Datadog

Datadog Learning Center

Datadog is an extensive, easy-to-use platform for understanding your infrastructure. The Datadog Learning Center ensures you are able to leverage everything the platform has to offer.

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Available courses

Going Deeper with Logs: Processing

Logs provide invaluable information about your infrastructure and applications. An important part of log management is processing logs after they've been collected to extract the information. During processing, logs are parsed for attributes and further processed to enrich the logs. In this course, you will focus on log processing for log management. The course provides an overview of Pipelines and Processors, Parsing, and Standard Attributes. You will also complete two hands-on activities. In Grok Parsing, you will practice creating parsing rules for log samples. In Creating a Custom Pipeline, you will create a pipeline with the necessary processors to process web access logs.

Tagging Best Practices

Tagging is a powerful tool for searching and correlating your infrastructure and application data throughout Datadog. With your use cases and end users in mind, you can develop a deliberate tagging approach to help you optimize your monitoring workflows. This course covers an overview of tagging, recommended tagging best practices, and examples of tagging use cases in Datadog. (This course does not focus on the details for assigning and using tags in Datadog, but rather focuses on the thought process behind selecting which tags to assign to meet business needs.)

Introduction to Service Level Objectives

Service level objectives (SLOs) are targets for quality of service to meet user expectations. SLO monitors in Datadog allow you to track your SLOs to ensure reliability of your services. In this course, you will learn about SLOs and about working with SLOs in Datadog.

Introduction to Application Performance Monitoring

Datadog Application Performance Monitoring (APM) provides a variety of features to monitor, troubleshoot, and optimize end-to-end application performance. Modern applications are distributed systems composed of numerous services that handle high volumes of requests to the application. Oftentimes, multiple services are involved in handling a request, and when a request fails or is handled poorly, it’s difficult to pinpoint the root cause. Distributed tracing is a method used to track how a request is handled by a distributed system.

This course presents an introduction to distributed tracing and APM in Datadog. In the course, you will instrument an app for Datadog APM and explore the Traces Search, Live Search, Trace View, Services List, Service Page, Resource Page, Service Map, App Analytics, and APM Monitors features.

Monitoring the Kubernetes Platform

When it comes to monitoring Kubernetes, there are two tasks you can take on. First, you might want to monitor the applications running on Kubernetes. While it may be similar to monitoring applications on an EC2 or other virtual machine environments, there are some unique aspects to keep in mind. But the other area to monitor is the Kubernetes platform itself. That is what this course focuses on. Monitoring the Kubernetes platform, or Control Plane means looking at each of the components that comprise the platform, including:

etcd
the API Server
the Controller Manager and Scheduler
CoreDNS

In this course we will look at each of these as well as installing the agent with the manifest and with Helm, using the cluster agent, and more.

Understanding Observability

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.

Introduction to Datadog

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.

Introduction to Monitoring

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

Editing Trainer: Matt Williams - Datadog Evangelist

Building Better Dashboards

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

Editing Trainer: Matt Williams - Datadog Evangelist

Introduction to Log Management in Datadog

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.

Introduction to Monitoring Kubernetes

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.

Tagging Best Practices

NOTE: The new version of this course can be found here. This version will no longer be available after 01/15/2021. If you have saved the link to this course, please update the link to https://learn.datadoghq.com/course/view.php?id=35.

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.

Going Deeper with Logs: Parsing

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.

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Running into an issue?

If you are experiencing any issues with the platform, there are two ways to reach out to the Learning team at Datadog. First, there is our email address learn@datadoghq.com. Then there is the #training slack channel. If you don't have an account there, go to http://chat.datadoghq.com and sign up.