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Master Kubernetes 1.31 from Control Plane Reconciliation, etcd Raft Quorum & Multi-Container Pods to Rolling Deployments, Zero-Trust NetworkPolicies, CSI Dynamic PVCs, StatefulSets, Least-Privilege RBAC, HPA v2, Helm 3 & ArgoCD GitOps — with 200 Mock Tests & Voice Gemini AI Interviews at ₹6,499.
KUBE • Cloud-Native Container Orchestration (CKA / CKAD)
Duration & Live Format: 15–17 Weeks • Live + Hands-On Projects (212h • Weekday & Weekend Batches)
🔒 100% Fixed Tuition: Zero bargaining or quality compromise on Senior Architect trainers & live labs.
✓ 16 Module Repos + Live Client Prod Project + Barcode Certificate & Official Internship Letter
Our counselor shares live demo link & issues your Student Portal ID to track every topic transparently.
When a company runs hundreds of Docker containers across dozens of cloud servers, Kubernetes (K8s) acts as the “autopilot captain” — automatically restarting crashed apps, scaling during festive sales, and deploying updates with zero downtime.
Taught visually with clear architecture diagrams and hands-on `kubectl` labs so Ops/Cloud learners and backend engineers master production orchestration step by step.
Core Industry Skill Pillars You Will Master (In Bullet Form + Exact Purpose):
For What Purpose: Understand exactly how Kubernetes schedules, heals, and reconciles desired cluster state.
For What Purpose: Ship stateless APIs and stateful databases with zero-downtime rollouts and instant rollbacks.
For What Purpose: Route external HTTPS traffic cleanly while enforcing zero-trust pod-to-pod firewalls.
For What Purpose: Inject environment configs securely and attach durable cloud disks to stateful workloads.
For What Purpose: Auto-scale pods on CPU/custom metrics and survive node maintenance without dropping traffic.
For What Purpose: Standardize multi-environment releases and diagnose `CrashLoopBackOff` incidents in minutes.
For What Purpose: `kubeconform` manifest validation • `helm lint` • Trivy image/cluster security scanning • Chaos pod-eviction tests — ensuring your code never breaks silently when real users hit edge cases.
For What Purpose: Verify every single Kubernetes topic through timed MCQ exams and spoken technical interviews before deploying your Live Client Project.
Live trainer-guided cohorts with 100% transparent topic-by-topic tracking.
Kubernetes Training — Weekday Live Online Cohort (₹6,499)
Mon - Fri (Weekday Live Online) • 07:30 PM - 09:00 PM IST (1.5 Hrs/Day)
Starts: 10 Oct 2026
Kubernetes Training — Weekend Intensive Live Cohort (₹6,499)
Sat - Sun (Weekend Live Online) • 10:00 AM - 01:00 PM IST (3.0 Hrs/Day)
Starts: 05 Nov 2026
Every student who completes training modules and deploys the end-of-course Live Client Production Project receives both a scannable Barcode Certificate AND an Official Project Internship Confirmation Letter detailing exact engineering contributions.
Inspect Live Sample Certificate & Internship Letter →Only enrolled students can log in to inspect batch timing, assigned tasks, content covered & pending, unlimited Mock Tests & Voice AI Interviews, projects assigned to batch & status, batch group chat, and daily recorded class videos.
Control-plane & node internals (`kube-apiserver`, `etcd`, scheduler, `kubelet`, CNI/CSI) and declarative YAML/Helm design.
Production workload specs: Deployments, StatefulSets, DaemonSets, liveness/readiness/startup probes, and resource requests/limits.
Ingress TLS routing, Services, NetworkPolicies, Secrets/ConfigMaps, RBAC ServiceAccounts, and Pod Security Standards.
Autoscaling & HA: Horizontal Pod Autoscaler (HPA), PodDisruptionBudgets, topology spread constraints, and rolling rollbacks.
`kubeconform` manifest validation • `helm lint` • Trivy image/cluster security scanning • Chaos pod-eviction tests
Helm/GitOps releases, Prometheus/Grafana cluster observability, and live `kubectl` incident troubleshooting.
Running privileged root pods without CPU/memory limits or deploying `:latest` mutable tags without probes.
1.5 Hours / Day (60 Mins Live Architecture & Coding + 30 Mins Hands-On Lab)
Days: Monday to Friday (5 Live Interactive Classes / Week)
Slots: Morning: 08:00 AM – 09:30 AM IST • Evening: 07:30 PM – 09:00 PM IST
3.0 Hours / Day (2 Hrs Deep-Dive Live Coding + 1 Hr Guided Project Build)
Days: Saturday & Sunday (Designed for Working Professionals & College Students)
Slots: Morning: 10:00 AM – 01:00 PM IST • Afternoon: 03:00 PM – 06:00 PM IST
100% Live Interactive Format: Live instructor coding, screen-share debugging, daily lab assignments, and HD session recordings inside your Student Portal.
After completing 10+ progressive hands-on module projects during live classes, students join our mentor-led Client Production Sprint to build, test, and deploy "KubeScale Zero-Downtime Microservices Orchestration & GitOps Production Cluster" to live cloud production — earning an Official Verified Internship Letter detailing their exact technical contributions.
🚀 Real Client Production Sprint + Internship Letter
Client / Domain: Enterprise E-Commerce Cloud Platform Client • Production Infra: Kubernetes Cluster + NGINX Ingress TLS + HPA + StatefulSets + Helm
What Your Official Internship Confirmation Letter Certifies You Built & Deployed:
Click any module or topic below to expand its learning objectives, plain-language overview, real-world purpose, hands-on code, lab tasks, 50-MCQ mocks, Gemini AI interviews, and capstone project.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability teaches you how Kubernetes applications why container schedulers superseded Snowflake VMs, the Kubernetes API resource model (`apiVersion`, `kind`, `metadata`, `spec` vs cluster-managed `status`), self-healing control loops, bin-packing efficiency, and CNCF portability across EKS, GKE, AKS, and bare metal. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability in real Kubernetes systems — specifically: Why container schedulers superseded Snowflake VMs, the Kubernetes API resource model (`apiVersion`, `kind`, `metadata`, `spec` vs cluster-managed `status`), self-healing control loops, bin-packing efficiency, and CNCF portability across EKS, GKE, AKS, and bare metal, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Por...
Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability — Hands-On Lab Task
apiVersion: v1
kind: ConfigMap
metadata:
name: platform-runtime-cfg
namespace: default
labels:
app.kubernetes.io/part-of: mockattempt-cloud
data:
CLUSTER_TIER: "production"
RECONCILE_MODE: "declarative"💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` teaches you how Kubernetes applications end-to-end Pod creation flow: `kubectl` -> `kube-apiserver` (AuthN -> AuthZ -> Admission Webhooks -> `etcd` commit) -> `kube-scheduler` (Filtering & Scoring nodes -> Binding) -> target node `kubelet` -> `containerd` CRI + CNI IP allocation. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` in real Kubernetes systems — specifically: End-to-end Pod creation flow: `kubectl` -> `kube-apiserver` (AuthN -> AuthZ -> Admission Webhooks -> `etcd` commit) -> `kube-scheduler` (Filtering & Scoring nodes -> Binding) -> target node `kubelet` -> `containerd` CRI + CNI IP allocation, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet`...
Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` — Hands-On Lab Task
apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
cgroupDriver: systemd
maxPods: 110
serializeImagePulls: false
authentication:
anonymous:
enabled: false
evictionHard:
memory.available: "256Mi"Design a multi-AZ High-Availability Kubernetes Control Plane topology with stacked/external 3-node `etcd` Raft quorum, API server health probes, and CRI `containerd` worker node configuration.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, `kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` teaches you how Kubernetes applications kubeconfig contexts (`clusters`, `users`, `contexts`), why `kubectl apply` three-way merge / Server-Side Apply beats imperative `create`/`replace`, validating manifests via `--dry-run=server`, extracting fields with `-o jsonpath`, and debugging distroless Pods via `kubectl debug`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply `kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` in real Kubernetes systems — specifically: kubeconfig contexts (`clusters`, `users`, `contexts`), why `kubectl apply` three-way merge / Server-Side Apply beats imperative `create`/`replace`, validating manifests via `--dry-run=server`, extracting fields with `-o jsonpath`, and debugging distroless Pods via `kubectl debug`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
`kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating `kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug`...
`kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` — Hands-On Lab Task
apiVersion: v1
kind: Config
current-context: prod-eks-payments
clusters:
- name: eks-mum-01
cluster:
server: https://api.eks-mum-01.internal:6443
contexts:
- name: prod-eks-payments
context:
cluster: eks-mum-01
namespace: payments-prod
user: oidc-sre-admin💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes teaches you how Kubernetes applications why the Pod is the atomic unit of scheduling: the `pause` infra container holding the shared Pod IP/`localhost` port space, sequential `initContainers`, `startupProbe` vs `readinessProbe` vs `livenessProbe`, graceful `SIGTERM` (`preStop` + `terminationGracePeriodSeconds`), and OOMKiller QoS tiers. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes in real Kubernetes systems — specifically: Why the Pod is the atomic unit of scheduling: the `pause` infra container holding the shared Pod IP/`localhost` port space, sequential `initContainers`, `startupProbe` vs `readinessProbe` vs `livenessProbe`, graceful `SIGTERM` (`preStop` + `terminationGracePeriodSeconds`), and OOMKiller QoS tiers, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes using v1/...
Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes — Hands-On Lab Task
apiVersion: v1
kind: Pod
metadata:
name: billing-api-pod
spec:
securityContext:
runAsNonRoot: true
runAsUser: 10001
containers:
- name: api
image: registry.internal/billing:1.4.2
resources:
requests: { cpu: "500m", memory: "512Mi" }
limits: { cpu: "500m", memory: "512Mi" }
readinessProbe:
httpGet: { path: /readyz, port: 8080 }Create a production Pod specification running as non-root (`runAsNonRoot: true`, read-only root filesystem), gated by an Init container dependency check and separate Startup, Readiness, and Liveness probes.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection teaches you how Kubernetes applications how Kubernetes controllers and Services dynamically discover Pods solely via key-value `labels` (never hardcoded IPs!), standard `app.kubernetes.io/*` taxonomy, equality (`=`, `!=`) vs set-based (`in`, `notin`, `exists`) selectors, and why `annotations` hold non-queryable metadata. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection in real Kubernetes systems — specifically: How Kubernetes controllers and Services dynamically discover Pods solely via key-value `labels` (never hardcoded IPs!), standard `app.kubernetes.io/*` taxonomy, equality (`=`, `!=`) vs set-based (`in`, `notin`, `exists`) selectors, and why `annotations` hold non-queryable metadata, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection us...
Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection — Hands-On Lab Task
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: checkout-api-pdb
spec:
minAvailable: 2
selector:
matchLabels:
app.kubernetes.io/name: checkout-api
matchExpressions:
- key: tier
operator: In
values: ["backend", "edge"]💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` teaches you how Kubernetes applications virtual sub-clusters inside a physical cluster: namespaced (`Pod`, `Service`, `Secret`) vs cluster-scoped (`Node`, `PersistentVolume`, `StorageClass`, `ClusterRole`) objects, cross-namespace CoreDNS resolution (`<svc>.<ns>.svc.cluster.local`), and quota enforcement. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` in real Kubernetes systems — specifically: Virtual sub-clusters inside a physical cluster: namespaced (`Pod`, `Service`, `Secret`) vs cluster-scoped (`Node`, `PersistentVolume`, `StorageClass`, `ClusterRole`) objects, cross-namespace CoreDNS resolution (`<svc>.<ns>.svc.cluster.local`), and quota enforcement, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` using v1/Na...
Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` — Hands-On Lab Task
apiVersion: v1
kind: ResourceQuota
metadata:
name: payments-tenant-quota
namespace: payments-prod
spec:
hard:
requests.cpu: "8"
requests.memory: "16Gi"
limits.cpu: "16"
limits.memory: "32Gi"
pods: "40"Provision a governed `checkout-prod` tenant namespace that enforces strict aggregate CPU/Memory/PVC ceilings while injecting sane default container requests/limits automatically.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging teaches you how Kubernetes applications how the `ReplicaSet` controller continuously compares matching active Pods against `spec.replicas`, `metadata.ownerReferences` garbage collection (`Cascade=background/foreground/orphan`), and isolating a misbehaving Pod live in production by removing its selector label. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging in real Kubernetes systems — specifically: How the `ReplicaSet` controller continuously compares matching active Pods against `spec.replicas`, `metadata.ownerReferences` garbage collection (`Cascade=background/foreground/orphan`), and isolating a misbehaving Pod live in production by removing its selector label, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debuggi...
Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging — Hands-On Lab Task
apiVersion: apps/v1
kind: ReplicaSet
metadata:
name: worker-pool-rs
spec:
replicas: 3
selector:
matchLabels: { app: async-worker }
template:
metadata:
labels: { app: async-worker }
spec:
containers:
- name: worker
image: registry.internal/worker:2.1.0💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` teaches you how Kubernetes applications how `Deployment` orchestrates old and new `ReplicaSet`s during `RollingUpdate` vs `Recreate`, `maxSurge` & `maxUnavailable` math, `minReadySeconds`, `progressDeadlineSeconds`, `revisionHistoryLimit`, and instant rollback when a bad build fails readiness checks. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` in real Kubernetes systems — specifically: How `Deployment` orchestrates old and new `ReplicaSet`s during `RollingUpdate` vs `Recreate`, `maxSurge` & `maxUnavailable` math, `minReadySeconds`, `progressDeadlineSeconds`, `revisionHistoryLimit`, and instant rollback when a bad build fails readiness checks, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout und...
Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` — Hands-On Lab Task
apiVersion: apps/v1
kind: Deployment
metadata: { name: catalog-api }
spec:
replicas: 4
revisionHistoryLimit: 5
strategy:
type: RollingUpdate
rollingUpdate: { maxUnavailable: 0, maxSurge: 1 }
selector: { matchLabels: { app: catalog-api } }
template:
metadata: { labels: { app: catalog-api } }
spec:
containers:
- { name: api, image: "registry.internal/catalog:3.0.4", ports: [{ containerPort: 8080 }] }Author a 4-replica stateless `Deployment` manifest engineered to survive both rolling image upgrades and voluntary node drains with **zero** HTTP `502/504` connection resets.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s teaches you how Kubernetes applications why ephemeral Pod IPs necessitate stable virtual Service IPs (`ClusterIP`), mapping `port` -> named `targetPort`, `EndpointSlice` scalability (100 endpoints per slice vs monolithic `Endpoints`), Headless `clusterIP: None` returning direct Pod A/AAAA records, and Ingress / Gateway API L7 routing. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s in real Kubernetes systems — specifically: Why ephemeral Pod IPs necessitate stable virtual Service IPs (`ClusterIP`), mapping `port` -> named `targetPort`, `EndpointSlice` scalability (100 endpoints per slice vs monolithic `Endpoints`), Headless `clusterIP: None` returning direct Pod A/AAAA records, and Ingress / Gateway API L7 routing, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s usi...
Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s — Hands-On Lab Task
apiVersion: v1
kind: Service
metadata:
name: payment-svc
namespace: payments-prod
spec:
type: ClusterIP
selector:
app.kubernetes.io/name: payment-engine
ports:
- name: https
protocol: TCP
port: 443
targetPort: https-api💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules teaches you how Kubernetes applications how Kubernetes defaults to a flat allow-all network, why `NetworkPolicy` requires a policy-capable CNI (Calico, Cilium, Weave — NOT basic `flannel` alone!), additive union semantics of multiple policies, `namespaceSelector` + `podSelector` AND vs OR YAML list traps, and CoreDNS egress rules. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules in real Kubernetes systems — specifically: How Kubernetes defaults to a flat allow-all network, why `NetworkPolicy` requires a policy-capable CNI (Calico, Cilium, Weave — NOT basic `flannel` alone!), additive union semantics of multiple policies, `namespaceSelector` + `podSelector` AND vs OR YAML list traps, and CoreDNS egress rules, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules using netwo...
Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules — Hands-On Lab Task
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-gateway-and-dns
namespace: payments-prod
spec:
podSelector:
matchLabels: { app: payment-engine }
policyTypes: [Ingress, Egress]
ingress:
- from: [{ podSelector: { matchLabels: { app: api-gateway } } }]
ports: [{ protocol: TCP, port: 8443 }]
egress:
- ports: [{ protocol: UDP, port: 53 }, { protocol: TCP, port: 53 }]Secure a `payments-prod` namespace with default-deny ingress/egress posture, allowing only `api-gateway` Pods to call `payment-svc:8443` and allowing `payment-svc` egress strictly to `postgres:5432` and CoreDNS `53`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats teaches you how Kubernetes applications why writable container overlay layers vanish on container restart whereas Pod `emptyDir` survives container crashes for the lifetime of the Pod, RAM-backed `emptyDir.medium: Memory`, `projected` volumes combining `ConfigMap` + `Secret` + `serviceAccountToken`, and why `hostPath` is banned in restricted security policies. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats in real Kubernetes systems — specifically: Why writable container overlay layers vanish on container restart whereas Pod `emptyDir` survives container crashes for the lifetime of the Pod, RAM-backed `emptyDir.medium: Memory`, `projected` volumes combining `ConfigMap` + `Secret` + `serviceAccountToken`, and why `hostPath` is banned in restricted security policies, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats u...
Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats — Hands-On Lab Task
apiVersion: v1
kind: Pod
metadata:
name: pdf-renderer
spec:
containers:
- name: renderer
image: registry.internal/renderer:1.2.0
securityContext: { readOnlyRootFilesystem: true }
volumeMounts:
- { name: scratch-tmp, mountPath: /tmp }
volumes:
- name: scratch-tmp
emptyDir: { sizeLimit: "256Mi" }💡 Self-Explanatory Overview: In simple, everyday terms, Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` teaches you how Kubernetes applications decoupling developer storage requests (`PersistentVolumeClaim`) from cluster infrastructure (`PersistentVolume` + CSI `StorageClass`), block `ReadWriteOnce` (`RWO`) vs shared NFS/EFS `ReadWriteMany` (`RWX`), `reclaimPolicy: Retain` vs `Delete`, and `WaitForFirstConsumer` multi-AZ scheduling. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` in real Kubernetes systems — specifically: Decoupling developer storage requests (`PersistentVolumeClaim`) from cluster infrastructure (`PersistentVolume` + CSI `StorageClass`), block `ReadWriteOnce` (`RWO`) vs shared NFS/EFS `ReadWriteMany` (`RWX`), `reclaimPolicy: Retain` vs `Delete`, and `WaitForFirstConsumer` multi-AZ scheduling, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirs...
Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` — Hands-On Lab Task
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: prod-fast-ssd
provisioner: ebs.csi.aws.com
reclaimPolicy: Retain
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer
parameters:
type: gp3
encrypted: "true"Configure a production gp3/SSD `StorageClass` with `WaitForFirstConsumer` topology binding, online volume expansion (`allowVolumeExpansion: true`), and `Retain` reclaim protection for stateful workloads.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` teaches you how Kubernetes applications why clustered databases/brokers cannot share a single `Deployment` PVC, how `StatefulSet` guarantees stable ordinal Pod names (`kafka-0`, `kafka-1`), predictable peer DNS (`kafka-0.kafka-hl.ns.svc.cluster.local`), dedicated per-ordinal PVCs via `volumeClaimTemplates`, and `OrderedReady` vs `Parallel` lifecycle. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` in real Kubernetes systems — specifically: Why clustered databases/brokers cannot share a single `Deployment` PVC, how `StatefulSet` guarantees stable ordinal Pod names (`kafka-0`, `kafka-1`), predictable peer DNS (`kafka-0.kafka-hl.ns.svc.cluster.local`), dedicated per-ordinal PVCs via `volumeClaimTemplates`, and `OrderedReady` vs `Parallel` lifecycle, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates...
Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` — Hands-On Lab Task
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: redis-cluster
spec:
serviceName: redis-headless
replicas: 3
selector: { matchLabels: { app: redis-cluster } }
template:
metadata: { labels: { app: redis-cluster } }
spec:
containers:
- { name: redis, image: "redis:7.4-alpine", volumeMounts: [{ name: data, mountPath: /data }] }
volumeClaimTemplates:
- metadata: { name: data }
spec: { accessModes: ["ReadWriteOnce"], resources: { requests: { storage: "20Gi" } } }💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync teaches you how Kubernetes applications why standard `Secret` `data` is merely Base64-encoded (NOT encrypted by default in `etcd`!), enabling `EncryptionConfiguration` with cloud KMS v2 providers, `Opaque` vs `kubernetes.io/tls` vs `dockerconfigjson` types, setting `immutable: true` to cut kubelet watch load, and External Secrets Operator. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync in real Kubernetes systems — specifically: Why standard `Secret` `data` is merely Base64-encoded (NOT encrypted by default in `etcd`!), enabling `EncryptionConfiguration` with cloud KMS v2 providers, `Opaque` vs `kubernetes.io/tls` vs `dockerconfigjson` types, setting `immutable: true` to cut kubelet watch load, and External Secrets Operator, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI...
Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync — Hands-On Lab Task
apiVersion: v1
kind: Secret
metadata:
name: db-app-credentials-v2
namespace: payments-prod
type: Opaque
immutable: true
stringData:
DB_HOST: "pg-primary.payments-prod.svc.cluster.local"
DB_USER: "payments_rw"
DB_SSL_MODE: "verify-full"Deploy a 3-replica clustered data store `StatefulSet` with deterministic DNS peers (`store-0.store-headless`), dedicated per-ordinal SSD PVCs, and an immutable TLS Secret mounted read-only.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup teaches you how Kubernetes applications running finite run-to-completion tasks (`restartPolicy: OnFailure | Never`), work-queue vs `completionMode: Indexed` parallel Jobs, bounding retries (`backoffLimit`, `activeDeadlineSeconds`), auto-pruning finished Pods (`ttlSecondsAfterFinished`), and `CronJob` `Allow` vs `Forbid` vs `Replace` overlap rules. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup in real Kubernetes systems — specifically: Running finite run-to-completion tasks (`restartPolicy: OnFailure | Never`), work-queue vs `completionMode: Indexed` parallel Jobs, bounding retries (`backoffLimit`, `activeDeadlineSeconds`), auto-pruning finished Pods (`ttlSecondsAfterFinished`), and `CronJob` `Allow` vs `Forbid` vs `Replace` overlap rules, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup using...
Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup — Hands-On Lab Task
apiVersion: batch/v1
kind: CronJob
metadata:
name: nightly-invoice-reconcile
spec:
schedule: "30 2 * * *"
timeZone: "Asia/Kolkata"
concurrencyPolicy: Forbid
jobTemplate:
spec:
ttlSecondsAfterFinished: 3600
backoffLimit: 3
template:
spec:
restartPolicy: OnFailure
containers: [{ name: reconcile, image: "registry.internal/reconcile:1.8.0" }]💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` teaches you how Kubernetes applications additive allow-only authorization: Subjects (`User`, `Group`, `ServiceAccount`), Verbs (`get`, `list`, `watch`, `create`, `patch`, `delete`), reusing a `ClusterRole` inside a single namespace via a `RoleBinding`, time-bound projected `TokenRequest` JWTs, and verifying access with `kubectl auth can-i --as`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` in real Kubernetes systems — specifically: Additive allow-only authorization: Subjects (`User`, `Group`, `ServiceAccount`), Verbs (`get`, `list`, `watch`, `create`, `patch`, `delete`), reusing a `ClusterRole` inside a single namespace via a `RoleBinding`, time-bound projected `TokenRequest` JWTs, and verifying access with `kubectl auth can-i --as`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth c...
Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` — Hands-On Lab Task
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: config-reader-role
namespace: payments-prod
rules:
- apiGroups: [""]
resources: ["configmaps"]
resourceNames: ["platform-runtime-cfg"]
verbs: ["get", "watch"]Create a timezone-explicit nightly settlement `CronJob` preventing overlapping runs (`concurrencyPolicy: Forbid`) paired with a dedicated, least-privilege `ServiceAccount` audited via `kubectl auth can-i`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling teaches you how Kubernetes applications the 3 autoscaling dimensions: Horizontal Pod Autoscaler (`HPA v2`: `desiredReplicas = ceil[currentReplicas * (currentMetric / desiredMetric)]`), Event-Driven `KEDA` scaling to/from zero on Kafka/SQS lag, Vertical Pod Autoscaler (`VPA`), and Cluster Autoscaler / Karpenter provisioning EC2/cloud nodes for `Pending` Pods. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling in real Kubernetes systems — specifically: The 3 autoscaling dimensions: Horizontal Pod Autoscaler (`HPA v2`: `desiredReplicas = ceil[currentReplicas * (currentMetric / desiredMetric)]`), Event-Driven `KEDA` scaling to/from zero on Kafka/SQS lag, Vertical Pod Autoscaler (`VPA`), and Cluster Autoscaler / Karpenter provisioning EC2/cloud nodes for `Pending` Pods, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling us...
Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling — Hands-On Lab Task
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: catalog-api-hpa
spec:
scaleTargetRef: { apiVersion: apps/v1, kind: Deployment, name: catalog-api }
minReplicas: 4
maxReplicas: 24
metrics:
- type: Resource
resource: { name: cpu, target: { type: Utilization, averageUtilization: 70 } }
behavior:
scaleDown:
stabilizationWindowSeconds: 300💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks teaches you how Kubernetes applications why Helm 3 eliminated Tiller (pure client-side library obeying user RBAC!), Chart structure (`Chart.yaml`, `values.yaml`, `templates/`, `_helpers.tpl`), `sha256sum` ConfigMap checksum annotations to trigger Deployment rollouts automatically, OCI chart registries, and `helm upgrade --install --atomic`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks in real Kubernetes systems — specifically: Why Helm 3 eliminated Tiller (pure client-side library obeying user RBAC!), Chart structure (`Chart.yaml`, `values.yaml`, `templates/`, `_helpers.tpl`), `sha256sum` ConfigMap checksum annotations to trigger Deployment rollouts automatically, OCI chart registries, and `helm upgrade --install --atomic`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks us...
Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks — Hands-On Lab Task
apiVersion: v2
name: checkout-service
description: Production Helm 3 chart for MockAttempt Checkout Microservice
type: application
version: 2.4.0
appVersion: "1.18.3"
kubeVersion: ">=1.29.0-0"
maintainers:
- name: Platform Engineering
email: platform@mockattempt.comPackage a production microservice as a reusable Helm 3 chart with `prod-values.yaml`, PodDisruptionBudget, and an `autoscaling/v2` `HPA` tuned for rapid burst scale-out and anti-flapping gradual scale-in.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector teaches you how Kubernetes applications the cloud-native observability triad: `cAdvisor` (embedded in `kubelet` for container CPU throttling `container_cpu_cfs_throttled_periods_total` & RSS memory), `kube-state-metrics` (API object state: `CrashLoopBackOff`, `Pending` PVCs, `Deployment` replica mismatches), DaemonSet log shipping, and OpenTelemetry Collector. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector in real Kubernetes systems — specifically: The cloud-native observability triad: `cAdvisor` (embedded in `kubelet` for container CPU throttling `container_cpu_cfs_throttled_periods_total` & RSS memory), `kube-state-metrics` (API object state: `CrashLoopBackOff`, `Pending` PVCs, `Deployment` replica mismatches), DaemonSet log shipping, and OpenTelemetry Collector, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Col...
Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector — Hands-On Lab Task
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: payment-svc-monitor
namespace: payments-prod
spec:
selector:
matchLabels:
app.kubernetes.io/name: payment-engine
endpoints:
- port: https
path: /metrics
interval: 15s💡 Self-Explanatory Overview: In simple, everyday terms, Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery teaches you how Kubernetes applications separating CI (test, Trivy scan, Cosign sign, push immutable image digest, open PR updating GitOps manifest repo) from pull-based CD (`ArgoCD` running inside the cluster reconciling Git -> cluster with `selfHeal` & `prune`), plus Argo Rollouts Canary / Blue-Green progressive delivery. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery in real Kubernetes systems — specifically: Separating CI (test, Trivy scan, Cosign sign, push immutable image digest, open PR updating GitOps manifest repo) from pull-based CD (`ArgoCD` running inside the cluster reconciling Git -> cluster with `selfHeal` & `prune`), plus Argo Rollouts Canary / Blue-Green progressive delivery, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Tutorial Kubernetes - Home Kubernetes - Overview Kubernetes - Architecture Kubernetes - Setup Kubernetes - Setup on Ubuntu Kubernetes - Images Kubernetes - Jobs Kubernetes - Labels & Selectors Kubernetes - Namespace Kubernetes - Node Kubernetes - Service Kubernetes - POD Kubernetes - Replication Controller Kubernetes - Replica Sets Kubernetes - Deployments Kubernetes - Volumes Kubernetes - Secrets Kube...
Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery — Practical Assignment
Implement a production-ready Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm solution demonstrating Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Del...
Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery — Hands-On Lab Task
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: payments-prod-gitops
namespace: argocd
spec:
project: production
source:
repoURL: https://git.internal/platform/k8s-gitops.git
targetRevision: main
path: overlays/mum-prod/payments
destination:
server: https://kubernetes.default.svc
namespace: payments-prod
syncPolicy:
automated: { prune: true, selfHeal: true }Define a Prometheus Operator `ServiceMonitor` scraping `/metrics` plus an ArgoCD `Application` manifest enforcing automated Git-to-cluster drift remediation (`selfHeal: true`).
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) teaches you how Kubernetes applications route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) in real Kubernetes systems — specifically: Route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS, so your code stays reliable, fast, and easy to maintain in production.
What Is Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) & Why Industry Teams Use ItIn Kubernetes Training, Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) exists for one clear real-world purpose: Route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS. Even if you come from a non-technical background, we start with the everyday real-life pr...
Hands-On Implementation: Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute)
Build and verify a clean working module demonstrating Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) (Route multi-domain HTTPS traffic with canary header/weight splitting and cert-man...
Live Lab Sprint: Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute)
# Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute)
# Purpose: Route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) teaches you how Kubernetes applications ensure a compromised frontend pod can never connect directly to database pods. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) in real Kubernetes systems — specifically: Ensure a compromised frontend pod can never connect directly to database pods, so your code stays reliable, fast, and easy to maintain in production.
What Is Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) & Why Industry Teams Use ItIn Kubernetes Training, Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) exists for one clear real-world purpose: Ensure a compromised frontend pod can never connect directly to database pods. Even if you come from a non-technical background, we start with the everyday real-life problem first before writi...
Hands-On Implementation: Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation)
Build and verify a clean working module demonstrating Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) (Ensure a compromised frontend pod can never connect directly to database pods.). Ha...
Live Lab Sprint: Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation)
# Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation)
# Purpose: Ensure a compromised frontend pod can never connect directly to database pods.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, CoreDNS Service Discovery, Headless Services & Topology-Aware Routing teaches you how Kubernetes applications resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply CoreDNS Service Discovery, Headless Services & Topology-Aware Routing in real Kubernetes systems — specifically: Resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ, so your code stays reliable, fast, and easy to maintain in production.
What Is CoreDNS Service Discovery, Headless Services & Topology-Aware Routing & Why Industry Teams Use ItIn Kubernetes Training, CoreDNS Service Discovery, Headless Services & Topology-Aware Routing exists for one clear real-world purpose: Resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ. Even if you come from a non-technical background, we start with the everyday real-li...
Hands-On Implementation: CoreDNS Service Discovery, Headless Services & Topology-Aware Routing
Build and verify a clean working module demonstrating CoreDNS Service Discovery, Headless Services & Topology-Aware Routing (Resolve internal cluster DNS cleanly and keep pod traffic inside the same c...
Live Lab Sprint: CoreDNS Service Discovery, Headless Services & Topology-Aware Routing
# CoreDNS Service Discovery, Headless Services & Topology-Aware Routing
# Purpose: Resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices teaches you how Kubernetes applications diagnose 502/504 cluster networking mysteries rapidly with kubectl debug. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices in real Kubernetes systems — specifically: Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug, so your code stays reliable, fast, and easy to maintain in production.
What Is Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices & Why Industry Teams Use ItIn Kubernetes Training, Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices exists for one clear real-world purpose: Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug. Even if you come from a non-technical background, we start with the everyday real-life problem f...
Hands-On Implementation: Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices
Build and verify a clean working module demonstrating Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices (Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug.)....
Live Lab Sprint: Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices
# Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices
# Purpose: Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3Design, implement, test, and document 'Multi-Namespace TLS-Routed & Network-Isolated K8s Mesh' applying every production concept from Module 11 (Gateway API / Ingress TLS, Zero-Trust NetworkPolicies & CoreDNS) of Kubernetes Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens teaches you how Kubernetes applications enforce strict per-team and per-microservice API server permissions. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens in real Kubernetes systems — specifically: Enforce strict per-team and per-microservice API server permissions, so your code stays reliable, fast, and easy to maintain in production.
What Is Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens & Why Industry Teams Use ItIn Kubernetes Training, Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens exists for one clear real-world purpose: Enforce strict per-team and per-microservice API server permissions. Even if you come from a non-technical background, we start with the everyday real-life problem first...
Hands-On Implementation: Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens
Build and verify a clean working module demonstrating Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens (Enforce strict per-team and per-microservice API server permissions.). Handl...
Live Lab Sprint: Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens
# Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens
# Purpose: Enforce strict per-team and per-microservice API server permissions.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS teaches you how Kubernetes applications block privileged containers, root UIDs, and Linux capability escalations cluster-wide. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS in real Kubernetes systems — specifically: Block privileged containers, root UIDs, and Linux capability escalations cluster-wide, so your code stays reliable, fast, and easy to maintain in production.
