Course Overview & Delivery Format
Methodology: 95% Hands-On Enterprise Labs / 5% Instructor-Led Architecture Discussion
Target Audience: VMware Administrators, Infrastructure Engineers, Systems Engineers, Linux Administrators, Cloud & DevOps Engineers
Overview: This intensive boot camp is designed for experienced VMware administrators and infrastructure engineers transitioning to Kubernetes. The course is approximately 95% hands-on and focuses on enterprise planning, migration, storage, networking, automation, high availability, backup, disaster recovery, and production operations across guided enterprise labs.
Module 1: Enterprise Kubernetes Architecture & Foundations
Module Focus: Establish a foundational understanding of enterprise Kubernetes architecture, container runtimes, networking models, persistent storage backends, and cluster control planes. Learn to build, optimize, and validate enterprise-ready nodes and multi-master Kubernetes clusters from bare-metal or VM-based infrastructure up.
Key Core Curriculum Topics
- Kubernetes Control Plane & Worker Node architecture vs. VMware vSphere/ESXi infrastructure models
- Enterprise container runtimes (containerd, CRI-O), OS dependencies, and node image patching
- Container Network Interfaces (CNI): Calico, Cilium, eBPF, BGP routing, and Network Policies
- Container Storage Interfaces (CSI): Local path provisioning, NFS, iSCSI, and Rook-Ceph storage operators
- Pod lifecycle management, Container Resource Requests/Limits, Declarative YAML Specs, and Helm Charts
- Multi-master cluster deployment (kubeadm), etcd quorum management, and cluster health diagnostics
Guided Hands-On Enterprise Labs
- Lab 1.1 Base Linux Host Preparation: Provision Linux OS nodes, configure hostnames, kernel modules (overlay, br_netfilter), sysctl parameters, and network interfaces for cluster nodes. Validate system dependencies and CRI readiness across subnets.
- Lab 1.2 Enterprise Container Runtime Installation: Install and configure containerd/CRI-O runtimes, configure systemd cgroup drivers, and configure internal registry mirrors for enterprise image pulling.
- Lab 1.3 Core Network & Etcd Latency Validation: Configure cluster-wide DNS, NTP time synchronization, and host resolution. Validate network latency and disk I/O requirements needed for etcd raft consensus stability.
- Lab 1.4 Calico/Cilium CNI Deployment: Install and configure enterprise CNI plugins (e.g., Cilium or Calico) across worker nodes. Establish pod network overlay networks and verify inter-node pod communication paths.
- Lab 1.5 Pod Network Isolation & NetworkPolicies: Create NetworkPolicy resources to enforce strict microsegmentation between namespaces, restricting traffic across frontend, backend, and database tiers.
- Lab 1.6 Local Persistent Volume Provisioning: Initialize Local Storage Class operators for high-performance block storage allocation directly on worker node NVMe/SATA drives.
- Lab 1.7 Rook-Ceph CSI Storage Operator Deployment: Deploy a hyperconverged Rook-Ceph storage operator within Kubernetes. Configure dynamic block (RBD) and file (CephFS) provisioning for persistent volume claims (PVCs).
- Lab 1.8 NFS & Shared File CSI Integration: Connect Kubernetes clusters to enterprise NFS arrays using NFS CSI drivers. Validate dynamic PVC provisioning and ReadWriteMany (RWX) multi-pod access models.
- Lab 1.9 iSCSI Block Storage Integration via CSI: Discover and attach external enterprise iSCSI targets to Kubernetes worker nodes using dedicated CSI drivers for high-throughput ReadWriteOnce (RWO) block storage.
- Lab 1.10 Private Enterprise Container Registry Setup: Deploy and configure a secure container registry (Harbor/ JFrog) for centralized application image hosting, complete with TLS, project RBAC, and vulnerability scanning.
- Lab 1.11 Stateless Workload Deployment (Deployments & ReplicaSets): Deploy multi-replica web applications using standard Kubernetes manifest specs. Define container resource requests, limits, and liveness/readiness probes.
- Lab 1.12 Stateful Workload Deployment (StatefulSets): Create a StatefulSet application with stable network identities and persistent storage volume claims. Test sequential pod deployment and persistent data attachment.
