This curriculum spans the technical and procedural rigor of a multi-workshop DevOps transformation program, addressing the same infrastructure, security, and operational disciplines applied in enterprise-scale production environments.
Module 1: Infrastructure as Code (IaC) Strategy and Implementation
- Selecting between declarative (e.g., Terraform) and imperative (e.g., Ansible) IaC tools based on team skill sets and change control requirements.
- Designing reusable, parameterized IaC modules with versioned dependencies to support consistent multi-environment deployments.
- Enforcing IaC peer review policies in pull requests to prevent configuration drift and unauthorized resource provisioning.
- Integrating IaC scanning tools (e.g., Checkov, tfsec) into CI pipelines to detect security misconfigurations before deployment.
- Managing state files securely in remote backends with role-based access and audit logging, avoiding local state in production workflows.
- Planning for immutable infrastructure patterns versus mutable updates when managing long-running production workloads.
Module 2: CI/CD Pipeline Design for Production Safety
- Implementing canary deployments with traffic shifting via service mesh or load balancer rules to reduce blast radius.
- Configuring automated rollback triggers based on health checks, error rates, or latency thresholds in monitoring systems.
- Requiring manual approval gates for production promotions while maintaining audit trails and role-based authorization.
- Enforcing artifact immutability by promoting the same build artifact across environments using versioned identifiers.
- Securing pipeline secrets using dedicated secret management tools (e.g., HashiCorp Vault) instead of environment variables.
- Isolating production pipeline stages with network segmentation and minimal privilege service accounts.
Module 3: Production Monitoring and Observability
- Defining SLOs and error budgets to guide incident response and feature release pacing in production systems.
- Instrumenting distributed tracing across microservices using context propagation to diagnose latency bottlenecks.
- Configuring alerting rules to minimize noise by focusing on user-impacting metrics rather than infrastructure-level thresholds.
- Centralizing logs with structured formatting and retention policies aligned with compliance requirements.
- Correlating metrics, logs, and traces using unique request identifiers to accelerate root cause analysis.
- Validating monitoring coverage during deployment by verifying new services are auto-discovered and scraped.
Module 4: Security and Compliance in Production Systems
- Enforcing runtime security policies using OPA or Kyverno to block non-compliant container deployments.
- Implementing network policies in Kubernetes to restrict pod-to-pod communication based on least privilege.
- Conducting regular vulnerability scans of container images and patching within defined SLAs for critical findings.
- Rotating production secrets and certificates automatically using tools like Vault or AWS Secrets Manager.
- Enabling audit logging for all production API calls and storing logs in immutable, write-once storage.
- Mapping controls to compliance frameworks (e.g., SOC 2, ISO 27001) and automating evidence collection.
Module 5: Disaster Recovery and High Availability Planning
- Defining RPO and RTO targets for each production service and designing backup strategies accordingly.
- Testing failover procedures regularly in staging environments to validate cross-region redundancy.
- Automating backup validation by restoring snapshots to isolated environments and verifying data integrity.
- Architecting stateful services with distributed databases that support multi-region replication and quorum reads.
- Documenting and versioning runbooks for critical failure scenarios, including escalation paths and communication protocols.
- Using chaos engineering tools to inject failures (e.g., node shutdown, latency spikes) and validate system resilience.
Module 6: Change Management and Production Governance
- Requiring change advisory board (CAB) review for high-risk changes while allowing low-risk changes to proceed via automated checks.
- Tracking all production changes in a centralized system with metadata such as change owner, impact level, and rollback plan.
- Enforcing a production change freeze window during peak business periods or critical events.
- Integrating deployment tracking with ITSM tools to maintain alignment with enterprise change management processes.
- Conducting post-implementation reviews for failed or impactful changes to update policies and prevent recurrence.
- Standardizing change templates to ensure consistent risk assessment and stakeholder notification.
Module 7: Capacity Planning and Performance Optimization
- Forecasting resource demand using historical usage trends and business growth projections to guide scaling decisions.
- Right-sizing cloud instances based on actual CPU, memory, and I/O utilization rather than default configurations.
- Implementing autoscaling policies with cooldown periods and predictive scaling to handle traffic spikes efficiently.
- Monitoring for resource contention in shared environments (e.g., noisy neighbors in multi-tenant clusters).
- Optimizing database performance through indexing strategies, query tuning, and read replica placement.
- Conducting load testing in pre-production environments with production-like data and traffic patterns.