What Is Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS & Why Industry Teams Use ItIn Kubernetes Training, Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS exists for one clear real-world purpose: Block privileged containers, root UIDs, and Linux capability escalations cluster-wide. Even if you come from a non-technical background, we start with t...
Hands-On Implementation: Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS
Build and verify a clean working module demonstrating Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS (Block privileged containers, root UIDs, and Linux capability escala...
Live Lab Sprint: Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS
# Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS
# Purpose: Block privileged containers, root UIDs, and Linux capability escalations cluster-wide.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption teaches you how Kubernetes applications sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption in real Kubernetes systems — specifically: Sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git, so your code stays reliable, fast, and easy to maintain in production.
What Is External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption & Why Industry Teams Use ItIn Kubernetes Training, External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption exists for one clear real-world purpose: Sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git. Even if you come from a non-technical background, we start with the e...
Hands-On Implementation: External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption
Build and verify a clean working module demonstrating External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption (Sync rotated cloud secrets into pods securely without committing base64...
Live Lab Sprint: External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption
# External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption
# Purpose: Sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, IRSA / Workload Identity Federation for Cloud SDK Access teaches you how Kubernetes applications grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply IRSA / Workload Identity Federation for Cloud SDK Access in real Kubernetes systems — specifically: Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys, so your code stays reliable, fast, and easy to maintain in production.
What Is IRSA / Workload Identity Federation for Cloud SDK Access & Why Industry Teams Use ItIn Kubernetes Training, IRSA / Workload Identity Federation for Cloud SDK Access exists for one clear real-world purpose: Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys. Even if you come from a non-technical background, we start with the everyday real-life problem first before writing pro...
Hands-On Implementation: IRSA / Workload Identity Federation for Cloud SDK Access
Build and verify a clean working module demonstrating IRSA / Workload Identity Federation for Cloud SDK Access (Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys.). Han...
Live Lab Sprint: IRSA / Workload Identity Federation for Cloud SDK Access
# IRSA / Workload Identity Federation for Cloud SDK Access
# Purpose: Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3Design, implement, test, and document 'Hardened CIS-Compliant Production Kubernetes Cluster' applying every production concept from Module 12 (RBAC Least Privilege, Pod Security Standards, External Secrets & Workload IAM) of Kubernetes Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Authoring Reusable Helm Charts, Values Schema Validation & Hooks teaches you how Kubernetes applications template standardized microservice deployments across 20+ engineering squads. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Authoring Reusable Helm Charts, Values Schema Validation & Hooks in real Kubernetes systems — specifically: Template standardized microservice deployments across 20+ engineering squads, so your code stays reliable, fast, and easy to maintain in production.
What Is Authoring Reusable Helm Charts, Values Schema Validation & Hooks & Why Industry Teams Use ItIn Kubernetes Training, Authoring Reusable Helm Charts, Values Schema Validation & Hooks exists for one clear real-world purpose: Template standardized microservice deployments across 20+ engineering squads. Even if you come from a non-technical background, we start with the everyday real-life problem first...
Hands-On Implementation: Authoring Reusable Helm Charts, Values Schema Validation & Hooks
Build and verify a clean working module demonstrating Authoring Reusable Helm Charts, Values Schema Validation & Hooks (Template standardized microservice deployments across 20+ engineering squads.)....
Live Lab Sprint: Authoring Reusable Helm Charts, Values Schema Validation & Hooks
# Authoring Reusable Helm Charts, Values Schema Validation & Hooks
# Purpose: Template standardized microservice deployments across 20+ engineering squads.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Kustomize Base + Dev/Stage/Prod Patch Overlays teaches you how Kubernetes applications manage environment replica/resource differences cleanly without copy-pasting YAML. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Kustomize Base + Dev/Stage/Prod Patch Overlays in real Kubernetes systems — specifically: Manage environment replica/resource differences cleanly without copy-pasting YAML, so your code stays reliable, fast, and easy to maintain in production.
What Is Kustomize Base + Dev/Stage/Prod Patch Overlays & Why Industry Teams Use ItIn Kubernetes Training, Kustomize Base + Dev/Stage/Prod Patch Overlays exists for one clear real-world purpose: Manage environment replica/resource differences cleanly without copy-pasting YAML. Even if you come from a non-technical background, we start with the everyday real-life problem first before writing production syntax.Sub-T...
Hands-On Implementation: Kustomize Base + Dev/Stage/Prod Patch Overlays
Build and verify a clean working module demonstrating Kustomize Base + Dev/Stage/Prod Patch Overlays (Manage environment replica/resource differences cleanly without copy-pasting YAML.). Handle invali...
Live Lab Sprint: Kustomize Base + Dev/Stage/Prod Patch Overlays
# Kustomize Base + Dev/Stage/Prod Patch Overlays
# Purpose: Manage environment replica/resource differences cleanly without copy-pasting YAML.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD teaches you how Kubernetes applications make Git the single auditable source of truth with 1-click rollbacks. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD in real Kubernetes systems — specifically: Make Git the single auditable source of truth with 1-click rollbacks, so your code stays reliable, fast, and easy to maintain in production.
What Is Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD & Why Industry Teams Use ItIn Kubernetes Training, Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD exists for one clear real-world purpose: Make Git the single auditable source of truth with 1-click rollbacks. Even if you come from a non-technical background, we start with the everyday real-life problem first b...
Hands-On Implementation: Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD
Build and verify a clean working module demonstrating Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD (Make Git the single auditable source of truth with 1-click rollbacks.). Handl...
Live Lab Sprint: Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD
# Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD
# Purpose: Make Git the single auditable source of truth with 1-click rollbacks.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) teaches you how Kubernetes applications auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) in real Kubernetes systems — specifically: Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike, so your code stays reliable, fast, and easy to maintain in production.
What Is Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) & Why Industry Teams Use ItIn Kubernetes Training, Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) exists for one clear real-world purpose: Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike. Even if you come from a non-technical background, we start with the everyday real-life pro...
Hands-On Implementation: Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts)
Build and verify a clean working module demonstrating Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) (Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike...
Live Lab Sprint: Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts)
# Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts)
# Purpose: Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3Design, implement, test, and document 'GitOps Multi-Environment Delivery Platform with ArgoCD' applying every production concept from Module 13 (Helm Charts, Kustomize Overlays, ArgoCD GitOps & Progressive Rollouts) of Kubernetes Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention teaches you how Kubernetes applications right-size pod memory/CPU scientifically and prevent OOMKilled evictions. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention in real Kubernetes systems — specifically: Right-size pod memory/CPU scientifically and prevent OOMKilled evictions, so your code stays reliable, fast, and easy to maintain in production.
What Is Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention & Why Industry Teams Use ItIn Kubernetes Training, Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention exists for one clear real-world purpose: Right-size pod memory/CPU scientifically and prevent OOMKilled evictions. Even if you come from a non-technical background, we start with the everyday real-life problem first befor...
Hands-On Implementation: Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention
Build and verify a clean working module demonstrating Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention (Right-size pod memory/CPU scientifically and prevent OOMKilled evictions.). Handl...
Live Lab Sprint: Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention
# Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention
# Purpose: Right-size pod memory/CPU scientifically and prevent OOMKilled evictions.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter teaches you how Kubernetes applications scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter in real Kubernetes systems — specifically: Scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s, so your code stays reliable, fast, and easy to maintain in production.
What Is HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter & Why Industry Teams Use ItIn Kubernetes Training, HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter exists for one clear real-world purpose: Scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s. Even if you come from a non-technical background, we start with the everyday real-...
Hands-On Implementation: HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter
Build and verify a clean working module demonstrating HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter (Scale pods on real queue backlog and provision right-sized spot/on-demand no...
Live Lab Sprint: HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter
# HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter
# Purpose: Scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks teaches you how Kubernetes applications survive AZ outages and node upgrades with zero user-visible downtime. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks in real Kubernetes systems — specifically: Survive AZ outages and node upgrades with zero user-visible downtime, so your code stays reliable, fast, and easy to maintain in production.
What Is PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks & Why Industry Teams Use ItIn Kubernetes Training, PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks exists for one clear real-world purpose: Survive AZ outages and node upgrades with zero user-visible downtime. Even if you come from a non-technical background, we start with the everyday real-life probl...
Hands-On Implementation: PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks
Build and verify a clean working module demonstrating PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks (Survive AZ outages and node upgrades with zero user-visible downtime.)....
