Mastering Chaos Engineering for Resilient Systems
You’re one outage away from a board meeting you didn’t want. Your systems are complex. Interdependencies hidden in layers of microservices, APIs, and third-party integrations. And no matter how many monitoring tools you deploy, failure still strikes-unpredictable, cascading, and expensive. You’re not just managing infrastructure. You’re managing risk. And when the next incident hits, you need more than firefighting skills. You need a proactive strategy that turns uncertainty into resilience. Mastering Chaos Engineering for Resilient Systems is the only structured framework designed to take you from reactive fire mode to strategic control-equipping you to build systems so robust, they withstand real-world chaos before it impacts users. This is not theory. One lead engineer at a Fortune 500 fintech implemented these methods and reduced critical system downtime by 73% within six weeks-measurable, business-impacting results that earned her a promotion to SRE Lead. You already know instability costs more than investment. Missed SLAs, eroded customer trust, engineering burnout. This course eliminates guesswork and delivers a repeatable, proven blueprint for resilience. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-paced. Immediate online access. Zero time pressure. This course is fully on-demand, allowing you to progress anytime, anywhere-without rigid schedules or fixed start dates. Most learners complete the core curriculum in 4–6 weeks, spending just 3–5 hours per week, while applying each principle directly to their production environments. You gain lifetime access to all materials, including every future update at no additional cost. As chaos engineering tools and practices evolve, your access evolves with them-ensuring your expertise remains sharp and relevant for years to come. 24/7 Global & Mobile-Friendly Access
The course platform is engineered for reliability and accessibility. Whether you’re reviewing a resilience checklist from your laptop during work hours or studying failure mode patterns on your phone during transit, the layout adapts perfectly to any device. - Progress syncs across devices automatically
- Offline-ready content downloads available
- Intuitive navigation for fast topic retrieval
Instructor Support & Expert Guidance
You’re not learning in isolation. Throughout the course, you’ll have direct access to expert-led guidance through structured Q&A forums, curated implementation templates, and weekly-reviewed support threads moderated by certified chaos engineering practitioners. Expect timely, context-aware responses tailored to your environment-whether you’re working with Kubernetes, serverless functions, legacy monoliths, or hybrid cloud architectures. Certificate of Completion from The Art of Service
Upon finishing the course and passing the final assessment, you will receive a globally recognised Certificate of Completion issued by The Art of Service, a leader in professional engineering education trusted by enterprises and engineers in over 120 countries. This certificate validates your mastery of chaos engineering principles and strengthens your credibility in roles such as Site Reliability Engineer, DevOps Architect, Platform Lead, and Engineering Manager. No Hidden Fees. Transparent Pricing.
The price you see is the price you pay-no surprises, no add-ons, no recurring charges beyond the one-time fee. You pay once, you own it for life. We accept all major payment methods, including Visa, Mastercard, and PayPal, with encrypted transactions to ensure security. You’re 100% Protected by Our Satisfied or Refunded Guarantee
Start the course with complete confidence. If you find within 30 days that the content does not meet your expectations or fails to deliver actionable value, simply request a full refund. No questions asked. No hoops to jump through. This is our promise: you take zero financial risk. Enrollment Confirmation & Access Flow
After enrollment, you will receive a confirmation email. Your course access details will be sent separately once your materials are fully provisioned-ensuring all resources are ready for a seamless learning experience. “Will This Work for Me?” - We Know the Doubts
You might be thinking: I’m not at Netflix. I don’t run thousands of services. My team is small. My budget is tight. My system wasn’t built for chaos testing. But here’s what you need to know: this course was designed for realistic environments-not unicorn-scale tech giants. It works even if: - You’re managing a hybrid system with partial observability
- You lack dedicated SRE resources
- Your management resists change due to perceived risk
- You’ve never run a single controlled experiment
A mid-level DevOps engineer at a healthcare SaaS provider used this course to design and run his first chaos experiment in just 11 days-using only open-source tools and existing monitoring. The result? A critical caching failure mode was uncovered and fixed before it impacted patient data delivery. The CTO now mandates chaos practice across all product teams. That shift-from uncertainty to influence-is exactly what this course delivers.