- Lab 1.13 Ingress Controller & Cert-Manager Setup: Install an NGINX or Envoy Ingress Controller alongside cert-manager. Configure TLS termination and routing rules for internal and external application access.
- Lab 1.14 Helm Chart Templating & Management: Package application manifests into reusable Helm charts. Parameterize values files for deployment across staging and production environments.
- Lab 1.15 Kube-VIP & Load Balancer Integration: Implement kube-vip or MetalLB to provide native layer 2/BGP load balancing and virtual IP management for control plane and application services.
- Lab 1.16 Multi-Master Kubernetes Cluster Bootstrap: Initialize a high-availability control plane using kubeadm with external/stacked etcd endpoints. Join worker nodes to the control plane and verify system pods.
- Lab 1.17 Etcd Backup, Quorum & Health Diagnostics: Perform etcdctl snapshot backups, evaluate cluster vote distribution, and simulate single control plane node failure to test raft quorum stability.
- Lab 1.18 Storage & Network Performance Benchmarking: Execute performance testing pods (using fio and iperf3) against dynamic CSI volumes and CNI plugins to establish performance baselines.
- Lab 1.19 Kubernetes Cluster Troubleshooting: Diagnose and resolve intentional CNI network misconfigurations, broken etcd endpoints, and node NotReady states using kubectl, journalctl, and crictl.
- Lab 1.20 Architecture Validation & Comprehensive Review: Audit cluster health, storage classes, CNI configurations, and control plane status using automated tools (e.g., sonobuoy) prior to executing migration workflows.
Module 2: VMware Infrastructure Assessment & Workload Modernization Plannin
Module Focus: Discover, analyze, and plan the systematic migration or containerization of VMware vSphere workloads to Kubernetes. Master automated discovery, container packaging, KubeVirt VM-to-Kubernetes imports, and database migration pipelines.
Key Core Curriculum Topics
- Automated discovery and inventory of VMware vCenter assets, VM sizing, and application profiling
- Workload transformation strategy: Rehosting VMs via KubeVirt vs. Containerizing (Refactoring) apps
- Image packaging: Converting VM disk images and application binaries into OCI container images
- Migration execution models: Offline, warm database sync, and dual-run cutover strategies
- Storage and Network Refactoring: Translating vSphere Storage/PortGroups to CSIs and CNIs
- Rollback strategies, change control execution, and post-migration validation
Guided Hands-On Enterprise Labs
- Lab 2.1 Automated VMware Environment Discovery: Execute discovery scripts against vCenter APIs to catalog ESXi hosts, datastores, network port groups, and virtual machines. Export machine specs to build a migration tracking matrix.
- Lab 2.2 Application Dependency & Microservices Mapping: Analyze network traffic and application interdependencies across legacy VMware workloads to group applications into low-risk migration waves.
- Lab 2.3 KubeVirt Operator Installation & Configuration: Install and configure the KubeVirt operator on Kubernetes to enable running virtualized workloads natively alongside containers within pods.
- Lab 2.4 VMware VM Import via Forklift/MTV: Deploy Migration Toolkit for Virtualization (MTV / Crane) to automate the direct import of ESXi VMs into KubeVirt VirtualMachine instances on Kubernetes.
- Lab 2.5 Containerizing Legacy Linux Applications: Refactor a monolithic Linux application running on VMware into a containerized workload using Dockerfiles/Buildah and push it to the internal registry.
- Lab 2.6 Containerizing Legacy Windows Applications: Extract legacy Windows Server services (IIS/Worker processes) into Windows Server Container images and run them on Windows Kubernetes worker nodes.
- Lab 2.7 Database Modernization (VMware SQL to K8s Operator): Plan the transition of standalone vSphere SQL/PostgreSQL databases to cloud-native database operators (e.g., CloudNativePG or Zalando DB Operator).
- Lab 2.8 Cold Migration Execution: Linux Workload to Container: Execute full offline migration of a Linux application server from vSphere into a native Kubernetes Deployment and Service.
- Lab 2.9 Cold Migration Execution: Windows Workload to KubeVirt VM: Perform cold migration of legacy Windows Server instances directly into KubeVirt VMs, injecting VirtIO containerized drivers.