Live Lab Sprint: PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks
# PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks
# Purpose: Survive AZ outages and node upgrades with zero user-visible downtime.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3💡 Self-Explanatory Overview: In simple, everyday terms, CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting teaches you how Kubernetes applications back up stateful volumes/cluster manifests and monitor golden SRE signals. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting in real Kubernetes systems — specifically: Back up stateful volumes/cluster manifests and monitor golden SRE signals, so your code stays reliable, fast, and easy to maintain in production.
What Is CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting & Why Industry Teams Use ItIn Kubernetes Training, CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting exists for one clear real-world purpose: Back up stateful volumes/cluster manifests and monitor golden SRE signals. Even if you come from a non-technical background, we start with the everyday real-l...
Hands-On Implementation: CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting
Build and verify a clean working module demonstrating CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting (Back up stateful volumes/cluster manifests and monitor golden SRE sign...
Live Lab Sprint: CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting
# CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting
# Purpose: Back up stateful volumes/cluster manifests and monitor golden SRE signals.
apiVersion: apps/v1
kind: Deployment
metadata:
name: hardened-service
spec:
replicas: 3Design, implement, test, and document 'Auto-Scaling High-Availability Production Cluster Stack' applying every production concept from Module 14 (HPA/KEDA Autoscaling, Karpenter Nodes, PDBs, Stateful CSI & Prometheus) of Kubernetes Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems teaches you how Kubernetes applications block container privilege escalation and enforce least-privilege RBAC ServiceAccounts. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems in real Kubernetes systems — specifically: Block container privilege escalation and enforce least-privilege RBAC ServiceAccounts, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems (Plain-English & Industry Purpose)In simple, everyday terms, Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems teaches you how Kubernetes applications block container privilege escalation and enforce least-privilege RBAC ServiceAccounts. Even if you...
Practical Implementation: Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems
Implement and verify Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems (Block container privilege escalation and enforce least-privilege RBAC ServiceAccounts)...
Guided Live Lab Checklist: Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems
# Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems
# Purpose: Block container privilege escalation and enforce least-privilege RBAC ServiceAccounts
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0💡 Self-Explanatory Overview: In simple, everyday terms, Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync teaches you how Kubernetes applications isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync in real Kubernetes systems — specifically: Isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync (Plain-English & Industry Purpose)In simple, everyday terms, Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync teaches you how Kubernetes applications isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML. Even if you have never seen this con...
Practical Implementation: Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync
Implement and verify Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync (Isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML) with explic...
Guided Live Lab Checklist: Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync
# Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync
# Purpose: Isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0💡 Self-Explanatory Overview: In simple, everyday terms, Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets teaches you how Kubernetes applications scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets in real Kubernetes systems — specifically: Scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets (Plain-English & Industry Purpose)In simple, everyday terms, Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets teaches you how Kubernetes applications scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains. Even if you have never seen...
Practical Implementation: Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets
Implement and verify Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets (Scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains) wit...
Guided Live Lab Checklist: Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets
# Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets
# Purpose: Scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0Architect, implement, test, and document 'Hardened Non-Root Namespace with NetworkPolicies, PSS Restricted & HPA/PDB' applying every bullet-wise engineering concept from Module 15 (Kubernetes Zero-Trust Security, Pod Security Standards & Autoscaling (HPA/KEDA)) of Kubernetes Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts teaches you how Kubernetes applications promote audited cluster changes automatically from Git with instant 1-click rollback. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts in real Kubernetes systems — specifically: Promote audited cluster changes automatically from Git with instant 1-click rollback, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts (Plain-English & Industry Purpose)In simple, everyday terms, GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts teaches you how Kubernetes applications promote audited cluster changes automatically from Git with instant 1-click rollback. Even if you have never seen this co...
Practical Implementation: GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts
Implement and verify GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts (Promote audited cluster changes automatically from Git with instant 1-click rollback) with explic...
Guided Live Lab Checklist: GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts
# GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts
# Purpose: Promote audited cluster changes automatically from Git with instant 1-click rollback
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0💡 Self-Explanatory Overview: In simple, everyday terms, Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures teaches you how Kubernetes applications debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures in real Kubernetes systems — specifically: Debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures (Plain-English & Industry Purpose)In simple, everyday terms, Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures teaches you how Kubernetes applications debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events. Even if you have nev...
Practical Implementation: Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures
Implement and verify Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures (Debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events) with...
Guided Live Lab Checklist: Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures
# Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures
# Purpose: Debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0💡 Self-Explanatory Overview: In simple, everyday terms, Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews teaches you how Kubernetes applications architect highly available stateful and stateless clusters and pass hands-on K8s scenarios. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Kubernetes 1.31 (kubeadm / EKS / GKE), kubectl, Kustomize & YAML Specs, Calico / Cilium CNI NetworkPolicies code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews in real Kubernetes systems — specifically: Architect highly available stateful and stateless clusters and pass hands-on K8s scenarios, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews (Plain-English & Industry Purpose)In simple, everyday terms, Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews teaches you how Kubernetes applications architect highly available stateful and stateless clusters and pass hands-on K8s scenarios. Even if you have never seen this concept before...
Practical Implementation: Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews
Implement and verify Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews (Architect highly available stateful and stateless clusters and pass hands-on K8s scenarios) with explicit...
Guided Live Lab Checklist: Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews
# Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews
# Purpose: Architect highly available stateful and stateless clusters and pass hands-on K8s scenarios
apiVersion: apps/v1
kind: Deployment
metadata:
name: verified-production-service
spec:
replicas: 3
template:
spec:
securityContext:
runAsNonRoot: true
containers:
- name: app
image: registry.mockattempt.com/service:1.0.0Architect, implement, test, and document 'Declarative Helm + Argo CD Rollout Pipeline & Live Cluster Failure Runbook' applying every bullet-wise engineering concept from Module 16 (GitOps Continuous Delivery (Argo CD), Cluster Troubleshooting & CKA/CKAD Prep) of Kubernetes Training.
For What Purpose: To understand and apply Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability in real Kubernetes systems — specifically: Why container schedulers superseded Snowflake VMs, the Kubernetes API resource model (`apiVersion`, `kind`, `metadata`, `spec` vs cluster-managed `status`), self-healing control loops, bin-packing efficiency, and CNCF portability across EKS, GKE, AKS, and bare metal, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` in real Kubernetes systems — specifically: End-to-end Pod creation flow: `kubectl` -> `kube-apiserver` (AuthN -> AuthZ -> Admission Webhooks -> `etcd` commit) -> `kube-scheduler` (Filtering & Scoring nodes -> Binding) -> target node `kubelet` -> `containerd` CRI + CNI IP allocation, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Design a multi-AZ High-Availability Kubernetes Control Plane topology with stacked/external 3-node `etcd` Raft quorum, API server health probes, and CRI `containerd` worker node configuration.
For What Purpose: To understand and apply `kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` in real Kubernetes systems — specifically: kubeconfig contexts (`clusters`, `users`, `contexts`), why `kubectl apply` three-way merge / Server-Side Apply beats imperative `create`/`replace`, validating manifests via `--dry-run=server`, extracting fields with `-o jsonpath`, and debugging distroless Pods via `kubectl debug`, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes in real Kubernetes systems — specifically: Why the Pod is the atomic unit of scheduling: the `pause` infra container holding the shared Pod IP/`localhost` port space, sequential `initContainers`, `startupProbe` vs `readinessProbe` vs `livenessProbe`, graceful `SIGTERM` (`preStop` + `terminationGracePeriodSeconds`), and OOMKiller QoS tiers, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Create a production Pod specification running as non-root (`runAsNonRoot: true`, read-only root filesystem), gated by an Init container dependency check and separate Startup, Readiness, and Liveness probes.
For What Purpose: To understand and apply Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection in real Kubernetes systems — specifically: How Kubernetes controllers and Services dynamically discover Pods solely via key-value `labels` (never hardcoded IPs!), standard `app.kubernetes.io/*` taxonomy, equality (`=`, `!=`) vs set-based (`in`, `notin`, `exists`) selectors, and why `annotations` hold non-queryable metadata, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` in real Kubernetes systems — specifically: Virtual sub-clusters inside a physical cluster: namespaced (`Pod`, `Service`, `Secret`) vs cluster-scoped (`Node`, `PersistentVolume`, `StorageClass`, `ClusterRole`) objects, cross-namespace CoreDNS resolution (`<svc>.<ns>.svc.cluster.local`), and quota enforcement, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Provision a governed `checkout-prod` tenant namespace that enforces strict aggregate CPU/Memory/PVC ceilings while injecting sane default container requests/limits automatically.