Extensive and Detailed Course Curriculum
Module 1: Foundations of Chaos Engineering - Defining chaos engineering beyond buzzwords
- The evolution of resilience in modern distributed systems
- Why traditional monitoring fails under cascading failures
- Principles of the scientific method in system testing
- Core tenets: hypothesis, control, experiment, verification
- Understanding the difference between failure testing and chaos engineering
- The critical role of observability in chaos validation
- Common misconceptions and pitfalls to avoid
- Aligning chaos practice with business objectives
- Establishing a culture of proactive resilience
- Mapping system complexity and pinpointing hidden risks
- Introduction to the resilience maturity model
- Creating a justification case for leadership buy-in
- Identifying high-impact, low-risk starting points
- Building your first resilience charter
Module 2: Designing Chaos Experiments - Establishing a steady state definition
- How to define measurable success criteria
- Formulating testable resilience hypotheses
- Selecting appropriate experimental scope and boundaries
- Choosing between targeted and emergent failure patterns
- Mapping dependencies and failure propagation paths
- Creating failure injection blueprints
- Defining blast radius constraints
- Integrating pre-experiment health checks
- Using monitoring baselines to validate system behaviour
- Designing rollback and recovery triggers
- Documenting assumptions and constraints
- Reviewing experiment design with cross-functional teams
- Obtaining stakeholder sign-off without resistance
- Building a reusable experiment design template
Module 3: Chaos Engineering Frameworks & Methodologies - Comparing established chaos frameworks: principles vs implementation
- Adapting the Netflix Simian Army model for real-world teams
- Implementing the GameDay methodology for team readiness
- The Chaos Toolkit open standard and its architecture
- Integrating chaos into incident response playbooks
- Using the Resilience Scorecard to measure progress
- Applying the Failure Mode and Effects Analysis (FMEA) framework
- Linking chaos objectives to SLOs and error budgets
- Adopting incremental adoption: crawl, walk, run approach
- Creating a Chaos Charter for organisational alignment
- Developing a Chaos Policy with escalation paths
- Integrating resilience into software development lifecycles
- Using chaos to validate deployment strategies
- Leveraging chaos for cloud migration assurance
- Measuring resilience improvements over time
Module 4: Tools & Platforms for Chaos Execution - Overview of open-source and commercial chaos tools
- Installing and configuring Chaos Monkey
- Leveraging Gremlin for controlled failure injection
- Using Litmus for Kubernetes-native chaos engineering
- Implementing chaos with custom scripts and APIs
- Integrating tools with CI/CD pipelines
- Setting up Chaos Center for experiment orchestration
- Using Toxiproxy for network-level failure simulation
- Configuring latency, packet loss, and disconnection patterns
- Simulating resource exhaustion: CPU, memory, disk I/O
- Testing container orchestration failure modes
- Validating autoscaling responses under stress
- Testing cloud provider failure zones and regions
- Using service mesh tools like Istio for traffic manipulation
- Automating experiment scheduling and validation
Module 5: Observability & Monitoring for Chaos Validation - Designing observability layers for chaos experiments
- Instrumenting applications with chaos-aware metrics
- Using Prometheus for real-time monitoring during experiments
- Creating Grafana dashboards for experimental visibility
- Identifying critical signals: latency, error rates, saturation
- Correlating logs during failure injection events
- Using distributed tracing with Jaeger or Zipkin
- Setting up alerting thresholds for safe experimentation
- Validating metric continuity during outages
- Using synthetic monitoring to assess user impact
- Mapping business KPIs to system resilience signals
- Analysing trace data for bottleneck detection
- Building observability checklists for pre-experiment reviews
- Conducting post-experiment data reviews
- Generating automated observability reports
Module 6: Failure Injection Patterns & Scenarios - Simulating node failure in clustered environments
- Testing pod eviction and rescheduling in Kubernetes
- Injecting network partitions and latency spikes
- Simulating DNS resolution failures
