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Monitoring and Observability with Datadog

Monitoring and Observability with Datadog

2h 48mIntermediate2022-10-20

Authors

Ibukun Itimi

Ibukun Itimi

DevOps and Observability Engineer

Course details

As systems are built— especially in this age of distributed systems and microservices— observability has become key to understanding and building reliable and highly available systems. Cloud infrastructure and microservices require so many dependencies to be functional, so it’s important to keep an eye on these different parts that keep our services up and running. In this course, Ibukun Itimi explains how to use Datadog to gain visibility into your systems to build more reliable services and infrastructure. Ibukun explains the basics of the concepts of observability infrastructure monitoring, and application performance monitoring. She covers the basics of Datadog features, including how to analyze and understand logs, create custom metrics with logs, build dashboards, set up monitors, and more.

Skills covered

DatadogSoftware AdministrationServer AdministrationNetwork AdministrationNetwork and System AdministrationDeep Dive (X:Y)

Concepts

0. Introduction

  • 01 - Datadog and observability
  • 02 - What you need to know

1. Introduction to Observability

  • 03 - What is observability
  • 04 - What is Datadog

2. Infrastructure Monitoring

  • 05 - What is infrastructure monitoring
  • 06 - Understanding Datadog infrastructure metrics

3. Application Performance Monitoring

  • 07 - What is APM
  • 08 - Understanding Datadog APM metrics and error tracking
  • 09 - Defining SLIs, SLOs, and error budgets with Datadog
  • 10 - Customizing APM metrics and traces on Datadog

4. Logging without Limits

  • 11 - Enabling Datadog logs
  • 12 - Analyzing logs with Datadog
  • 13 - Defining a robust logging pipeline on Datadog
  • 14 - Creating custom metrics with logs
  • 15 - Correlating logs and APM metrics on Datadog

5. Utilizing Datadog Metrics

  • 16 - Building Datadog dashboards
  • 17 - Setting up Datadog monitors
  • 18 - Datadog Synthetics tests
  • 19 - Datadog incidents

Conclusion

  • 20 - Leveraging Datadog integrations
  • 21 - Anomaly detection with Datadog
  • 22 - Next steps

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