- Lab 2.10 Migrating Active Directory / LDAP Integration: Rebind containerized applications and K8s API authentication to enterprise Active Directory domain controllers remaining on-premises or cloud.
- Lab 2.11 Migrating Enterprise Database Data: Perform database-aware data transfers from VMware-hosted databases to Kubernetes-managed persistent volumes using live replication/logical dumps.
- Lab 2.12 Migrating Web Application Stack to Ingress: Transition vSphere-hosted IIS/Nginx web servers to K8s Deployments fronted by Ingress resource paths and automatic TLS certificates.
- Lab 2.13 Post-Migration Cleanup & Agent Injection: Remove legacy VMware Tools agents, inject Kubernetes readiness/liveness health probes, and configure native stdout/stderr log output.
- Lab 2.14 External DNS & Ingress Routing Automation: Execute automated external-dns automation to update enterprise DNS records and routing tables during application cutovers.
- Lab 2.15 Persistent Volume Tuning & I/O Optimization: Tune CSI persistent volume caching, disk IOPS limits, and storage classes to match or exceed vSphere baseline performance metrics.
- Lab 2.16 Cold Migration Wave Execution: Execute a multi-application migration wave using automated scripted pipelines. Track conversion times, deployment durations, and application downtime.
- Lab 2.17 Near-Zero Downtime Data Syncing: Configure continuous database/storage sync tasks (e.g., CDC or volume replication) to replicate changed blocks prior to final traffic cutover.
- Lab 2.18 Rollback Plan Execution & DR Simulation: Simulate a migration failure during cutover, execute traffic redirection back to VMware, and validate data integrity to ensure zero data loss.
- Lab 2.19 Post-Migration Application Acceptance Testing: Run automated validation scripts (e2e tests) to verify database connectivity, web endpoints, and API response codes on the K8s stack.
- Lab 2.20 Migration Readiness Gate Review: Audit migration metrics, application response times, and target cluster capacity usage. Complete the readiness checklist before moving to production storage/HA.
Module 3: Enterprise Storage, Backup & High Availability Architecture
Module Focus: Design, deploy, and maintain highly available compute, networking, and storage infrastructure in Kubernetes. Master cluster autoscaling, Velero container backups, disaster recovery, stateful volume snapshots, and control plane HA.
Key Core Curriculum Topics
- High Availability Control Plane Topology: Multi-master design, Load Balancing, and etcd recovery
- Cloud-Native Storage: Ceph/Rook, Longhorn, or SAN/NAS CSI storage management, snapshots, and expansion
- Application Resiliency: Pod Disruption Budgets (PDB), Node Affinity, Taints, Tolerations, and Topology Spread Rules
- Disaster Recovery & Backup: Velero backup server, volume snapshotting, and cross-cluster recovery
- Autoscaling: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler
- Cluster Health, Node Remediation, and Automated Fencing Operators (e.g., Poison-Pill / Node Health Check Operators)
Guided Hands-On Enterprise Labs
- Lab 3.1 Highly Available Control Plane Load Balancing: Configure redundant HAProxy/Keepalived or kube-vip instances to load balance API requests across multiple Kubernetes control plane nodes.
- Lab 3.2 Dynamic CSI Storage Class Provisioning: Define and tune dynamic StorageClasses configured with customized replication factors, IOPS tiers, and snapshot capabilities.
- Lab 3.3 Stateful Workload Scaling & Storage Expansion: Scale a stateful database cluster, execute live Persistent Volume expansion on running PVCs, and verify data consistency.
- Lab 3.4 Velero Backup Operator Installation: Deploy Velero with S3-compatible object storage backends for total cluster backup, volume snapshotting, and disaster recovery execution.
- Lab 3.5 Velero Cluster & Namespace Backup Configuration: Configure scheduled, automated backups for application namespaces, including manifest state, secrets, and underlying PVC snapshots.
- Lab 3.6 Automated Volume Snapshots & Restores: Execute VolumeSnapshot resources via CSI drivers. Perform point-in-time state restores from volume snapshots into new application PVCs.
- Lab 3.7 Granular Application File & Object Restores: Perform granular application data restores using Velero and restic/Kopia file-level backup engines without restoring full volumes.