- Testing stateful service recovery (databases, queues)
- Validating retry logic and circuit breaker patterns
- Testing cache eviction and cold start scenarios
- Simulating API rate limiting and throttling
- Testing third-party service outage responses
- Validating authentication and session failover
- Inducing storage latency and I/O bottlenecks
- Testing message queue backpressure handling
- Simulating geo-region failover scenarios
- Inducing certificate expiration and trust chain failures
- Testing graceful degradation mechanisms
Module 7: Advanced Chaos Engineering Techniques - Progressive delivery integration: canary and feature flag chaos
- Introducing algorithmic chaos: probabilistic failure injection
- Using machine learning to predict high-risk failure paths
- Automating hypothesis generation from incident data
- Creating synthetic traffic patterns for stress testing
- Implementing continuous chaos in production
- Designing self-healing systems with chaos feedback loops
- Using chaos to validate disaster recovery runbooks
- Testing multi-cloud failover strategies
- Validating data consistency across distributed systems
- Simulating data corruption and recovery
- Testing backup integrity and restore processes
- Inducing time skew and clock drift in distributed nodes
- Testing consensus algorithms under stress
- Validating distributed locking mechanisms
Module 8: Organisational Integration & Scaling - Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
Module 1: Foundations of Chaos Engineering - Defining chaos engineering beyond buzzwords
- The evolution of resilience in modern distributed systems
- Why traditional monitoring fails under cascading failures
- Principles of the scientific method in system testing
- Core tenets: hypothesis, control, experiment, verification
- Understanding the difference between failure testing and chaos engineering
- The critical role of observability in chaos validation
- Common misconceptions and pitfalls to avoid
- Aligning chaos practice with business objectives
- Establishing a culture of proactive resilience
- Mapping system complexity and pinpointing hidden risks
- Introduction to the resilience maturity model
- Creating a justification case for leadership buy-in
- Identifying high-impact, low-risk starting points
- Building your first resilience charter
Module 2: Designing Chaos Experiments - Establishing a steady state definition
- How to define measurable success criteria
- Formulating testable resilience hypotheses
- Selecting appropriate experimental scope and boundaries
- Choosing between targeted and emergent failure patterns
- Mapping dependencies and failure propagation paths
- Creating failure injection blueprints
- Defining blast radius constraints
- Integrating pre-experiment health checks
- Using monitoring baselines to validate system behaviour
- Designing rollback and recovery triggers
- Documenting assumptions and constraints
- Reviewing experiment design with cross-functional teams
- Obtaining stakeholder sign-off without resistance
- Building a reusable experiment design template
Module 3: Chaos Engineering Frameworks & Methodologies - Comparing established chaos frameworks: principles vs implementation
- Adapting the Netflix Simian Army model for real-world teams
- Implementing the GameDay methodology for team readiness
- The Chaos Toolkit open standard and its architecture
- Integrating chaos into incident response playbooks
- Using the Resilience Scorecard to measure progress
- Applying the Failure Mode and Effects Analysis (FMEA) framework
- Linking chaos objectives to SLOs and error budgets
- Adopting incremental adoption: crawl, walk, run approach
- Creating a Chaos Charter for organisational alignment
- Developing a Chaos Policy with escalation paths
- Integrating resilience into software development lifecycles
- Using chaos to validate deployment strategies
- Leveraging chaos for cloud migration assurance
- Measuring resilience improvements over time
Module 4: Tools & Platforms for Chaos Execution - Overview of open-source and commercial chaos tools
- Installing and configuring Chaos Monkey
- Leveraging Gremlin for controlled failure injection
- Using Litmus for Kubernetes-native chaos engineering
- Implementing chaos with custom scripts and APIs
- Integrating tools with CI/CD pipelines
- Setting up Chaos Center for experiment orchestration
- Using Toxiproxy for network-level failure simulation
- Configuring latency, packet loss, and disconnection patterns
