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Cloud Native Applications in DevOps

Cloud Native Applications in DevOps

$251.00
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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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What does the Cloud Native Applications in DevOps course cover?

Cloud Native Applications in DevOps is covered here in 8 modules: Architecting Cloud-Native Application Foundations, Continuous Integration and Delivery Pipeline Design, Infrastructure as Code and Environment Management and 5 more. The outline lists 48 specific topics, opening with selecting containerization strategies between full OS containers and application-level isolation based on legacy dependencies and security requirements.

How do you approach Cloud Native Applications in DevOps step by step?

The work is sequenced in 8 stages. It starts with Architecting Cloud-Native Application Foundations, moves through Continuous Integration and Delivery Pipeline Design and Infrastructure as Code and Environment Management, and ends at Cost Optimization and Resource Efficiency. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Cloud Native Applications in DevOps course?

Module 1 is Architecting Cloud-Native Application Foundations. It works through selecting containerization strategies between full OS containers and application-level isolation based on legacy dependencies and security requirements., defining service boundaries in a microservices architecture using domain-driven design to minimize coupling and enable independent deployments., implementing service discovery mechanisms in dynamic environments where IP addresses change frequently across restarts and scaling events.

How is the Cloud Native Applications in DevOps course delivered?

The Cloud Native Applications in DevOps course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Cloud Native Applications in DevOps course cost?

The Cloud Native Applications in DevOps course is $250 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Cloud Native DevOps for AI-Driven Enterprises, AI-Powered Cloud Native DevOps for Enterprise Scalability, Cloud Native DevOps Engineering Foundations in enterprise.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical and operational rigor of a multi-workshop cloud modernization program, addressing the same architectural, security, and operational challenges encountered in enterprise-scale DevOps transformations.

Module 1: Architecting Cloud-Native Application Foundations

  • Selecting containerization strategies between full OS containers and application-level isolation based on legacy dependencies and security requirements.
  • Defining service boundaries in a microservices architecture using domain-driven design to minimize coupling and enable independent deployments.
  • Implementing service discovery mechanisms in dynamic environments where IP addresses change frequently across restarts and scaling events.
  • Choosing between serverless functions and long-running containers based on workload predictability, cold-start tolerance, and cost models.
  • Designing multi-region deployment blueprints to meet data residency regulations without sacrificing failover capabilities.
  • Integrating configuration management systems that separate environment-specific values from code while maintaining auditability and access controls.

Module 2: Continuous Integration and Delivery Pipeline Design

  • Structuring parallel CI stages to validate infrastructure-as-code templates alongside application builds using policy-as-code tools.
  • Implementing artifact promotion workflows that enforce version immutability and traceability from development to production.
  • Configuring pipeline concurrency limits to prevent resource saturation during high-frequency commits in large teams.
  • Embedding security scanning tools in CI with defined failure thresholds that balance risk detection and developer velocity.
  • Designing rollback mechanisms that work across both application and infrastructure changes without relying on manual intervention.
  • Managing pipeline secrets using short-lived tokens and dynamic credential injection instead of static credentials in build environments.

Module 3: Infrastructure as Code and Environment Management

  • Enforcing IaC module versioning and dependency pinning to prevent unintended changes from upstream updates.
  • Implementing environment parity by codifying differences in configuration rather than structure across dev, staging, and prod.
  • Applying drift detection workflows that alert on manual changes while allowing emergency overrides with post-incident reconciliation.
  • Designing reusable IaC modules that abstract cloud provider nuances while exposing necessary customization points for compliance.
  • Integrating IaC validation into pull requests using static analysis tools to catch misconfigurations before deployment.
  • Managing state file access and locking in distributed teams to prevent concurrent modifications that corrupt infrastructure state.

Module 4: Observability and Runtime Monitoring

  • Instrumenting distributed tracing with context propagation across service boundaries to diagnose latency bottlenecks in async workflows.
  • Configuring log sampling strategies to reduce volume costs while preserving diagnostic fidelity for error conditions.
  • Defining service-level objectives with measurable indicators that trigger alerts before user impact occurs.
  • Correlating metrics, logs, and traces using a shared identifier to reconstruct user transaction paths across microservices.
  • Implementing synthetic monitoring that validates critical user journeys at regular intervals across regions.
  • Managing retention policies for observability data based on regulatory requirements and operational debugging needs.

Module 5: Security and Compliance in DevOps Workflows

  • Integrating vulnerability scanning for container images into CI with policy gates that vary by deployment environment.
  • Enforcing least-privilege access for CI/CD service accounts across cloud and container orchestration platforms.
  • Implementing automated compliance checks for infrastructure configurations using frameworks like OpenSCAP or Rego.
  • Designing audit trails that capture who changed what, when, and through which pipeline execution across all environments.
  • Managing certificate lifecycle automation for internal services using short-lived certificates with automatic renewal.
  • Segmenting network traffic between services using service mesh or cloud-native firewall rules based on zero-trust principles.

Module 6: Scalability, Resilience, and Failover Engineering

  • Configuring horizontal pod autoscaling based on custom metrics derived from application-specific performance indicators.
  • Implementing circuit breakers and bulkheads in service communication to prevent cascading failures during dependency outages.
  • Designing database sharding and connection pooling strategies that scale under high-concurrency workloads.
  • Testing failover procedures across availability zones with controlled network partitioning to validate recovery time objectives.
  • Setting up graceful degradation paths that disable non-critical features during resource constraints to preserve core functionality.
  • Managing backpressure in event-driven systems by regulating message consumption rates and queue depth thresholds.

Module 7: Team Topologies and DevOps Governance

  • Defining ownership models for shared platforms using internal developer portals with clear SLAs and support channels.
  • Establishing change advisory boards for production deployments that balance speed and risk without creating bottlenecks.
  • Implementing feature flagging systems with kill switches and gradual rollouts to decouple deployment from release.
  • Creating feedback loops between operations and development teams using incident postmortems that drive product improvements.
  • Standardizing logging and monitoring contracts that services must implement to be onboarded to production environments.
  • Managing technical debt in CI/CD pipelines by scheduling refactoring windows and tracking pipeline reliability metrics.

Module 8: Cost Optimization and Resource Efficiency

  • Right-sizing container resource requests and limits based on historical usage patterns to avoid overprovisioning.
  • Implementing auto-scaling of CI/CD runners to match build load, reducing idle compute during off-peak hours.
  • Using spot instances or preemptible VMs for stateless workloads with checkpointing and retry logic to handle interruptions.
  • Tagging cloud resources with cost center, project, and owner metadata to enable chargeback and showback reporting.
  • Archiving cold data to lower-cost storage tiers while maintaining compliance with data retention policies.
  • Conducting regular cost reviews that correlate usage metrics with business value to identify underutilized services.
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