Tagging is a powerful tool for correlating your infrastructure and application data throughout Datadog. With a variety of methods to assign and use tags, Datadog enables you to maximize your infrastructure, application performance, and network monitoring and alerting workflows. This course covers an overview of tagging, recommended tagging best practices, and tagging use cases for monitoring your infrastructure and application data with Datadog. 

Service level objectives (SLOs) in Datadog allow you to track SLOs to ensure reliability of your services and meet user expectations. In Datadog, you can create Monitor- (time) and Metric-based SLOs. You can also use Error Budgets, which quantify how well you are meeting your SLOs, to make your SLOs actionable. In this course, you will learn the basics about SLOs, creating SLOs in Datadog, and using error budgets to make your SLOs actionable.

Understanding Observability is a short course designed to introduce you to the concept of observability and why it's important to you. Watch this one video before starting the Introduction to Datadog course. After you complete both courses you will have completed the Basic Observability Certification.

Datadog is a monitoring and analytics platform made to be incredibly powerful yet very easy to use. But to take full advantage of all that the platform offers, we suggest taking this interactive course introducing you to many of the features. By the end of the course, you will have a full understanding of how to use and work with dashboards, how to setup alerting, working with APM and Logs, and using Synthetics. 

This course is designed to introduce attendees to a higher level of perspective to monitoring their systems.  It focuses on how to approach metrics and how to view them in perspective, rather than Datadog features.  The course begins with a short introduction to Metrics and moves on to how to most effectively monitor your systems and how to think about your alerting procedures.

Those new to the concept of Monitoring should consume the 
Intro to Datadog Course first. 

Table of Contents: 

  • Defining Metrics
  • How to Think about Metrics
  • Alerting on What Matters
  • Performance Issues
  • More Than Just Metrics

Average Course Length: 60m

This course is designed to show attendees how to go beyond the standard dashboards in Datadog, viewing their data in ways that allow new insights and layers of information to be exposed and interpreted. The course starts out with a quick review video of Dashboards and moves on to customizing your view of data in many helpful and innovative ways.

Those new to the concept of Dashboards should consume the Intro to Datadog Course first.

Table of Contents:

  • Building Dashboards 
  • Visualizing Data in Time Series 
  • Visualizing Summaries of Data 
  • Flipping Your Data Visualization 
  • Monitoring Math

Average Course Length: 45m

Datadog APM provides you with deep insight into your application’s performance-from automatically generated dashboards monitoring key metrics, such as request volume and latency, to detailed traces of individual requests-side by side with your logs and infrastructure monitoring.

This course will get you started with Application Performance Monitoring and Datadog. We use Docker and Docker Compose in this course.

Use Datadog Log management solution to enrich, monitor, and analyze logs from all your systems for troubleshooting, auditing, visualization, and alerting.

This course will get you started with Datadog Log management solution by showing you how to set up the Datadog Agent to collect your logs then by going through best practices for log collection and processing, and finally by diving into scenarios that will build experience with troubleshooting and monitoring techniques with the Datadog platform.

Working with logs in Datadog allows you to get more information about what is going on in your environment. If you are familiar with the three pillars of observability, you know that metrics show you the known unknowns, but the logs give you information about the unknown unknowns. We provide integrations that automatically parse many of the common logging formats, but what happens when you need to parse a log that is not from one of the included integrations? How do you convert a line of formatted text into the facets and attributes that can be manipulated in Datadog. When those facets exist, you can easily search through the logs and analyze them. This course will get you started on the more advanced parsing topics available in Datadog Logs. If you are new to logging in Datadog, we recommend that you first follow the Introduction to Logs course. 

Now that you have taken the various courses that show you the basics of Datadog, its time to apply those learnings to Kubernetes. This course will show you what's special about installing the agent, setting up APM and Logs, and monitoring the applications and the platform when that platform is Kubernetes. Most of what you see here will apply no matter where your Kubernetes cluster live, whether thats up in the cloud or in your own datacenter. But every environment is different and there is no one right way to architect a Kubernetes cluster. So what you see here may not be exactly what you might see. Our goal here is to show you enough of what's special and then you can apply it to your own environment.