- Simulating resource exhaustion: CPU, memory, disk I/O
- Testing container orchestration failure modes
- Validating autoscaling responses under stress
- Testing cloud provider failure zones and regions
- Using service mesh tools like Istio for traffic manipulation
- Automating experiment scheduling and validation
Module 5: Observability & Monitoring for Chaos Validation - Designing observability layers for chaos experiments
- Instrumenting applications with chaos-aware metrics
- Using Prometheus for real-time monitoring during experiments
- Creating Grafana dashboards for experimental visibility
- Identifying critical signals: latency, error rates, saturation
- Correlating logs during failure injection events
- Using distributed tracing with Jaeger or Zipkin
- Setting up alerting thresholds for safe experimentation
- Validating metric continuity during outages
- Using synthetic monitoring to assess user impact
- Mapping business KPIs to system resilience signals
- Analysing trace data for bottleneck detection
- Building observability checklists for pre-experiment reviews
- Conducting post-experiment data reviews
- Generating automated observability reports
Module 6: Failure Injection Patterns & Scenarios - Simulating node failure in clustered environments
- Testing pod eviction and rescheduling in Kubernetes
- Injecting network partitions and latency spikes
- Simulating DNS resolution failures
- Testing stateful service recovery (databases, queues)
- Validating retry logic and circuit breaker patterns
- Testing cache eviction and cold start scenarios
- Simulating API rate limiting and throttling
- Testing third-party service outage responses
- Validating authentication and session failover
- Inducing storage latency and I/O bottlenecks
- Testing message queue backpressure handling
- Simulating geo-region failover scenarios
- Inducing certificate expiration and trust chain failures
- Testing graceful degradation mechanisms
Module 7: Advanced Chaos Engineering Techniques - Progressive delivery integration: canary and feature flag chaos
- Introducing algorithmic chaos: probabilistic failure injection
- Using machine learning to predict high-risk failure paths
- Automating hypothesis generation from incident data
- Creating synthetic traffic patterns for stress testing
- Implementing continuous chaos in production
- Designing self-healing systems with chaos feedback loops
- Using chaos to validate disaster recovery runbooks
- Testing multi-cloud failover strategies
- Validating data consistency across distributed systems
- Simulating data corruption and recovery
- Testing backup integrity and restore processes
- Inducing time skew and clock drift in distributed nodes
- Testing consensus algorithms under stress
- Validating distributed locking mechanisms
Module 8: Organisational Integration & Scaling - Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Establishing a steady state definition
- How to define measurable success criteria
- Formulating testable resilience hypotheses
- Selecting appropriate experimental scope and boundaries
- Choosing between targeted and emergent failure patterns
- Mapping dependencies and failure propagation paths
- Creating failure injection blueprints
- Defining blast radius constraints
- Integrating pre-experiment health checks
- Using monitoring baselines to validate system behaviour
- Designing rollback and recovery triggers
- Documenting assumptions and constraints
- Reviewing experiment design with cross-functional teams
- Obtaining stakeholder sign-off without resistance
- Building a reusable experiment design template
Module 3: Chaos Engineering Frameworks & Methodologies - Comparing established chaos frameworks: principles vs implementation
- Adapting the Netflix Simian Army model for real-world teams
- Implementing the GameDay methodology for team readiness
- The Chaos Toolkit open standard and its architecture
- Integrating chaos into incident response playbooks
- Using the Resilience Scorecard to measure progress
- Applying the Failure Mode and Effects Analysis (FMEA) framework
- Linking chaos objectives to SLOs and error budgets
- Adopting incremental adoption: crawl, walk, run approach
- Creating a Chaos Charter for organisational alignment
- Developing a Chaos Policy with escalation paths
- Integrating resilience into software development lifecycles
- Using chaos to validate deployment strategies
- Leveraging chaos for cloud migration assurance
- Measuring resilience improvements over time
