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Containerization And Virtualization in DevOps

$299.00
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Course access is prepared after purchase and delivered via email
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Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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This curriculum spans the technical depth and operational breadth of a multi-workshop program focused on enterprise containerization and virtualization, comparable to an internal capability build-out for standardizing cloud-native infrastructure across hybrid environments.

Module 1: Foundations of Virtualization in Enterprise Infrastructure

  • Selecting between full virtualization, paravirtualization, and hardware-assisted virtualization based on guest OS compatibility and performance requirements.
  • Configuring CPU and memory overcommit ratios in hypervisors while maintaining SLA compliance for critical workloads.
  • Implementing NUMA-aware VM placement to avoid remote memory access penalties in multi-socket hosts.
  • Designing storage backends for VMs using thin vs. thick provisioning based on IOPS, capacity planning, and snapshot needs.
  • Integrating VMs with existing identity providers for console access and audit logging.
  • Establishing VM lifecycle policies for patching, decommissioning, and image version control.
  • Evaluating Type 1 vs. Type 2 hypervisors in regulated environments with strict isolation requirements.
  • Managing VM sprawl through automated tagging, resource quotas, and chargeback mechanisms.

Module 2: Container Architecture and Runtime Design

  • Choosing between container runtimes (runc, gVisor, Kata Containers) based on security, performance, and compatibility needs.
  • Defining resource limits and requests for CPU and memory in container manifests to prevent noisy neighbor issues.
  • Implementing init containers for pre-start dependency checks and configuration validation.
  • Configuring container health checks using liveness, readiness, and startup probes with appropriate thresholds.
  • Designing multi-stage Dockerfiles to minimize image size and reduce attack surface.
  • Managing container UID/GID mappings to prevent privilege escalation on host systems.
  • Enforcing seccomp, AppArmor, and SELinux profiles at runtime for defense-in-depth.
  • Handling PID and orphaned process management in long-running containerized services.

Module 3: Image Management and Registry Operations

  • Designing a multi-tenant container registry hierarchy with project-based access controls and retention policies.
  • Implementing image signing using Cosign or Notary to enforce supply chain integrity.
  • Automating vulnerability scanning in CI pipelines with tools like Trivy or Clair and defining severity thresholds for blocking.
  • Syncing images across geographically distributed registries to reduce pull latency and improve resiliency.
  • Creating base image governance policies that mandate patching cadence and owner accountability.
  • Managing image metadata through annotations for compliance, ownership, and deployment constraints.
  • Configuring registry garbage collection and storage cleanup to avoid disk exhaustion.
  • Integrating image promotion workflows with GitOps pipelines using semantic versioning.

Module 4: Orchestration with Kubernetes in Production

  • Designing node pools with taints and tolerations to isolate workloads by security level or hardware type.
  • Implementing PodDisruptionBudgets to maintain availability during node maintenance or cluster upgrades.
  • Configuring custom resource definitions (CRDs) with validation schemas and admission controllers.
  • Setting up horizontal and vertical pod autoscaling with metrics from custom Prometheus exporters.
  • Managing stateful applications using StatefulSets with persistent volume claims and storage classes.
  • Implementing network policies to restrict pod-to-pod communication based on zero-trust principles.
  • Using init containers to enforce preconditions before application startup in multi-container pods.
  • Planning for etcd backup and restore procedures with regular snapshot testing.

Module 5: Networking Models and Service Connectivity

  • Selecting CNI plugins (Calico, Cilium, Flannel) based on network policy enforcement and performance needs.
  • Designing service mesh integration using sidecar injection and mTLS for inter-service encryption.
  • Configuring ingress controllers with rate limiting, WAF integration, and TLS termination.
  • Implementing multi-cluster service discovery using federated DNS or service mesh gateways.
  • Managing external access through NodePort, LoadBalancer, or MetalLB in on-prem environments.
  • Resolving DNS latency issues by tuning CoreDNS cache settings and upstream resolvers.
  • Isolating development, staging, and production traffic using namespace-level network policies.
  • Debugging hairpinning and SNAT issues in NAT-heavy environments with custom iptables rules.

Module 6: Persistent Storage and Data Management

  • Selecting storage classes (SSD, HDD, NVMe) based on application I/O patterns and cost constraints.
  • Implementing dynamic provisioning with CSI drivers for cloud and on-prem storage systems.
  • Designing backup and restore workflows for stateful applications using Velero with application consistency hooks.
  • Managing access modes (ReadWriteOnce, ReadWriteMany) for shared filesystems in clustered applications.
  • Handling volume resizing operations with minimal downtime and application impact.
  • Monitoring storage utilization and IOPS to detect misconfigured PVCs or runaway processes.
  • Integrating with enterprise storage solutions (NetApp, Pure Storage) using vendor-specific CSI plugins.
  • Enforcing data retention and encryption policies at the storage layer for compliance.

Module 7: Security, Compliance, and Runtime Enforcement

  • Implementing admission controllers (OPA Gatekeeper, Kyverno) to enforce organizational policies on resource creation.
  • Conducting regular node hardening audits using CIS benchmarks and automated scanning tools.
  • Managing secrets using external vaults (HashiCorp Vault) with short-lived tokens and rotation policies.
  • Enabling audit logging for Kubernetes API server and filtering events based on sensitivity.
  • Configuring pod security standards (restricted, baseline, privileged) across namespaces.
  • Performing runtime threat detection using Falco or Sysdig to monitor for anomalous process execution.
  • Integrating container security into CI/CD with pre-commit hooks and policy-as-code checks.
  • Responding to container breakout incidents with host-level containment and forensic collection.

Module 8: Observability and Day 2 Operations

  • Deploying distributed tracing for microservices using OpenTelemetry and backend collectors.
  • Configuring structured logging pipelines with Fluentd or Vector and enforcing JSON schema compliance.
  • Setting up SLOs and error budgets using Prometheus metrics and alerting via Alertmanager.
  • Managing log retention and indexing costs by filtering low-value logs at the source.
  • Diagnosing performance bottlenecks using container-level CPU, memory, and network profiling.
  • Implementing cluster health dashboards with Grafana for infrastructure and application metrics.
  • Automating routine operations (node rotation, certificate renewal) using operators and CronJobs.
  • Conducting chaos engineering experiments to validate resilience of containerized systems.

Module 9: Hybrid and Multi-Cloud Deployment Strategies

  • Designing cluster federation models for workload portability across AWS, Azure, and on-prem environments.
  • Managing configuration drift using GitOps tools (ArgoCD, Flux) with environment-specific overlays.
  • Implementing hybrid DNS and service discovery to bridge cloud and data center workloads.
  • Optimizing cross-cloud data transfer costs using caching, compression, and scheduling.
  • Enforcing consistent security policies across clusters using centralized policy engines.
  • Handling cloud provider-specific IAM roles and service accounts in multi-cloud Kubernetes.
  • Planning for disaster recovery using active-passive cluster configurations and data replication.
  • Monitoring cloud spending by namespace and team using cost allocation tools like Kubecost.