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Cloud-Native Development in Cloud Migration

Cloud-Native Development in Cloud Migration

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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 and operational rigor of a multi-workshop cloud migration program, covering the same breadth of cross-functional planning, platform engineering, and runtime governance activities typically engaged in enterprise advisory projects for cloud-native transformation.

Module 1: Strategic Assessment and Readiness for Cloud-Native Migration

  • Evaluate existing monolithic applications to determine rehost, refactor, or rebuild decisions based on business criticality and technical debt.
  • Map legacy system dependencies using automated discovery tools to identify integration risks during migration.
  • Define cloud readiness criteria including compliance posture, data residency constraints, and operational maturity.
  • Conduct workload profiling to assess performance, scalability, and availability requirements pre-migration.
  • Establish cross-functional migration teams with clear ownership across development, operations, security, and business units.
  • Develop a phased migration roadmap prioritizing applications by risk, value, and interdependencies.

Module 2: Cloud Platform Selection and Architecture Design

  • Compare managed Kubernetes services (EKS, AKS, GKE) based on control plane SLAs, networking models, and integration with existing tooling.
  • Design multi-account or multi-tenant cloud landing zones with isolated environments for dev, staging, and production.
  • Implement identity federation between on-premises directories and cloud IAM using SAML or OIDC.
  • Select storage backends (block, object, file) based on application I/O patterns and durability requirements.
  • Architect hybrid connectivity using Direct Connect, ExpressRoute, or Cloud VPN with failover and bandwidth planning.
  • Define network segmentation using VPC peering, transit gateways, or service mesh sidecar patterns.

Module 3: Containerization and Microservices Transformation

  • Break monolithic applications into bounded-context services using domain-driven design and transactional boundary analysis.
  • Containerize legacy applications with minimal code changes while managing stateful components and local filesystem dependencies.
  • Define Docker image build standards including base OS selection, CVE scanning, and artifact signing.
  • Implement sidecar patterns for logging, monitoring, and configuration to decouple cross-cutting concerns.
  • Manage service discovery and inter-service communication using DNS, service mesh, or API gateways.
  • Refactor synchronous HTTP calls to asynchronous event-driven patterns using message queues or event buses.

Module 4: CI/CD Pipeline Implementation for Cloud-Native Systems

  • Design immutable deployment pipelines with versioned artifacts and environment promotion gates.
  • Integrate static code analysis, SAST, and dependency scanning into pull request workflows.
  • Configure canary deployments with automated rollback based on health checks and metrics thresholds.
  • Manage infrastructure as code using GitOps workflows with tools like ArgoCD or Flux.
  • Secure pipeline secrets using external vaults (Hashicorp Vault, AWS Secrets Manager) instead of environment variables.
  • Enforce pipeline compliance through policy-as-code tools like OPA or Sentinel for IaC validation.

Module 5: Observability and Runtime Governance

  • Standardize telemetry collection across logs, metrics, and traces using OpenTelemetry instrumentation.
  • Design log aggregation pipelines with filtering, sampling, and retention policies to control cost and volume.
  • Define SLOs and error budgets for critical services to guide incident response and release pacing.
  • Configure distributed tracing to diagnose latency across service boundaries and third-party dependencies.
  • Implement synthetic monitoring to validate end-to-end user journeys across cloud regions.
  • Enforce tagging and resource naming policies to enable chargeback, cost allocation, and security audits.

Module 6: Security and Compliance in Cloud-Native Environments

  • Apply least-privilege IAM roles to workloads using pod identity or instance profiles instead of shared credentials.
  • Scan container images in registries for vulnerabilities and enforce admission controls via Kubernetes policies.
  • Encrypt data in transit using mTLS across service mesh or API gateway layers.
  • Implement network policies to restrict pod-to-pod communication based on zero-trust principles.
  • Conduct regular configuration audits of Kubernetes clusters using tools like kube-bench or Policheck.
  • Integrate cloud security posture management (CSPM) tools to detect misconfigurations in real time.

Module 7: Resilience, Scaling, and Cost Optimization

  • Design failure domains across availability zones and implement pod disruption budgets for rolling updates.
  • Configure horizontal and vertical pod autoscalers based on custom or external metrics from application queues.
  • Implement circuit breakers and retry logic with exponential backoff in service clients.
  • Use spot instances or preemptible VMs for stateless workloads with fallback strategies for termination events.
  • Right-size container resource requests and limits using historical utilization data from monitoring tools.
  • Optimize storage costs by tiering data across storage classes and automating lifecycle policies.

Module 8: Operationalization and Team Enablement

  • Define runbooks for common incidents including pod evictions, node failures, and API throttling.
  • Establish on-call rotations with escalation paths and integrate alerts into incident management systems.
  • Conduct blameless postmortems to document root causes and track remediation actions.
  • Standardize developer onboarding with self-service environments via internal developer portals.
  • Train operations teams on Kubernetes debugging tools (kubectl, stern, k9s) and log querying syntax.
  • Implement feedback loops from production telemetry into development backlog prioritization.
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