Module 4: Tools & Platforms for Chaos Execution - Overview of open-source and commercial chaos tools
- Installing and configuring Chaos Monkey
- Leveraging Gremlin for controlled failure injection
- Using Litmus for Kubernetes-native chaos engineering
- Implementing chaos with custom scripts and APIs
- Integrating tools with CI/CD pipelines
- Setting up Chaos Center for experiment orchestration
- Using Toxiproxy for network-level failure simulation
- Configuring latency, packet loss, and disconnection patterns
- Simulating resource exhaustion: CPU, memory, disk I/O
- Testing container orchestration failure modes
- Validating autoscaling responses under stress
- Testing cloud provider failure zones and regions
- Using service mesh tools like Istio for traffic manipulation
- Automating experiment scheduling and validation
Module 5: Observability & Monitoring for Chaos Validation - Designing observability layers for chaos experiments
- Instrumenting applications with chaos-aware metrics
- Using Prometheus for real-time monitoring during experiments
- Creating Grafana dashboards for experimental visibility
- Identifying critical signals: latency, error rates, saturation
- Correlating logs during failure injection events
- Using distributed tracing with Jaeger or Zipkin
- Setting up alerting thresholds for safe experimentation
- Validating metric continuity during outages
- Using synthetic monitoring to assess user impact
- Mapping business KPIs to system resilience signals
- Analysing trace data for bottleneck detection
- Building observability checklists for pre-experiment reviews
- Conducting post-experiment data reviews
- Generating automated observability reports
Module 6: Failure Injection Patterns & Scenarios - Simulating node failure in clustered environments
- Testing pod eviction and rescheduling in Kubernetes
- Injecting network partitions and latency spikes
- Simulating DNS resolution failures
- Testing stateful service recovery (databases, queues)
- Validating retry logic and circuit breaker patterns
- Testing cache eviction and cold start scenarios
- Simulating API rate limiting and throttling
- Testing third-party service outage responses
- Validating authentication and session failover
- Inducing storage latency and I/O bottlenecks
- Testing message queue backpressure handling
- Simulating geo-region failover scenarios
- Inducing certificate expiration and trust chain failures
- Testing graceful degradation mechanisms
Module 7: Advanced Chaos Engineering Techniques - Progressive delivery integration: canary and feature flag chaos
- Introducing algorithmic chaos: probabilistic failure injection
- Using machine learning to predict high-risk failure paths
- Automating hypothesis generation from incident data
- Creating synthetic traffic patterns for stress testing
- Implementing continuous chaos in production
- Designing self-healing systems with chaos feedback loops
- Using chaos to validate disaster recovery runbooks
- Testing multi-cloud failover strategies
- Validating data consistency across distributed systems
- Simulating data corruption and recovery
- Testing backup integrity and restore processes
- Inducing time skew and clock drift in distributed nodes
- Testing consensus algorithms under stress
- Validating distributed locking mechanisms
Module 8: Organisational Integration & Scaling - Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Overview of open-source and commercial chaos tools
- Installing and configuring Chaos Monkey
- Leveraging Gremlin for controlled failure injection
- Using Litmus for Kubernetes-native chaos engineering
- Implementing chaos with custom scripts and APIs
- Integrating tools with CI/CD pipelines
- Setting up Chaos Center for experiment orchestration
- Using Toxiproxy for network-level failure simulation
- Configuring latency, packet loss, and disconnection patterns
- Simulating resource exhaustion: CPU, memory, disk I/O
- Testing container orchestration failure modes
- Validating autoscaling responses under stress
- Testing cloud provider failure zones and regions
- Using service mesh tools like Istio for traffic manipulation
- Automating experiment scheduling and validation
Module 5: Observability & Monitoring for Chaos Validation - Designing observability layers for chaos experiments
- Instrumenting applications with chaos-aware metrics
- Using Prometheus for real-time monitoring during experiments
- Creating Grafana dashboards for experimental visibility