- Lab 3.8 Cross-Namespace & Cross-Cluster Data Replication: Set up automated storage volume replication tasks across multiple Kubernetes clusters or geographically separated namespaces.
- Lab 3.9 Pod Disruption Budgets & Anti-Affinity Configuration: Define PodDisruptionBudgets (PDB) and pod anti-affinity rules to guarantee application availability during node drains and worker maintenance.
- Lab 3.10 Hard Worker Node Failure Simulation: Simulate ungraceful node power loss on a worker node. Observe pod eviction timers, controller rescheduling, and PV attachment to surviving nodes.
- Lab 3.11 Live Node Maintenance & Eviction (Drain/Cordon): Gracefully cordon and drain Kubernetes worker nodes using kubectl drain, tracking pod migration times and zero-downtime execution.
- Lab 3.12 KubeVirt Live VM Migration: Perform live migration of a running KubeVirt VirtualMachine instance between Kubernetes worker nodes, monitoring latency and network state preservation.
- Lab 3.13 Horizontal & Vertical Pod Autoscaling: Configure HPA based on CPU/RAM metrics and custom Prometheus metrics. Configure VPA to dynamically resize pod resource requests.
- Lab 3.14 Storage Node Failure & Resynchronization: Forcefully take down a storage node within a hyperconverged storage cluster (e.g., Rook-Ceph). Observe automated data rebalancing and disk recovery.
- Lab 3.15 CNI Network Link Failover & Multipathing: Simulate physical network interface failures on underlying worker nodes. Test bonded CNI interfaces and multi-pathing resilience.
- Lab 3.16 Etcd Backup & Disaster Recovery Restoration: Perform a simulated corruption of the etcd datastore. Restore control plane state from an offline etcd snapshot to bring the cluster back online.
- Lab 3.17 Automated Node Remediation & Fencing: Deploy the Node Health Check and Poison-Pill operators to automatically fence and reboot unresponsive worker nodes, freeing stuck PVCs.
- Lab 3.18 Cross-Datacenter Disaster Recovery & Velero Restore: Configure Velero to sync backups to a secondary remote Kubernetes cluster. Perform a cross-site disaster recovery restore challenge.
- Lab 3.19 Total Cluster Rebuild & Disaster Recovery Challenge: Simulate complete control plane and worker cluster destruction. Rebuild bare-metal nodes and restore all workloads from Velero backups.
- Lab 3.20 Storage & HA Operational Review: Evaluate cluster storage quotas, backup status, and HA rules using administrative dashboards (OpenShift Console / Lens / Kube-Prometheus).
Module 4: Enterprise Security, Automation & Production Operations
Module Focus: Secure, automate, and manage enterprise Kubernetes clusters at scale. Master Role-Based Access Control (RBAC), OIDC authentication, GitOps with ArgoCD/Flux, Infrastructure-as-Code via Terraform, policy enforcement, and observability.
Key Core Curriculum Topics
- Enterprise Authentication & Authorization: RBAC, OIDC, Dex, Active Directory/Okta integration
- Kubernetes Security Hardening: Pod Security Standards (PSS), Kyverno/OPA Gatekeeper, Network Policies, and CIS Benchmarks
- GitOps & Continuous Delivery: Automated deployment pipelines with ArgoCD or FluxCD
- Infrastructure as Code (IaC): Provisioning K8s infrastructure using Terraform, OpenTofu, and Ansible
- Enterprise Observability: Prometheus, Thanos, Grafana dashboards, Loki log aggregation, and OpenTelemetry
- Automated Patching & Maintenance: Rolling worker node OS updates, K8s control plane upgrades (kubeadm upgrade)
Guided Hands-On Enterprise Labs
- Lab 4.1 OIDC & Active Directory Integration: Connect Kubernetes API server authentication to corporate Active Directory/Okta via Dex or Keycloak OIDC providers.
- Lab 4.2 Role-Based Access Control (RBAC) Implementation: Define ServiceAccounts, Roles, ClusterRoles, RoleBindings, and ClusterRoleBindings. Restrict developer access to specific namespace resources.
- Lab 4.3 Service Account Token Security & Secrets Management: Configure projected service account tokens and integrate external secret operators (Vault / AWS Secrets Manager) to securely inject app secrets.
