Module 1: Bootstrapping a Google Cloud organization for DevOps
1.1 Designing the overall resource hierarchy for an organization. Considerations include:
- Projects and folders
- Shared networking
- Identity and Access Management (IAM) roles and organization-level policies
- Creating and managing service accounts
1.2 Managing infrastructure as code. Considerations include:
- Infrastructure as code tooling (e.g., Cloud Foundation Toolkit, Config Connector, Terraform, Helm)
- Making infrastructure changes using Google-recommended practices and infrastructure as code blueprints
- Immutable architecture
1.3 Designing a CI/CD architecture stack in Google Cloud, hybrid, and multi-cloud environments. Considerations include:
- CI with Cloud Build
- CD with Google Cloud Deploy
- Widely used third-party tooling (e.g., Jenkins, Git, ArgoCD, Packer)
- Security of CI/CD tooling
1.4 Managing multiple environments (e.g., staging, production). Considerations include:
- Determining the number of environments and their purpose
- Creating environments dynamically for each feature branch with Google Kubernetes Engine (GKE) and Terraform
- Anthos Config Management
Module 2: Building and implementing CI/CD pipelines for a service
2.1 Designing and managing CI/CD pipelines. Considerations include:
- Artifact management with Artifact Registry
- Deployment to hybrid and multi-cloud environments (e.g., Anthos, GKE)
- CI/CD pipeline triggers
- Testing a new application version in the pipeline
- Configuring deployment processes (e.g., approval flows)
- CI/CD of serverless applications
2.2 Implementing CI/CD pipelines. Considerations include:
- Auditing and tracking deployments (e.g., Artifact Registry, Cloud Build, Google Cloud Deploy, Cloud Audit Logs)
- Deployment strategies (e.g., canary, blue/green, rolling, traffic splitting)
- Rollback strategies
- Troubleshooting deployment issues
2.3 Managing CI/CD configuration and secrets. Considerations include:
- Secure storage methods and key rotation services (e.g., Cloud Key Management Service, Secret Manager)
- Secret management
- Build versus runtime secret injection
2.4 Securing the CI/CD deployment pipeline. Considerations include:
- Vulnerability analysis with Artifact Registry
- Binary Authorization
- IAM policies per environment
Module 3: Applying site reliability engineering practices to a service
3.1 Balancing change, velocity, and reliability of the service. Considerations include:
- Discovering SLIs (e.g., availability, latency)
- Defining SLOs and understanding SLAs
- Error budgets
- Toil automation
- Opportunity cost of risk and reliability (e.g., number of “nines”)
3.2 Managing service lifecycle. Considerations include:
- Service management (introduction of a new service by using a pre-service onboarding checklist, launch plan, or deployment plan, deployment, maintenance, and retirement)
- Capacity planning (e.g., quotas and limits management)
- Autoscaling using managed instance groups, Cloud Run, Cloud Functions, or GKE
- Implementing feedback loops to improve a service
3.3 Ensuring healthy communication and collaboration for operations. Considerations include:
- Preventing burnout (e.g., setting up automation processes to prevent burnout)
- Fostering a culture of learning and blamelessness
- Establishing joint ownership of services to eliminate team silos
3.4 Mitigating incident impact on users. Considerations include:
- Communicating during an incident
- Draining/redirecting traffic
- Adding capacity
3.5 Conducting a postmortem. Considerations include:
- Documenting root causes
- Creating and prioritizing action items
- Communicating the postmortem to stakeholders
Module 4: Implementing service monitoring strategies
4.1 Managing logs. Considerations include:
- Collecting structured and unstructured logs from Compute Engine, GKE, and serverless platforms using Cloud Logging
- Configuring the Cloud Logging agent
- Collecting logs from outside Google Cloud
- Sending application logs directly to the Cloud Logging API
- Log levels (e.g., info, error, debug, fatal)
- Optimizing logs (e.g., multiline logging, exceptions, size, cost)
4.2 Managing metrics with Cloud Monitoring. Considerations include:
- Collecting and analyzing application and platform metrics
- Collecting networking and service mesh metrics
- Using Metrics Explorer for ad hoc metric analysis
- Creating custom metrics from logs
4.3 Managing dashboards and alerts in Cloud Monitoring. Considerations include:
- Creating a monitoring dashboard
- Filtering and sharing dashboards
- Configuring alerting
- Defining alerting policies based on SLOs and SLIs
- Automating alerting policy definition using Terraform
- Using Google Cloud Managed Service for Prometheus to collect metrics and set up monitoring and alerting
4.4 Managing Cloud Logging platform. Considerations include:
- Enabling data access logs (e.g., Cloud Audit Logs)
- Enabling VPC Flow Logs
- Viewing logs in the Google Cloud console
- Using basic versus advanced log filters
- Logs exclusion versus logs export
- Project-level versus organization-level export
- Managing and viewing log exports
- Sending logs to an external logging platform
- Filtering and redacting sensitive data (e.g., personally identifiable information [PII], protected health information PHI)
4.5 Implementing logging and monitoring access controls. Considerations include:
- Restricting access to audit logs and VPC Flow Logs with Cloud Logging
- Restricting export configuration with Cloud Logging
- Allowing metric and log writing with Cloud Monitoring
Module 5: Optimizing service performance
5.1 Identifying service performance issues. Considerations include:
- Using Google Cloud’s operations suite to identify cloud resource utilization
- Interpreting service mesh telemetry
- Troubleshooting issues with compute resources
- Troubleshooting deploy time and runtime issues with applications
- Troubleshooting network issues (e.g., VPC Flow Logs, firewall logs, latency, network details)
5.2 Implementing debugging tools in Google Cloud. Considerations include:
- Application instrumentation
- Cloud Logging
- Cloud Trace
- Error Reporting
- Cloud Profiler
- Cloud Monitoring
5.3 Optimizing resource utilization and costs. Considerations include:
- Preemptible/Spot virtual machines (VMs)
- Committed-use discounts (e.g., flexible, resource-based)
- Sustained-use discounts
- Network tiers
- Sizing recommendations