- Identifying critical signals: latency, error rates, saturation
- Correlating logs during failure injection events
- Using distributed tracing with Jaeger or Zipkin
- Setting up alerting thresholds for safe experimentation
- Validating metric continuity during outages
- Using synthetic monitoring to assess user impact
- Mapping business KPIs to system resilience signals
- Analysing trace data for bottleneck detection
- Building observability checklists for pre-experiment reviews
- Conducting post-experiment data reviews
- Generating automated observability reports
Module 6: Failure Injection Patterns & Scenarios - Simulating node failure in clustered environments
- Testing pod eviction and rescheduling in Kubernetes
- Injecting network partitions and latency spikes
- Simulating DNS resolution failures
- Testing stateful service recovery (databases, queues)
- Validating retry logic and circuit breaker patterns
- Testing cache eviction and cold start scenarios
- Simulating API rate limiting and throttling
- Testing third-party service outage responses
- Validating authentication and session failover
- Inducing storage latency and I/O bottlenecks
- Testing message queue backpressure handling
- Simulating geo-region failover scenarios
- Inducing certificate expiration and trust chain failures
- Testing graceful degradation mechanisms
Module 7: Advanced Chaos Engineering Techniques - Progressive delivery integration: canary and feature flag chaos
- Introducing algorithmic chaos: probabilistic failure injection
- Using machine learning to predict high-risk failure paths
- Automating hypothesis generation from incident data
- Creating synthetic traffic patterns for stress testing
- Implementing continuous chaos in production
- Designing self-healing systems with chaos feedback loops
- Using chaos to validate disaster recovery runbooks
- Testing multi-cloud failover strategies
- Validating data consistency across distributed systems
- Simulating data corruption and recovery
- Testing backup integrity and restore processes
- Inducing time skew and clock drift in distributed nodes
- Testing consensus algorithms under stress
- Validating distributed locking mechanisms
Module 8: Organisational Integration & Scaling - Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Simulating node failure in clustered environments
- Testing pod eviction and rescheduling in Kubernetes
- Injecting network partitions and latency spikes
- Simulating DNS resolution failures
- Testing stateful service recovery (databases, queues)
- Validating retry logic and circuit breaker patterns
- Testing cache eviction and cold start scenarios
- Simulating API rate limiting and throttling
- Testing third-party service outage responses
- Validating authentication and session failover
- Inducing storage latency and I/O bottlenecks
- Testing message queue backpressure handling
- Simulating geo-region failover scenarios
- Inducing certificate expiration and trust chain failures
- Testing graceful degradation mechanisms
Module 7: Advanced Chaos Engineering Techniques - Progressive delivery integration: canary and feature flag chaos
- Introducing algorithmic chaos: probabilistic failure injection
- Using machine learning to predict high-risk failure paths
- Automating hypothesis generation from incident data
- Creating synthetic traffic patterns for stress testing
- Implementing continuous chaos in production
- Designing self-healing systems with chaos feedback loops
- Using chaos to validate disaster recovery runbooks
- Testing multi-cloud failover strategies
- Validating data consistency across distributed systems
- Simulating data corruption and recovery
- Testing backup integrity and restore processes
- Inducing time skew and clock drift in distributed nodes
- Testing consensus algorithms under stress
- Validating distributed locking mechanisms
Module 8: Organisational Integration & Scaling - Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Building a Centre of Excellence for resilience
- Establishing Chaos Ambassadors across engineering teams
- Integrating chaos into post-mortem analysis
- Creating feedback loops from chaos to development
- Developing resilience training for new hires
- Scaling chaos from single services to enterprise-wide
- Creating executive reporting dashboards
- Presenting resilience metrics to non-technical stakeholders
- Aligning chaos initiatives with security and compliance
- Integrating chaos into regulatory audit processes