For What Purpose: To understand and apply Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging in real Kubernetes systems — specifically: How the `ReplicaSet` controller continuously compares matching active Pods against `spec.replicas`, `metadata.ownerReferences` garbage collection (`Cascade=background/foreground/orphan`), and isolating a misbehaving Pod live in production by removing its selector label, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` in real Kubernetes systems — specifically: How `Deployment` orchestrates old and new `ReplicaSet`s during `RollingUpdate` vs `Recreate`, `maxSurge` & `maxUnavailable` math, `minReadySeconds`, `progressDeadlineSeconds`, `revisionHistoryLimit`, and instant rollback when a bad build fails readiness checks, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Author a 4-replica stateless `Deployment` manifest engineered to survive both rolling image upgrades and voluntary node drains with **zero** HTTP `502/504` connection resets.
For What Purpose: To understand and apply Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s in real Kubernetes systems — specifically: Why ephemeral Pod IPs necessitate stable virtual Service IPs (`ClusterIP`), mapping `port` -> named `targetPort`, `EndpointSlice` scalability (100 endpoints per slice vs monolithic `Endpoints`), Headless `clusterIP: None` returning direct Pod A/AAAA records, and Ingress / Gateway API L7 routing, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules in real Kubernetes systems — specifically: How Kubernetes defaults to a flat allow-all network, why `NetworkPolicy` requires a policy-capable CNI (Calico, Cilium, Weave — NOT basic `flannel` alone!), additive union semantics of multiple policies, `namespaceSelector` + `podSelector` AND vs OR YAML list traps, and CoreDNS egress rules, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Secure a `payments-prod` namespace with default-deny ingress/egress posture, allowing only `api-gateway` Pods to call `payment-svc:8443` and allowing `payment-svc` egress strictly to `postgres:5432` and CoreDNS `53`.
For What Purpose: To understand and apply Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats in real Kubernetes systems — specifically: Why writable container overlay layers vanish on container restart whereas Pod `emptyDir` survives container crashes for the lifetime of the Pod, RAM-backed `emptyDir.medium: Memory`, `projected` volumes combining `ConfigMap` + `Secret` + `serviceAccountToken`, and why `hostPath` is banned in restricted security policies, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` in real Kubernetes systems — specifically: Decoupling developer storage requests (`PersistentVolumeClaim`) from cluster infrastructure (`PersistentVolume` + CSI `StorageClass`), block `ReadWriteOnce` (`RWO`) vs shared NFS/EFS `ReadWriteMany` (`RWX`), `reclaimPolicy: Retain` vs `Delete`, and `WaitForFirstConsumer` multi-AZ scheduling, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Configure a production gp3/SSD `StorageClass` with `WaitForFirstConsumer` topology binding, online volume expansion (`allowVolumeExpansion: true`), and `Retain` reclaim protection for stateful workloads.
For What Purpose: To understand and apply Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` in real Kubernetes systems — specifically: Why clustered databases/brokers cannot share a single `Deployment` PVC, how `StatefulSet` guarantees stable ordinal Pod names (`kafka-0`, `kafka-1`), predictable peer DNS (`kafka-0.kafka-hl.ns.svc.cluster.local`), dedicated per-ordinal PVCs via `volumeClaimTemplates`, and `OrderedReady` vs `Parallel` lifecycle, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync in real Kubernetes systems — specifically: Why standard `Secret` `data` is merely Base64-encoded (NOT encrypted by default in `etcd`!), enabling `EncryptionConfiguration` with cloud KMS v2 providers, `Opaque` vs `kubernetes.io/tls` vs `dockerconfigjson` types, setting `immutable: true` to cut kubelet watch load, and External Secrets Operator, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Deploy a 3-replica clustered data store `StatefulSet` with deterministic DNS peers (`store-0.store-headless`), dedicated per-ordinal SSD PVCs, and an immutable TLS Secret mounted read-only.
For What Purpose: To understand and apply Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup in real Kubernetes systems — specifically: Running finite run-to-completion tasks (`restartPolicy: OnFailure | Never`), work-queue vs `completionMode: Indexed` parallel Jobs, bounding retries (`backoffLimit`, `activeDeadlineSeconds`), auto-pruning finished Pods (`ttlSecondsAfterFinished`), and `CronJob` `Allow` vs `Forbid` vs `Replace` overlap rules, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` in real Kubernetes systems — specifically: Additive allow-only authorization: Subjects (`User`, `Group`, `ServiceAccount`), Verbs (`get`, `list`, `watch`, `create`, `patch`, `delete`), reusing a `ClusterRole` inside a single namespace via a `RoleBinding`, time-bound projected `TokenRequest` JWTs, and verifying access with `kubectl auth can-i --as`, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Create a timezone-explicit nightly settlement `CronJob` preventing overlapping runs (`concurrencyPolicy: Forbid`) paired with a dedicated, least-privilege `ServiceAccount` audited via `kubectl auth can-i`.
For What Purpose: To understand and apply Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling in real Kubernetes systems — specifically: The 3 autoscaling dimensions: Horizontal Pod Autoscaler (`HPA v2`: `desiredReplicas = ceil[currentReplicas * (currentMetric / desiredMetric)]`), Event-Driven `KEDA` scaling to/from zero on Kafka/SQS lag, Vertical Pod Autoscaler (`VPA`), and Cluster Autoscaler / Karpenter provisioning EC2/cloud nodes for `Pending` Pods, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks in real Kubernetes systems — specifically: Why Helm 3 eliminated Tiller (pure client-side library obeying user RBAC!), Chart structure (`Chart.yaml`, `values.yaml`, `templates/`, `_helpers.tpl`), `sha256sum` ConfigMap checksum annotations to trigger Deployment rollouts automatically, OCI chart registries, and `helm upgrade --install --atomic`, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Package a production microservice as a reusable Helm 3 chart with `prod-values.yaml`, PodDisruptionBudget, and an `autoscaling/v2` `HPA` tuned for rapid burst scale-out and anti-flapping gradual scale-in.
For What Purpose: To understand and apply Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector in real Kubernetes systems — specifically: The cloud-native observability triad: `cAdvisor` (embedded in `kubelet` for container CPU throttling `container_cpu_cfs_throttled_periods_total` & RSS memory), `kube-state-metrics` (API object state: `CrashLoopBackOff`, `Pending` PVCs, `Deployment` replica mismatches), DaemonSet log shipping, and OpenTelemetry Collector, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery in real Kubernetes systems — specifically: Separating CI (test, Trivy scan, Cosign sign, push immutable image digest, open PR updating GitOps manifest repo) from pull-based CD (`ArgoCD` running inside the cluster reconciling Git -> cluster with `selfHeal` & `prune`), plus Argo Rollouts Canary / Blue-Green progressive delivery, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Define a Prometheus Operator `ServiceMonitor` scraping `/metrics` plus an ArgoCD `Application` manifest enforcing automated Git-to-cluster drift remediation (`selfHeal: true`).
For What Purpose: To understand and apply Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) in real Kubernetes systems — specifically: Route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) in real Kubernetes systems — specifically: Ensure a compromised frontend pod can never connect directly to database pods, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply CoreDNS Service Discovery, Headless Services & Topology-Aware Routing in real Kubernetes systems — specifically: Resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices in real Kubernetes systems — specifically: Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Design, implement, test, and document 'Multi-Namespace TLS-Routed & Network-Isolated K8s Mesh' applying every production concept from Module 11 (Gateway API / Ingress TLS, Zero-Trust NetworkPolicies & CoreDNS) of Kubernetes Training.
For What Purpose: To understand and apply Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens in real Kubernetes systems — specifically: Enforce strict per-team and per-microservice API server permissions, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS in real Kubernetes systems — specifically: Block privileged containers, root UIDs, and Linux capability escalations cluster-wide, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption in real Kubernetes systems — specifically: Sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply IRSA / Workload Identity Federation for Cloud SDK Access in real Kubernetes systems — specifically: Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Design, implement, test, and document 'Hardened CIS-Compliant Production Kubernetes Cluster' applying every production concept from Module 12 (RBAC Least Privilege, Pod Security Standards, External Secrets & Workload IAM) of Kubernetes Training.
For What Purpose: To understand and apply Authoring Reusable Helm Charts, Values Schema Validation & Hooks in real Kubernetes systems — specifically: Template standardized microservice deployments across 20+ engineering squads, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Kustomize Base + Dev/Stage/Prod Patch Overlays in real Kubernetes systems — specifically: Manage environment replica/resource differences cleanly without copy-pasting YAML, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD in real Kubernetes systems — specifically: Make Git the single auditable source of truth with 1-click rollbacks, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) in real Kubernetes systems — specifically: Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Design, implement, test, and document 'GitOps Multi-Environment Delivery Platform with ArgoCD' applying every production concept from Module 13 (Helm Charts, Kustomize Overlays, ArgoCD GitOps & Progressive Rollouts) of Kubernetes Training.
