Google Cloud DevOps Engineer Training and Certification in Coimbatore

Chapter 1.

Bootstrapping a Google Cloud organization for DevOps

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

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

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

1.0 Applying site reliability engineering principles to a service
1.1 Balance change, velocity, and reliability of the service
1.2 Manage service life cycle
1.3 Ensure healthy communication and collaboration for operations

Chapter 2.
2.0 Building and implementing CI/CD pipelines for a service
2.1 Design CI/CD pipelines

- 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

2.2 Implement CI/CD pipelines

- 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 Manage configuration and secrets

- Secure storage methods and key rotation services (e.g., Cloud Key Management Service, Secret Manager)

- Secret management

- Build versus runtime secret injection

2.4 Manage infrastructure as code
2.5 Deploy CI/CD tooling
2.6 Manage different development environments
2.7 Secure the deployment pipeline

- Vulnerability analysis with Artifact Registry

- Binary Authorization

- IAM policies per environment

Chapter 3.
3.0 Implementing service monitoring strategies
3.1 Manage application logs
3.2 Manage application metrics with Stackdriver Monitoring
3.3 Manage Stackdriver Monitoring platform
3.4 Manage Stackdriver Logging platform
3.5 Implement logging and monitoring access controls

Applying site reliability engineering practices to a service

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”)

Managing service lifecycle. Considerations include:

- Service management (e.g., introduction of a new service by using pre-mortems [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

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

Chapter 4.
4.0 Optimizing service performance
4.1 Identify service performance issues
4.2 Debug application code
4.3 Optimize resource utilization

Mitigating incident impact on users. Considerations include:

- Communicating during an incident

- Draining/redirecting traffic

- Adding capacity

Chapter 5.
5.0 Managing service incidents
5.1 Coordinate roles and implement communication channels during a service incident
5.2 Investigate incident symptoms impacting users with Stackdriver IRM
5.3 Mitigate incident impact on users
5.4 Resolve issues
5.5 Document issue in a postmortem

Implementing service monitoring strategies

- 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)

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

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

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])

Optimizing service performance

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)

Implementing debugging tools in Google Cloud. Considerations include:

- Application instrumentation

- Cloud Logging

- Cloud Trace

- Error Reporting

- Cloud Profiler

- Cloud Monitoring

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