- Using chaos findings to improve technical debt prioritization
- Developing a resilience roadmap with milestone tracking
- Measuring ROI of resilience investments
- Establishing regular resilience review cycles
- Driving cultural change: from blame to inquiry
Module 9: Production Safety & Risk Mitigation - Defining and enforcing blast radius controls
- Implementing automated rollback triggers
- Using canary analysis to detect degradation early
- Designing safe failure injection windows
- Understanding the difference between testing and breaking
- Creating pre-experiment safety checklists
- Establishing communication protocols during experiments
- Preparing incident response teams for active chaos
- Using feature toggles to contain experimental impact
- Testing with shadow traffic to minimise user risk
- Validating system recovery without user disruption
- Using dark launches for safe chaos validation
- Implementing real-time alerting during experiments
- Assessing regulatory and compliance implications
- Documenting safety procedures for audit readiness
Module 10: Case Studies & Real-World Applications - Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Netflix: Chaos Monkey and the birth of chaos engineering
- Amazon: validating AWS resilience with controlled outages
- LinkedIn: scaling chaos across microservices
- Google: SRE practices and resilience validation
- Microsoft Azure: fault injection in global cloud services
- Fintech case study: resilience during high-transaction periods
- Healthcare SaaS: ensuring zero-downtime patient data flows
- E-commerce platform: Black Friday chaos readiness
- Telco provider: network resiliency under scale
- Startup example: building resilience with limited resources
- Cross-industry analysis of common failure patterns
- Lessons learned from failed chaos experiments
- How companies recover from unexpected chaos outcomes
- Measuring business impact of resilience investments
- Presenting real-time chaos demos for stakeholder alignment
Module 11: Chaos Engineering in Specific Environments - Chaos in Kubernetes: pod, node, and control plane failures
- Serverless environments: function timeout and cold start testing
- Legacy monoliths: isolating components for safe testing
- Hybrid cloud: synchronising chaos across on-prem and cloud
- Edge computing: simulating network instability
- IoT systems: validating device and gateway resilience
- Database clusters: testing replication and failover
- Message brokers: Kafka and RabbitMQ failure scenarios
- CDN and edge cache failure simulations
- Testing distributed identity and access management
- Validating CI/CD pipeline resilience
- Chaos in data-intensive workloads: ETL and analytics
- Testing machine learning model serving infrastructure
- Resilience in real-time streaming systems
- Testing multi-tenant SaaS isolation mechanisms
Module 12: Building Your First Chaos Program - Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers
Module 13: Certification & Career Advancement - Overview of the final assessment structure
- Preparing for the Certificate of Completion exam
- Reviewing key concepts and decision frameworks
- Practicing scenario-based evaluation questions
- Submitting your final resilience proposal
- Earning your Certificate of Completion from The Art of Service
- Adding the certification to your LinkedIn profile
- Using the credential in job applications and promotions
- Joining the global community of certified practitioners
- Accessing ongoing expert-curated updates and resources
- Receiving invitations to advanced mastermind sessions
- Building a personal portfolio of chaos experiments
- Positioning yourself as a resilience leader
- Leveraging certification for consulting and speaking opportunities
- Planning your next career move with resilience expertise
- Assessing organisational readiness for chaos
- Creating a 30-60-90 day chaos roadmap
- Selecting your first experiment target
- Gathering stakeholder input and support
- Conducting a pre-mortem for risk identification
- Running your first controlled, small-scale experiment
- Documenting outcomes and lessons learned
- Sharing results with engineering and leadership
- Iterating based on feedback and data
- Scaling to additional services and teams
- Integrating chaos into release validation
- Establishing monthly chaos review meetings
- Creating a public-facing resilience report
- Building a knowledge base of failure patterns
- Developing a self-service chaos lab for engineers