glimpse into the world of distributed computing, where no single component is permanent, yet the system as a whole remains stable through coordination and redundancy. Many engineers find this experience transformative. It changes how they think about infrastructure—from something rigid and fragile to something elastic and self-adapting.
But Kubernetes isn’t just about automation and abstraction. It demands a thoughtful understanding of what’s happening under the hood. You learn about control planes, API servers, schedulers, kubelets, container runtimes, networking layers, and storage interfaces. You discover how traffic flows from node to pod through virtual networks, how storage volumes are provisioned, how load balancing works inside and outside the cluster, and how certificates and authentication keep everything secure. These details matter because they determine the performance, reliability, and security of the applications you deploy. Kubernetes rewards people who take the time to understand its internals—not because you must master every detail, but because deeper knowledge gives you more confidence to design systems that work well in production environments.
As you progress through the hundred articles in this course, you’ll gradually build an intuition for Kubernetes. That intuition is more important than memorizing commands or YAML structures. It’s the kind of understanding that makes you comfortable solving problems in the moment—whether a pod won’t schedule, a deployment keeps restarting, a service isn’t reachable, or a node behaves unpredictably. Kubernetes can feel overwhelming at first, but once you grasp its patterns, it becomes one of the most empowering tools you’ll ever use.
The most exciting part of learning Kubernetes is how quickly the knowledge becomes valuable. Organizations across the world rely on Kubernetes to run their mission-critical systems. They need engineers, DevOps specialists, SREs, platform engineers, security analysts, and cloud architects who understand how Kubernetes works, how to operate it, how to troubleshoot it, and how to use it to build scalable and resilient platforms. This course aims to equip you with that level of competence—not through shortcuts but through a steady, practical exploration of the ideas and capabilities that define Kubernetes.
By the end of the full series, Kubernetes won’t feel like a wild ocean of YAML and commands anymore. It will feel like a coordinated and predictable system that you can navigate with clarity. You’ll understand how to deploy applications, operate clusters, secure workloads, monitor performance, debug issues, manage storage, optimize resource use, integrate CI/CD pipelines, and extend Kubernetes with the broader tooling ecosystem. You’ll also gain the ability to think cloud-natively, designing applications and systems that thrive in dynamic environments rather than struggle against them.
Kubernetes is more than a tool; it's a turning point in how modern infrastructure works. It brings order to distributed systems, reliability to cloud deployments, freedom to software teams, and a sense of stability to environments that change every second. As you begin this journey, keep curiosity at the center. Explore. Experiment. Try things. Break things intentionally to understand how the system reacts. Kubernetes rewards hands-on exploration more than theoretical study.
This introduction is simply the starting point. The deeper you go, the more you’ll appreciate the elegance behind Kubernetes’ complexity. There’s a reason it has become the foundation of the cloud-native world, and through this course, you’ll come to understand that foundation not just in theory but in practice—confidently, creatively, and with a sense of mastery that grows article by article.
¶ Kubernetes (Open Source)
¶ Beginner Level:
1. Introduction to Kubernetes: What It Is and Why It’s Important
2. The Basics of Containers and Container Orchestration
3. Understanding Kubernetes Architecture: Nodes, Pods, and Clusters
4. Setting Up a Kubernetes Cluster on Your Local Machine
5. Getting Started with kubectl: The Kubernetes Command Line Tool
6. Kubernetes Core Concepts: Pods, Deployments, and Services
7. How to Deploy Your First Pod in Kubernetes
8. Managing Pods with kubectl: Basic Commands and Operations
9. Exploring Kubernetes Namespaces and Their Use Cases
10. Kubernetes Deployments: Automating Application Updates
11. Scaling Applications in Kubernetes: Horizontal Scaling and Pods
12. Introduction to Kubernetes Services and Networking
13. How to Expose Your Application Using Kubernetes Services
14. Persistent Storage in Kubernetes: Using Volumes and Persistent Volumes
15. Exploring Kubernetes ConfigMaps for Configuration Management
16. Managing Secrets in Kubernetes for Sensitive Data
17. How to Monitor Pods with Kubernetes Logs
18. Basic Troubleshooting in Kubernetes: Pods, Nodes, and Logs
19. Using Kubernetes Events to Monitor Cluster Health
20. Understanding Kubernetes Scheduling and Pod Affinity
