Description

This curriculum spans the technical and operational rigor of a multi-workshop cloud transformation program, addressing the same workload assessment, automated governance, and incident response practices seen in enterprise advisory engagements.

Module 1: Assessing Current-State Infrastructure and Workload Readiness

Conduct inventory audits of on-premises applications to determine cloud suitability based on dependencies, licensing, and data residency constraints.
Evaluate legacy system integration points to identify refactoring requirements before migration.
Classify workloads by criticality, compliance needs, and performance sensitivity to prioritize migration sequence.
Map application data flows to uncover hidden interdependencies that could disrupt deployment timelines.
Engage application owners to validate uptime requirements and acceptable downtime windows during cutover.
Document technical debt in existing environments that may delay cloud readiness or require remediation pre-migration.

Module 2: Designing Cloud Landing Zones with Scalable Governance

Define organizational units and service control policies in AWS Organizations or Azure Management Groups to enforce baseline security.
Implement multi-account or multi-subscription strategies aligned with business units while minimizing operational overhead.
Configure centralized logging and monitoring accounts to aggregate CloudTrail, VPC Flow Logs, and security events.
Select identity federation models (e.g., SAML 2.0, OIDC) that integrate with existing enterprise directories without duplicating user stores.
Establish network topology standards (e.g., hub-and-spoke, mesh) with shared transit gateways or virtual WANs.
Standardize naming conventions and tagging policies to enable cost allocation and resource tracking across teams.

Module 3: Automating Provisioning with Infrastructure as Code (IaC)

Select IaC tools (e.g., Terraform, AWS CloudFormation, Azure Bicep) based on team expertise and multi-cloud requirements.
Structure modular templates with reusable components for VPCs, subnets, and IAM roles to reduce duplication.
Implement state file management using remote backends with locking to prevent configuration drift.
Integrate IaC pipelines with version control (e.g., Git) and enforce pull request reviews for production changes.
Validate configurations using static analysis tools (e.g., Checkov, tfsec) before deployment to catch policy violations.
Manage secrets using dedicated services (e.g., AWS Secrets Manager, HashiCorp Vault) instead of hardcoding in templates.

Module 4: Optimizing CI/CD Pipelines for Cloud-Native Deployments

Design stage-specific deployment strategies (e.g., blue/green, canary) based on application risk tolerance and rollback requirements.
Integrate automated security scanning (SAST/DAST) into CI pipelines to enforce compliance before production promotion.
Configure pipeline permissions using least-privilege roles to prevent unauthorized infrastructure changes.
Cache build dependencies in private artifact repositories to reduce pipeline execution time.
Orchestrate parallel testing across environments (dev, test, staging) to shorten feedback loops.
Implement deployment gates that require manual approval for production releases based on change advisory board (CAB) policies.

Module 5: Managing Data Migration and Synchronization

Choose between online and offline data transfer methods (e.g., AWS Snowball, Azure Data Box) based on data volume and network bandwidth.

Pre-stage database schema transformations to minimize downtime during cutover windows.

Use change data capture (CDC) tools to maintain source-target synchronization during extended migration phases.

Validate data consistency post-migration using checksums and row-count reconciliation scripts.

Plan for cross-region replication requirements to meet disaster recovery objectives.

Address encryption key management during migration, ensuring KMS or BYOK policies are applied consistently.

Module 6: Enforcing Security and Compliance at Scale

Deploy automated compliance checks using configuration rules (e.g., AWS Config, Azure Policy) to detect non-conformant resources.
Integrate vulnerability scanning into deployment pipelines to block images with critical CVEs.
Implement network segmentation using security groups and NSGs with least-permissive rules.
Centralize threat detection using cloud-native SIEM solutions (e.g., AWS GuardDuty, Azure Sentinel).
Rotate credentials and API keys automatically using scheduled workflows and secret rotation policies.
Conduct regular access reviews to remove stale IAM permissions and enforce just-in-time access.

Module 7: Monitoring, Observability, and Performance Tuning

Instrument applications with distributed tracing (e.g., AWS X-Ray, Azure Application Insights) to diagnose latency bottlenecks.
Define custom metrics and alarms for business-critical transactions beyond infrastructure health.
Correlate logs, metrics, and traces in a centralized observability platform to reduce mean time to resolution (MTTR).
Set dynamic scaling policies based on actual workload patterns rather than static thresholds.
Conduct load testing in pre-production to validate auto-scaling group behavior under peak demand.
Optimize cloud spend by identifying underutilized instances and rightsizing based on performance telemetry.

Module 8: Establishing Operational Runbooks and Incident Response

Document standard operating procedures (SOPs) for common failure scenarios (e.g., AZ outage, DNS misconfiguration).
Define escalation paths and on-call rotations for cloud-specific incidents outside traditional ITIL frameworks.
Integrate incident management tools (e.g., PagerDuty, Opsgenie) with cloud monitoring alerts to trigger response workflows.
Conduct blameless post-mortems for production incidents to update runbooks and prevent recurrence.
Simulate disaster recovery scenarios using automated failover tests for critical applications.
Train L1 support teams on cloud console navigation and log querying to reduce dependency on specialized engineers.