Arki1, Google Cloud Authorized Training Partner of the years 2019, 2020 & 2021 in Latin America 
This three-day instructor-led course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
Objectives
In this course, participants will learn the following skills:
- Plan and implement a well-architected logging and monitoring infrastructure
- Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs)
- Create effective monitoring dashboards and alerts
- Monitor, troubleshoot, and improve Google Cloud infrastructure
- Analyze and export Google Cloud audit logs
- Find production code defects, identify bottlenecks, and improve performance
- Optimize monitoring costs
Audience
This class is intended for the following audience:
Cloud architects, administrators, and SysOps personnel
Cloud developers and DevOps personnel
Prerrequisites
To get the most out of this course, participants should have:
- Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience
- Basic scripting or coding familiarity
- Proficiency with command-line tools and Linux operating system environments
Duration
24 hours (3 days)
Investment
Check the next open public class in our enrollment page.
If you are interested in a private training class for your company, contact us.
Course Outline
The course includes presentations, demonstrations, and hands-on labs.
- Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring
- Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace,
and Profiler
- Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation
- Measure customer pain with SLIs
- Define critical performance measures
- Create and use SLOs and SLAs
- Achieve developer and operation harmony with error budgets
- Develop alerting strategies
- Define alerting policies
- Add notification channels
- Identify types of alerts and common uses for each
- Construct and alert on resource groups
- Manage alerting policies programmatically
- Choose best practice monitoring project architectures
- Differentiate Cloud IAM roles for monitoring
- Use the default dashboards appropriately
- Build custom dashboards to show resource consumption and application load
- Define uptime checks to track aliveness and latency
- Integrate logging and monitoring agents into Compute Engine VMs
and images - Enable and utilize Kubernetes Monitoring
- Extend and clarify Kubernetes monitoring with Prometheus
- Expose custom metrics through code, and with the help of
OpenCensus
- Identify and choose among resource tagging approaches
- Define log sinks (inclusion filters) and exclusion filters
- Create metrics based on logs
- Define custom metrics
- Link application errors to Logging using Error Reporting
- Export logs to BigQuery
- Collect and analyze VPC Flow logs and Firewall Rules logs
- Enable and monitor Packet Mirroring
- Explain the capabilities of Network Intelligence Center
- Use Admin Activity audit logs to track changes to the configuration or metadata of resources
- Use Data Access audit logs to track accesses or changes to user-provided resource data
- Use System Event audit logs to track GCP administrative actions
- Define incident management roles and communication channels
- Mitigate incident impact
- Troubleshoot root causes
- Resolve incidents
- Document incidents in a post-mortem process
- Debug production code to correct code defects
- Trace latency through layers of service interaction to eliminate performance bottlenecks
- Profile and identify resource-intensive functions in an application
- Analyze resource utilization cust for monitoring related components within Google Cloud
- Implement best practices for controlling the cost of monitoring within Google Cloud