Logging, Monitoring, and Observability in Google Cloud (LMOGC)

Logging, Monitoring, and Observability in Google Cloud (LMOGC)

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Duration

3 Days

Certified Instructor

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Course Description

Course Overview

The Logging, Monitoring, and Observability in Google Cloud (LMOGC) course is designed to provide individuals with the knowledge and skills necessary to implement effective logging, monitoring, and observability strategies on the Google Cloud Platform (GCP). This course focuses on the key concepts, tools, and best practices for collecting, analyzing, and visualizing logs and metrics to gain valuable insights into system performance and troubleshoot issues.

Prerequisites

To enroll in the LMOGC course, participants should have a basic understanding of cloud computing concepts and familiarity with GCP fundamentals. Basic knowledge of networking and system administration will be beneficial. Participants should also have access to a GCP project or demo environment to practice the concepts covered in the course.

Methodology

The LMOGC course follows a blended learning approach, combining theoretical instruction, demonstrations, discussions, and hands-on labs. Participants will engage in instructor-led sessions where logging, monitoring, and observability concepts and best practices are explained. They will also have access to GCP resources and tools to gain practical experience in implementing logging and monitoring solutions. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Logging, Monitoring, and Observability

Overview of logging, monitoring, and observability concepts and their importance

Understanding the role of logs and metrics in system visibility and troubleshooting

Exploring GCP tools and services for logging and monitoring

Collecting and Analyzing Logs

Configuring logging agents and log sinks in GCP

Utilizing Stackdriver Logging for log ingestion and storage

Analyzing logs and performing log-based queries and filtering

Monitoring and Alerting

Configuring metrics and dashboards in Stackdriver Monitoring

Creating uptime checks and setting alerting policies

Utilizing Stackdriver Debugger for application performance monitoring

Tracing and Profiling Applications

Utilizing Stackdriver Trace for distributed tracing and latency analysis

Analyzing application performance with Stackdriver Profiler

Identifying performance bottlenecks and optimizing application performance

Observability and Distributed Systems

Implementing observability practices with distributed systems

Utilizing Google Cloud Pub/Sub and Cloud Functions for observability workflows

Designing for resilience and observability in microservices architectures

Log Analysis and Visualization

Utilizing BigQuery for log analysis and querying large datasets

Creating custom log-based metrics and visualizations

Visualizing log data and creating dashboards with Data Studio

Outcome

By the end of the LMOGC course, participants will have:

  • Developed a comprehensive understanding of logging, monitoring, and observability concepts and best practices
  • Acquired practical knowledge in implementing logging and monitoring solutions in GCP
  • Gained expertise in collecting, analyzing, and visualizing logs and metrics using GCP tools and services
  • Learned techniques for troubleshooting and optimizing system performance with log and metric data
  • Gained hands-on experience through practical labs and exercises
  • Prepared to implement effective logging, monitoring, and observability strategies on GCP

Labs

The LMOGC course includes hands-on labs that provide participants with practical experience in implementing logging and monitoring solutions on GCP. Some examples of lab exercises include:

  • Configuring logging agents and sinks to collect and store logs in Stackdriver Logging
  • Creating metrics and dashboards in Stackdriver Monitoring
  • Configuring uptime checks and setting alerting policies
  • Utilizing Stackdriver Trace for distributed tracing and latency analysis
  • Analyzing logs and querying large datasets using BigQuery
  • Creating visualizations and dashboards with Data Studio

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in implementing logging, monitoring, and observability solutions on GCP, allowing them to develop practical skills in ensuring system visibility and troubleshooting capabilities.

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