Description

This curriculum spans the equivalent of a multi-workshop technical engagement, covering the same breadth and sequence of activities as an end-to-end cloud migration program for complex web applications, from discovery and refactoring to governance and operational handover.

Module 1: Assessment and Discovery of Legacy Web Applications

Inventory and classify web applications based on usage patterns, dependencies, and business criticality to determine migration priority.
Conduct dependency mapping to identify integrations with databases, third-party APIs, internal services, and authentication systems.
Evaluate application architecture for statefulness, session management, and hardcoded configurations that impede cloud portability.
Assess compliance and data residency requirements that may restrict cloud region selection or data handling practices.
Engage application owners to validate uptime expectations and define acceptable downtime windows during migration.
Determine technical debt and code quality issues that may require refactoring prior to migration.

Module 2: Cloud Target Architecture Design

Select appropriate cloud deployment models (IaaS, PaaS, serverless) based on application characteristics and operational ownership preferences.
Design VPC topology, subnets, and routing rules to isolate web, application, and database tiers while enabling secure inter-tier communication.
Define auto-scaling policies and load balancing strategies aligned with traffic patterns and performance SLAs.
Integrate with centralized identity providers using federation (e.g., SAML, OIDC) for consistent access control.
Architect for high availability by distributing instances across availability zones and configuring health checks.
Plan for DNS cutover using TTL reduction and blue-green routing strategies to minimize user impact.

Module 3: Data Migration and Database Modernization

Choose between homogeneous and heterogeneous database migration tools based on source and target database engines.
Implement change data capture (CDC) to synchronize on-premises databases with cloud replicas during extended migration windows.
Convert legacy database schemas to leverage cloud-native features such as managed instances or read replicas.
Validate referential integrity and data consistency post-migration using checksums and reconciliation scripts.
Address latency-sensitive queries by optimizing connection pooling and selecting regionally proximate database instances.
Establish backup and point-in-time recovery mechanisms aligned with RPO and RTO requirements.

Module 4: Application Refactoring and Replatforming

Refactor monolithic applications to externalize session state into managed services like Redis or DynamoDB.
Replace hardcoded IP addresses and on-premises DNS entries with cloud service discovery mechanisms.
Containerize applications using Docker and orchestrate via Kubernetes when portability and scalability are critical.
Modify logging mechanisms to forward to centralized cloud logging services instead of local file systems.
Update configuration management to use cloud parameter stores or secrets managers instead of local config files.
Adjust retry logic and timeout settings to accommodate variable network conditions in cloud environments.

Module 5: Security, Compliance, and Identity Integration

Implement network security groups and web application firewalls (WAF) to restrict traffic to known patterns and block common exploits.
Enforce encryption at rest and in transit using managed key services and TLS termination at load balancers.
Integrate with enterprise IAM systems to synchronize user roles and enforce least-privilege access to cloud resources.
Conduct vulnerability scanning of migrated instances and containers to detect misconfigurations or outdated software.
Establish audit trails by enabling cloud provider logging and forwarding logs to SIEM systems for monitoring.
Align resource tagging policies with organizational compliance frameworks for cost tracking and access governance.

Module 6: Testing, Cutover, and Traffic Migration

Execute performance testing under simulated load to validate scalability and response times in the cloud environment.
Conduct end-to-end integration tests to confirm functionality of external service calls and API integrations.
Perform user acceptance testing (UAT) with business stakeholders to validate functional correctness post-migration.
Coordinate DNS cutover timing with global TTL expiration to ensure consistent resolution across user locations.
Implement blue-green deployment to route a subset of production traffic to the cloud instance before full cutover.
Establish rollback procedures, including DNS reversion and database sync reversal, in case of critical post-cutover defects.

Module 7: Post-Migration Optimization and Operations

Right-size compute instances based on actual CPU, memory, and I/O utilization observed in production.
Implement auto-remediation for common failure scenarios using health checks and automated instance replacement.
Configure cost allocation tags and set up budget alerts to monitor and control cloud spending.
Optimize storage tiers by moving infrequently accessed logs or backups to lower-cost object storage.
Refine monitoring dashboards to include business-relevant KPIs alongside infrastructure metrics.
Document runbooks and hand over operational responsibilities to support teams with clear escalation paths.

Module 8: Governance, Change Management, and Continuous Improvement

Establish a cloud center of excellence (CCoE) to standardize migration practices and enforce architectural guardrails.
Implement infrastructure as code (IaC) using Terraform or CloudFormation to ensure reproducible environments.
Conduct post-mortems after migration events to capture lessons learned and update standard operating procedures.
Define change approval workflows for production environment modifications to prevent configuration drift.
Monitor for configuration non-compliance using automated policy engines like AWS Config or Azure Policy.
Plan iterative improvements such as serverless transformation or AI-driven cost optimization in subsequent phases.