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Cloud Native Applications in Application Development

Cloud Native Applications in Application Development

$248.00
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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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Course access is prepared after purchase and delivered via email
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What does the Cloud Native Applications in Application Development course cover?

Cloud Native Applications in Application Development is covered here in 8 modules: Architectural Foundations of Cloud Native Systems, Containerization and Orchestration at Scale, Continuous Delivery and GitOps Practices and 5 more. The outline lists 48 specific topics, opening with selecting between monolithic decomposition and greenfield microservices based on team maturity and system criticality.

How do you approach Cloud Native Applications in Application Development step by step?

The work is sequenced in 8 stages. It starts with Architectural Foundations of Cloud Native Systems, moves through Containerization and Orchestration at Scale and Continuous Delivery and GitOps Practices, and ends at Cost Management and Resource Optimization. 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 Application Development course?

Module 1 is Architectural Foundations of Cloud Native Systems. It works through selecting between monolithic decomposition and greenfield microservices based on team maturity and system criticality., defining service boundaries using domain-driven design (DDD) to align with business capabilities and reduce coupling., implementing API gateways to manage routing, authentication, and rate limiting across heterogeneous backend services. and 3 more.

How is the Cloud Native Applications in Application Development course delivered?

The Cloud Native Applications in Application Development 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 Application Development course cost?

The Cloud Native Applications in Application Development course is $247 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: Native Application Toolkit, Native Applications Toolkit, Native Cloud Application Toolkit, Cloud Native Applications in DevOps.

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 transformation program, addressing the same architectural, security, and platform decisions faced during large-scale internal developer platform rollouts and cross-team service modernization efforts.

Module 1: Architectural Foundations of Cloud Native Systems

  • Selecting between monolithic decomposition and greenfield microservices based on team maturity and system criticality.
  • Defining service boundaries using domain-driven design (DDD) to align with business capabilities and reduce coupling.
  • Implementing API gateways to manage routing, authentication, and rate limiting across heterogeneous backend services.
  • Choosing synchronous (REST/gRPC) versus asynchronous (message queues) communication based on latency and reliability requirements.
  • Evaluating the operational overhead of service mesh adoption for observability, security, and traffic control.
  • Designing for disposability by ensuring services start quickly and terminate gracefully to support autoscaling and rolling updates.

Module 2: Containerization and Orchestration at Scale

  • Standardizing container images using base image policies and vulnerability scanning in CI pipelines.
  • Configuring Kubernetes resource requests and limits to balance performance, cost, and cluster utilization.
  • Implementing pod disruption budgets to maintain availability during node maintenance or cluster upgrades.
  • Managing configuration and secrets using Kubernetes ConfigMaps and external secret managers like HashiCorp Vault.
  • Designing multi-tenant namespaces with network policies and resource quotas to isolate workloads.
  • Automating cluster lifecycle management using infrastructure-as-code tools like Terraform or Pulumi.

Module 3: Continuous Delivery and GitOps Practices

  • Structuring Git repository layouts (mono-repo vs. multi-repo) to support independent service deployment and team autonomy.
  • Implementing canary deployments with traffic shifting using service mesh or ingress controllers.
  • Enforcing deployment approvals and rollback mechanisms for production environments via policy engines.
  • Integrating security scanning (SAST, DAST, SCA) into CI pipelines without introducing unacceptable delays.
  • Using GitOps tools like Argo CD to reconcile desired state and detect configuration drift in clusters.
  • Managing Helm chart versioning and dependency updates across multiple environments and teams.

Module 4: Observability and Runtime Intelligence

  • Instrumenting applications with structured logging to enable correlation across distributed transactions.
  • Configuring distributed tracing with context propagation to identify latency bottlenecks in service chains.
  • Designing custom metrics and dashboards that reflect business KPIs, not just infrastructure health.
  • Setting meaningful alert thresholds using SLOs and error budgets to reduce alert fatigue.
  • Implementing log sampling strategies to control costs in high-throughput systems.
  • Integrating observability data with incident response tools to streamline root cause analysis.

Module 5: Resilience and Fault Tolerance Engineering

  • Implementing circuit breakers and bulkheads to prevent cascading failures during downstream outages.
  • Designing retry strategies with exponential backoff and jitter to avoid thundering herd problems.
  • Conducting regular chaos engineering experiments to validate recovery procedures in production-like environments.
  • Ensuring stateful services use persistent storage with appropriate backup and restore workflows.
  • Using health checks (liveness and readiness probes) to guide Kubernetes restart and routing decisions.
  • Planning for region failover by replicating critical services and data with acceptable RPO and RTO.

Module 6: Security and Compliance in Distributed Systems

  • Enforcing zero-trust network policies using service mesh or CNI plugins to restrict inter-service communication.
  • Managing identity and access for workloads using short-lived tokens and workload identity federation.
  • Implementing pod security standards through admission controllers to prevent privilege escalation.
  • Conducting regular compliance audits of container images and runtime configurations using automated tooling.
  • Encrypting data in transit with mTLS across service mesh and external endpoints.
  • Integrating security posture management tools to detect misconfigurations in Kubernetes manifests.

Module 7: Platform Engineering and Internal Developer Platforms

  • Defining standardized deployment templates to reduce configuration drift and onboarding time.
  • Building self-service portals for environment provisioning with guardrails for cost and compliance.
  • Integrating feedback loops from operations into developer workflows via embedded observability.
  • Managing API documentation and contract testing to ensure backward compatibility across teams.
  • Operating a catalog of reusable components and managed services to reduce duplication.
  • Measuring platform effectiveness using DORA metrics without incentivizing gaming of the system.

Module 8: Cost Management and Resource Optimization

  • Right-sizing container resources using historical usage data and vertical pod autoscalers.
  • Implementing spot instance usage with workload tolerance for interruptions and fallback strategies.
  • Tagging cloud resources by team, project, and environment to enable cost allocation and accountability.
  • Automating scale-to-zero for non-production workloads during off-hours to reduce spend.
  • Monitoring egress costs and optimizing data transfer patterns between regions and services.
  • Conducting regular cost reviews with engineering teams to align technical decisions with budget constraints.
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