21. Kubernetes Resource Requests and Limits: Optimizing Resource Allocation
22. How to Set Up and Use Kubernetes Ingress for External Access
23. Running Multiple Applications in One Kubernetes Cluster
24. Getting Started with Helm for Kubernetes Package Management
25. Introduction to Kubernetes Dashboard for Web-Based Management
26. How to Perform Rolling Updates and Rollbacks in Kubernetes
27. Kubernetes Networking: Pod-to-Pod Communication
28. Setting Up Kubernetes on Cloud Providers: AWS, Azure, and GCP
29. Introduction to Kubernetes RBAC (Role-Based Access Control)
30. Deploying Stateful Applications in Kubernetes
31. Exploring Kubernetes Auto-scaling: Horizontal Pod Autoscaler
32. Using Helm to Deploy a Simple Application on Kubernetes
33. How to Use Kubernetes to Manage Microservices
34. Kubernetes for CI/CD: Using GitOps for Automation
35. How to Use Kubernetes Namespaces for Multi-Tenant Applications
36. Best Practices for Writing Kubernetes YAML Manifests
37. Introduction to Kubernetes CronJobs for Scheduled Tasks
38. Kubernetes Volume Management: Dynamic Provisioning and Storage Classes
39. How to Integrate Kubernetes with Continuous Integration Systems
40. Using Kubernetes for Cloud-Native Applications
41. Kubernetes Dashboard: Installing, Configuring, and Using
42. How to Manage Configurations with Kubernetes ConfigMaps and Secrets
43. Introduction to Kubernetes Network Policies for Security
44. Exploring Kubernetes Horizontal Pod Autoscaler (HPA)
45. How to Perform Basic Kubernetes Cluster Maintenance
46. What Is Kubernetes Federation? Managing Multi-Cluster Deployments
47. Kubernetes Events: An Overview of the Kubernetes Event System
48. Using Labels and Annotations in Kubernetes
49. How to Manage Kubernetes Application Lifecycle
50. Introduction to Kubernetes Operators for Custom Resources
¶ Intermediate Level:
51. Advanced Kubernetes Networking: Services, Endpoints, and DNS
52. How to Secure Kubernetes Clusters: Network Policies and RBAC
53. Setting Up Kubernetes in High Availability (HA) Mode
54. Running Stateful Applications in Kubernetes with StatefulSets
55. Kubernetes Monitoring and Logging with Prometheus and Grafana
56. Integrating Kubernetes with External Load Balancers
57. Kubernetes Scheduling and Affinity Rules for Efficient Resource Usage
58. Using Helm Charts for Reusable Kubernetes Deployments
59. Kubernetes Persistent Storage with NFS, Ceph, and Cloud Storage
60. How to Integrate Kubernetes with AWS EKS, Azure AKS, and GCP GKE
61. Configuring Kubernetes Ingress Controllers for Multi-Tier Applications
62. Using Kubernetes Secrets Management for Better Security
63. Kubernetes Custom Resource Definitions (CRDs) for Extending Kubernetes
64. Setting Up Kubernetes Metrics Server for Resource Monitoring
65. Managing Secrets and Configurations with Kubernetes Vault Integration
66. Using Kubernetes Network Policies for Advanced Security
67. Setting Up and Managing Multiple Kubernetes Clusters
68. How to Use Kubernetes with Docker and container runtimes
69. Kubernetes Logging with Fluentd and Elasticsearch
70. Advanced Kubernetes Scheduling: Taints and Tolerations
71. How to Use Kubernetes with Serverless Frameworks (e.g., Kubeless, OpenFaaS)
72. Implementing Continuous Deployment with Kubernetes
73. How to Perform Blue-Green and Canary Deployments in Kubernetes
74. Managing Application Configuration and Secrets in Multi-Cluster Kubernetes
75. How to Integrate Kubernetes with CI/CD Pipelines (Jenkins, GitLab)
76. Kubernetes and Service Mesh: Istio vs. Linkerd
77. Best Practices for Resource Limits and Requests in Kubernetes
78. Deploying and Managing Kubernetes on Bare Metal Infrastructure
79. Using Helm for Continuous Delivery in Kubernetes
80. Kubernetes Federation for Cross-Cluster Deployments
81. Integrating Kubernetes with Terraform for Infrastructure as Code
82. How to Set Up and Use Kubernetes Resource Quotas
83. Optimizing Kubernetes Resource Usage with Vertical Pod Autoscaler (VPA)
84. Creating and Managing Custom Kubernetes Controllers
85. Using Kubernetes Metrics and Prometheus to Scale Applications
86. How to Use Kubernetes CronJobs for Batch Processing
87. Running GPU-Accelerated Workloads on Kubernetes
88. Securing Kubernetes Clusters with Pod Security Policies
89. Advanced Troubleshooting in Kubernetes with kubectl and Logs
90. Scaling Kubernetes Deployments with the Horizontal Pod Autoscaler
91. Kubernetes High Availability: Concepts and Best Practices
92. How to Use Kubernetes for Multi-Tier Web Applications
93. Managing Kubernetes Resources with Resource Limits and Requests
94. Building a Kubernetes Dashboard with Custom Metrics
95. Using Kubernetes with Docker Compose for Local Development
96. Setting Up Kubernetes for Machine Learning Workloads
97. How to Implement Kubernetes Secrets Management with HashiCorp Vault
98. How to Monitor Kubernetes Clusters with Datadog or Prometheus
99. Securing Kubernetes with Mutual TLS and Service Mesh
100. Advanced Kubernetes Security Practices for Multi-Tenant Environments