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Application Delivery in Cloud Migration

Application Delivery in Cloud Migration

$249.00
How you learn:
Self-paced • Lifetime updates
Toolkit Included:
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, addressing the same decision frameworks and implementation challenges encountered in enterprise advisory engagements for application modernization.

Module 1: Defining Application Delivery Strategy in Cloud Migration

  • Selecting between rehost, refactor, rearchitect, or replace strategies for each application based on technical debt, business criticality, and SLA requirements.
  • Establishing application ownership and accountability across business units, IT, and cloud providers to prevent governance gaps during migration.
  • Mapping application dependencies using discovery tools to avoid breaking integrations during lift-and-shift or partial migrations.
  • Defining acceptable downtime windows and rollback procedures for each application tier during migration cutover.
  • Aligning application delivery timelines with enterprise change management calendars to minimize business disruption.
  • Documenting compliance and data residency constraints that influence cloud region selection and deployment topology.

Module 2: Infrastructure as Code and Environment Standardization

  • Choosing between Terraform, AWS CloudFormation, or Azure Bicep based on multi-cloud needs, team expertise, and toolchain integration.
  • Designing reusable module templates for network, compute, and storage that enforce naming conventions and tagging policies.
  • Implementing version control workflows for IaC with peer review, automated testing, and drift detection.
  • Managing state file security and access controls in distributed teams to prevent unauthorized infrastructure changes.
  • Integrating IaC pipelines with configuration management tools like Ansible or Puppet for consistent OS-level setup.
  • Enforcing environment parity by using identical configurations across dev, test, and production with parameterized overrides.

Module 3: CI/CD Pipeline Design for Migrated Workloads

  • Selecting pipeline orchestration tools (e.g., Jenkins, GitLab CI, GitHub Actions) based on existing DevOps maturity and artifact repository integration.
  • Securing pipeline secrets using vault integration instead of hardcoded credentials in build scripts.
  • Implementing automated canary analysis using metrics and logs to gate production deployments.
  • Configuring artifact promotion workflows that require approval steps between environments for regulated applications.
  • Integrating static code analysis and container scanning into pre-deployment stages to enforce security policies.
  • Designing pipeline concurrency and resource throttling to prevent cloud cost spikes during peak development cycles.

Module 4: Cloud Networking and Connectivity Patterns

  • Choosing between VPC peering, transit gateways, or cloud provider interconnects based on latency, cost, and scalability requirements.
  • Designing DNS strategy to support hybrid environments with on-premises and cloud-resident services.
  • Implementing private service endpoints to prevent public exposure of backend APIs and databases.
  • Configuring firewall rules and security groups using the principle of least privilege for inter-service communication.
  • Planning bandwidth allocation and QoS for applications with real-time data transfer needs across regions.
  • Validating failover paths for network connectivity during provider outages or backbone disruptions.

Module 5: Data Migration and Synchronization Strategy

  • Selecting between online or offline data transfer methods (e.g., AWS Snowball, Azure Data Box) based on data volume and network capacity.
  • Scheduling cutover synchronization windows to minimize data drift between source and target databases.
  • Validating referential integrity and data consistency after migration using automated checksum and row-count verification.
  • Handling identity and access mapping when replicating directory services to cloud identity providers.
  • Designing retry and error-handling logic for batch data pipelines to manage transient network failures.
  • Implementing data masking or anonymization during test environment population from production datasets.

Module 6: Performance Optimization and Scalability Engineering

  • Tuning auto-scaling policies using historical load patterns and predictive analytics to avoid over-provisioning.
  • Configuring caching layers (e.g., Redis, Cloud CDN) to reduce backend load and improve response times for stateless applications.
  • Right-sizing compute instances based on actual CPU, memory, and I/O utilization rather than on-premises equivalents.
  • Optimizing database query performance through indexing, partitioning, and connection pooling in cloud environments.
  • Implementing circuit breakers and bulkheads in microservices to prevent cascading failures during traffic spikes.
  • Monitoring cold start impact on serverless functions and adjusting memory allocation or provisioned concurrency accordingly.

Module 7: Observability and Incident Response in Cloud Environments

  • Centralizing logs from cloud services, containers, and applications into a single platform (e.g., ELK, Datadog, Splunk).
  • Defining baseline metrics and dynamic thresholds for alerting to reduce false positives in fluctuating workloads.
  • Correlating distributed traces across microservices to identify performance bottlenecks in request flows.
  • Implementing structured logging with consistent schema to enable automated parsing and analysis.
  • Designing runbooks with cloud-specific recovery steps for common failure scenarios like AZ outages or IAM misconfigurations.
  • Conducting blameless post-mortems after incidents to update monitoring coverage and prevent recurrence.

Module 8: Cost Governance and FinOps Integration

  • Allocating cloud spend to business units using cost allocation tags and enforcing tagging compliance through policy-as-code.
  • Comparing reserved instances versus spot instances for stateful and stateless workloads based on availability requirements.
  • Setting up budget alerts and automated shutdown policies for non-production environments to control waste.
  • Negotiating enterprise discount programs (e.g., AWS Enterprise Discount Program) based on projected 3-year usage.
  • Conducting monthly cost reviews with engineering teams to identify underutilized resources and optimize configurations.
  • Integrating cloud cost data into existing financial reporting systems for accurate chargeback or showback models.
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