- Lab 4.4 Automated TLS Certificate Management (Cert-Manager): Deploy cert-manager integrated with internal Enterprise PKI or ACME providers to auto-issue and renew TLS certificates across Ingress resources.
- Lab 4.5 Pod Security Standards (PSS) & Admission Control: Enforce Privileged, Baseline, and Restricted Pod Security Standards across namespaces to prevent root container executions.
- Lab 4.6 Policy Enforcement via Kyverno / OPA Gatekeeper: Write and deploy Kyverno policy manifests to enforce container image provenance, required resource limits, and blocked volume types.
- Lab 4.7 GitOps Continuous Delivery Setup with ArgoCD: Deploy ArgoCD, establish connection to application Git repositories, and configure declarative GitOps application management.
- Lab 4.8 IaC Infrastructure Provisioning with Terraform: Write and execute Terraform manifests to provision Kubernetes cluster infrastructure, namespace hierarchies, and core operators automatically.
- Lab 4.9 Terraform Workload Management & Helm Provider: Manage application Helm releases declaratively via the Terraform Helm and Kubernetes providers.
- Lab 4.10 Ansible Automation for Node Lifecycle Management: Execute Ansible playbooks to standardize worker node OS packages, apply kernel patches, and join new nodes into the cluster.
- Lab 4.11 Automated GitOps Application Deployment: Push application updates to Git and observe automated, zero-downtime rolling updates deployed by ArgoCD across worker nodes.
- Lab 4.12 Custom Kubernetes API Scripting & Operator SDK: Interact with the Kubernetes REST API using Python (kubernetes client) and kubectl plugins to automate custom operational tasks.
- Lab 4.13 Prometheus & Grafana Observability Stack Deployment: Deploy the Prometheus Operator (kube-prometheus-stack) to collect metrics across control plane nodes, kubelets, and workload pods.
- Lab 4.14 Grafana Enterprise Dashboards & Visualization: Import and customize Grafana dashboards to monitor cluster CPU/RAM utilization, pod restart rates, network bandwidth, and persistent storage.
- Lab 4.15 Centralized Logging with Loki & Vector/Fluentbit: Deploy Loki and Fluentbit/Vector agents to collect, aggregate, and index stdout/stderr logs from all running application containers.
- Lab 4.16 Automated Alerting & Incident Notification: Configure Prometheus Alertmanager rules and notification targets (Slack, PagerDuty, Webhooks) for critical cluster events.
- Lab 4.17 Cluster Security Hardening & CIS Benchmark Audit: Run kube-bench and kube-hunter to audit control plane and worker nodes against CIS Kubernetes Benchmarks. Apply remediation steps.
- Lab 4.18 Zero-Downtime Cluster Version Upgrades: Perform rolling upgrades of the Kubernetes control plane and worker nodes using kubeadm upgrade, draining nodes sequentially without workload downtime.
- Lab 4.19 Automated Disaster Recovery Scripting: Build automated recovery scripts to restore ArgoCD configurations and recreate namespaces from Git repositories in a total disaster recovery scenario.
- Lab 4.20 Security & Operations Operational Review: Audit security policies, RBAC access, monitoring pipelines, and backup jobs to ensure the cluster is fully operational and compliant.
Bootcamp Key Takeaways & Enterprise Competencies
- Architecture & Deployment: Design, build, and maintain production-grade, highly available Kubernetes clusters integrated with cloud-native CSI storage arrays and advanced CNI network fabrics.
- Workload Modernization & Migration: Execute end-to-end migrations of complex Linux and Windows workloads from VMware vSphere to Kubernetes (via containerization or KubeVirt VMs) with minimal downtime.
- Automation & Infrastructure as Code: Leverage Terraform, Ansible, GitOps (ArgoCD), and the Kubernetes REST API to automate cluster initialization, application provisioning, and daily operational tasks declaratively.
- Resilience & Disaster Recovery: Implement robust backup and disaster recovery topologies using Velero, automated snapshot management, Pod Disruption Budgets, and automated node fencing.
- Operational Excellence: Develop comprehensive migration runbooks, enforce security hardening compliance, manage observability stacks (Prometheus/Grafana), and execute seamless production cutovers.