For What Purpose: To understand and apply Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention in real Kubernetes systems — specifically: Right-size pod memory/CPU scientifically and prevent OOMKilled evictions, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter in real Kubernetes systems — specifically: Scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks in real Kubernetes systems — specifically: Survive AZ outages and node upgrades with zero user-visible downtime, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting in real Kubernetes systems — specifically: Back up stateful volumes/cluster manifests and monitor golden SRE signals, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Design, implement, test, and document 'Auto-Scaling High-Availability Production Cluster Stack' applying every production concept from Module 14 (HPA/KEDA Autoscaling, Karpenter Nodes, PDBs, Stateful CSI & Prometheus) of Kubernetes Training.
For What Purpose: To understand and apply Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems in real Kubernetes systems — specifically: Block container privilege escalation and enforce least-privilege RBAC ServiceAccounts, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync in real Kubernetes systems — specifically: Isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets in real Kubernetes systems — specifically: Scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Architect, implement, test, and document 'Hardened Non-Root Namespace with NetworkPolicies, PSS Restricted & HPA/PDB' applying every bullet-wise engineering concept from Module 15 (Kubernetes Zero-Trust Security, Pod Security Standards & Autoscaling (HPA/KEDA)) of Kubernetes Training.
For What Purpose: To understand and apply GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts in real Kubernetes systems — specifically: Promote audited cluster changes automatically from Git with instant 1-click rollback, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures in real Kubernetes systems — specifically: Debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: To understand and apply Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews in real Kubernetes systems — specifically: Architect highly available stateful and stateless clusters and pass hands-on K8s scenarios, so your code stays reliable, fast, and easy to maintain in production.
For What Purpose: Architect, implement, test, and document 'Declarative Helm + Argo CD Rollout Pipeline & Live Cluster Failure Runbook' applying every bullet-wise engineering concept from Module 16 (GitOps Continuous Delivery (Argo CD), Cluster Troubleshooting & CKA/CKAD Prep) of Kubernetes Training.
For What Purpose: Understand real client business requirements, production architecture (Kubernetes Cluster + NGINX Ingress TLS + HPA + StatefulSets + Helm), and team Git branching.
For What Purpose: Build core client workflows, validation, and optimized database/state logic under Industry Expert mentorship.
For What Purpose: Eliminate vulnerabilities, race conditions, and regressions before exposing the system to real client traffic.
For What Purpose: Deploy the verified application to live cloud production with health checks, SSL/TLS, and uptime monitoring.
For What Purpose: Demonstrate your deployed client project, lock in resume/LinkedIn positioning, and receive dual recruiter-verifiable credentials.
Optional external proctored exams coached to ≥90% readiness: Official Vendor Exam Fee (Paid by Student) + Platform Charge (10% of Vendor Fee + 18% GST). Click any certificate card for full exam domains & blueprint details.
CNCF / The Linux Foundation
Pass: 66% • 1 Aligned Course
CNCF / The Linux Foundation
Pass: 66% • 1 Aligned Course
Mirantis / Docker Ecosystem
Pass: 65% • 1 Aligned Course
See the exact job positions and realistic salary packages unlocked by Kubernetes Training, plus which specialized MockAttempt Academy courses to pair next to qualify for higher-paying multi-stack engineering roles.
Deployments, Ingress TLS, HPA, StatefulSets, Helm & live K8s cluster project proof.
RBAC, NetworkPolicies, GitOps, observability & multi-AZ production troubleshooting.
Unlocked by combining Kubernetes with Docker, AWS Cloud, and Terraform CI/CD!
Why Learn Next (For What Purpose): Build hardened Docker images, automate CI/CD & Terraform, and deploy seamlessly onto Kubernetes.
Why Learn Next (For What Purpose): Combine Kubernetes orchestration with AWS VPC, IAM, ALB, and managed cloud services.
Why Learn Next (For What Purpose): Bridge application engineering and Kubernetes platform design for top architectural roles.
MockAttempt Academy keeps production-grade Kubernetes (K8s), Pods, StatefulSets, RBAC & Helm training ultra-affordable at ₹6,499 so every learner gets 10,000 MCQs, 10 Capstone Projects, Live Client Production deployment, and spoken Google Gemini AI interview coaching.
Total guided duration is 10–12 Weeks (120 Hours). Weekday Live Online Batches run Mon–Fri for 1.5 Hours/Day (08:00–09:30 AM or 07:30–09:00 PM IST). Weekend Intensive Live Online Batches run Sat–Sun for 3.0 Hours/Day (10:00 AM–01:00 PM or 03:00–06:00 PM IST).
Instead of passive pre-recorded videos, you code live with senior architects, monitor 100% of syllabus progress transparently, build 10 progressive Module Projects during training, and deploy a real Live Client Project to production at the end of the course — earning both a Barcode Graduation Certificate and an Official Project Internship Confirmation Letter.
Every single topic includes 10 Timed Mock Tests (each containing 50 MCQs = 500 MCQs per topic) and 10 Spoken Voice + Google Gemini AI Technical Interview rounds.
The AI Interviewer speaks each question aloud via Text-to-Speech, listens via microphone Speech-to-Text, normalizes spoken technical syntax, and sends your transcript to the Google Gemini AI verification agent for scoring and follow-up probing.
All MockAttempt Tech Academy fees are 100% fixed and non-negotiable (₹6,499 + 18% GST). We never inflate fake MRPs only to bargain discounts because we refuse to compromise on Senior Industry Expert trainers, small interactive live batches, real cloud/repository labs, daily assessments, or placement mentorship.
MockAttempt Tech Academy ensures you qualify through official exam-blueprint live coaching, timed 50-MCQ scenario simulators, and 1-on-1 readiness audits with unlimited re-tests until you score ≥90%. When you opt to apply for an external proctored exam (AWS, Oracle, Python Institute, MongoDB, PostgreSQL, CNCF, Pearson VUE, etc.), you pay the official exam registration fee to the certification vendor plus a transparent 10% Platform Per-Exam Charge (10% of the fee paid to the vendor + 18% GST) for dedicated exam-readiness verification and booking facilitation. Statutory 18% GST also applies to all course tuition fees with official GST invoices.
How MockAttempt Tech Academy Beats Other Institutes
| Evaluation Criteria | ✨ MockAttempt Tech Academy | ₹2–3L Bootcamps | Recorded Video Sites |
|---|---|---|---|
| Fixed Non-Negotiable Fees | ✓ From $24 USD + 18% GST Skill Tracks & From $536 USD + 18% GST Career Programs • 0% Salary Cut | ✕ Inflated ₹80K–₹2.5L fees + 15%–20% ISA salary cut | △ Flash-sale pricing, zero live mentor support |
| Live Mentorship & Schedule | ✓ 100% Live Senior Architect Sessions (Weekday & Weekend + Day 1–Day N Plan) | △ Crowded 80+ batches or junior TA instructors | ✕ Pre-recorded videos with zero live doubt resolution |
| Topic Mock Tests & Voice AI Viva | ✓ 100% Topic Coverage in Timed MCQ Exams & Spoken Google Gemini AI Interviews | ✕ 1–2 generic tests at the end of training | ✕ Basic 3–5 question quizzes; zero spoken viva |
| Production Repos & Credentials | ✓ Real Module Repos + Live Client Prod Deploy + Barcode Cert & Internship Letter | ✕ Toy clone apps & unverifiable paper certificate | ✕ Copy-along demos; zero internship letter |
| Global Vendor Certifications | 🏅 32+ Official Exams Supported (Vendor Fee by Student + 10% Platform Charge) | ✕ Extra ₹15K–₹30K exam coaching charges | ✕ Self-study videos; zero readiness audit |
| 6-Month Placement Covenant | 🛡️ 100 Verified IT Job Openings in 6 Months via Incentivized Recruiters + Indian MNC & Consultancy Tie-Ups — Or 100% Net Tuition Refund* | ✕ Passive job-board links; zero recruiter closure incentive or refund | ✕ None |
Complete Solution Desk: Enquire for Live IT Courses, 100% Placement-Guaranteed* Career Programs, Unplaced / Career-Gap Placement Support, or Ready-to-Submit College / Final-Year Projects (Code + Report + Viva).
🔒 Fixed & Non-Negotiable Course Fees: Course prices are fixed for every student — zero bargaining so we never compromise on Senior Industry Trainers & placement outcomes.
