Datadog Learning Center

Understanding Observability

Understanding Observability is a short course designed to introduce you to the concept of observability and why it's important to you. This is an excellent place to start if you are new to the space in which Datadog operates.

Estimated Course Length: 5 Minutes

Datadog 101: Developer

Application developers get enormous value out of Datadog, but in significantly different ways than DevOps engineers or SREs.

You’ll begin this course by installing the Datadog Agent in a containerized web application. You’ll then create Synthetic Tests to monitor critical front-end services with simulated customer interactions. And as the name implies, Real User Monitoring (RUM) will help you measure and improve the quality of your users’ experience.

Along the way you will see how Datadog’s core tools such as Logs, APM, Dashboards, Monitors, and Alerts can help you stay ahead of application issues before they cause problems for your users.

Datadog 101: Site Reliability Engineer

Datadog's core suite of tools are tailored for SREs. Together, they provide a single pane of glass that will give you a bird's eye view of your entire infrastructure, with the ability to zoom in on individual processes.

In this course, you will add the Datadog Agent to a containerized web application and use essential Datadog tools including:

The Agent
Integrations
Application Performance Monitoring (APM)
Network Performance Monitoring (NPM)
Logs
Metrics & Monitors
Dashboards

By the end of this course you will be able to use Datadog tools to analyze the past, observe the present, and optimize the future of your application infrastructure.

Introduction to Application Performance Monitoring

Datadog Application Performance Monitoring (APM) provides a variety of features for monitoring application performance, including the Service List and Map, Live and Historic Traces views, APM Monitors, and more. This course provides you with an introduction to distributed tracing and APM in Datadog.

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

Estimated Course Length: 45 Minutes

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

Estimated Course Length: 1.5 Hours

Editing Trainer: Matt Williams - Datadog Evangelist

Introduction to Log Management

Logs provide invaluable information about your infrastructure and applications. Datadog provides you with a suite of features for managing all the logs you collect from these sources. In this course, you’ll learn the basics of Log Management in Datadog. You’ll first learn about configuring logging and log collection, followed by log querying and analytics, log-based metrics and monitoring, log processing, and log storage and data access.

Estimated Course Length: 3 hours

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.

Estimated Course Length: 2 Hours

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.)

Estimated Course Length: 1.5 Hours

Introduction to Integrations

Custom Checks are great for occasional reporting, or in cases where the data source is either unique or very limited. For more general use-cases - such as application frameworks, open source projects, or commonly-used software - it makes sense to write a custom integration. In this course, you'll learn how to set up the development environment and create your first integration.

Estimated Course Length: 2 Hours

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.

Estimated Course Length: 1.5 Hours

Introduction to Monitoring Kubernetes

Now that you have taken the various courses that show you the basics of Datadog, it's 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.

Estimated Course Length: 2 Hours

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.

Estimated Course Length: 3 Hours

Monitoring Workloads on Kubernetes

No matter where your workloads run, you need to be able to monitor them effectively. You may already be familiar with monitoring those workloads running on standard compute resources available from the major cloud providers. But when you run them on Kubernetes, there are some aspects of monitoring that are different and quite unique to Kubernetes. In this short course we will cover those differences so that you can be ready to get the most out of the platform.

Progressive Delivery in Kubernetes

Will a new version of my service drive more usage? Will it reduce or increase the overall latency of my application? Can I easily test it in production for a set of users first? Progressive delivery, A/B testing, and canary deployments are strategies that help answer those questions by splitting network traffic in a controlled manner, allowing you to make informed decisions on the quality of a release.

In this workshop we will demonstrate several strategies to implement progressive delivery in Kubernetes. You will learn which of those strategies is best suited to your organization needs and how a good observability strategy is key to be successful with progressive delivery.

Introduction to Synthetic Tests

In this course, you'll learn how to monitor a web service's user experience with each of Datadog's Synthetic tests: API tests, Multistep API tests, and Browser tests.

Synthetic Tests in a CI/CD Pipeline

In this course, you'll learn how to incorporate Datadog's synthetic browser tests into a Continuous Integration/Continuous Deployment (CI/CD) pipeline to minimize user-facing application regressions resulting from buggy releases.

We strongly recommend that you complete the Introduction to Synthetics course before starting this course, as it walks you through creating the browser test used in this course.

Datadog 201: Becoming a Power User

Take your knowledge to the next level. Now that you’ve established your baseline knowledge, what’s next?

In this course you’ll learn how to build sophisticated custom dashboards and notebooks, monitor custom metrics from logs, and create and monitor UX SLOs. These advanced workflows will help you drive even more value from your Datadog usage.

You’ll also learn techniques and shortcuts that will increase your productivity in everyday Datadog tasks.

Introduction to Incident Management

Learn to use Datadog Incident Management to track incidents and collaborate with your team on a resolution. In this course, you'll learn the basics of incident management, walk through a hands-on scenario, generate a postmortem notebook, and learn about Datadog's Slack integration.

Datadog API: Automation and Infrastructure as Code

A hands-on tour of the Datadog API and its capabilities. Learn a variety of ways to communicate with the Datadog API with an emphasis on automation.

You will start exploring the Datadog API using the Postman collection, and move through using curl, dogshell, client libraries, and finally Terraform to perform useful Datadog tasks.

Estimated course length: 2hrs

Highly Scalable Observability Data Pipelines with Vector (Beta)

Vector is an open source platform for creating highly flexible and scalable observability data pipelines. It runs on your infrastructure and connects a wide variety of data sources—from Syslog to HTTP to Prometheus to Kubernetes logs—to a wide variety of sinks—from Kafka to S3 to Clickhouse to SaaS platforms like Datadog. Those pipelines can serve a wide array of use cases, such as ensuring data locality, enabling easy migration between SaaS vendors, data enrichment, and cost reduction. Built in Rust and designed for maximum throughput, minimum resource usage, and operational ease of use, Vector may just be the tool you’re looking for to power observability in your large-scale systems. In this course, you’ll learn how to use Vector to create processing pipelines for your logs and metrics via Vector’s built-in event transformation DSL (Vector Remap Language). We’ll use it to modify logs and metrics as they flow through your Vector instances, and showcase Vector aggregators to process data from multiple observability agents (including the Datadog Agent and Fluent-Bit). The general approach will be hands-on and geared toward real use cases.

This course has been removed temporarily for maintenance and will return.

Introduction to Datadog

This course has been retired. It is superseded by two new courses that feature updated content and an improved interactive lab experience:

Datadog 101: SREs

Coverage of most products, tailored to SREs. Includes Agent installation on a Host and Network Performance Monitoring

Datadog 101: Developers

Coverage of most products, tailored to developers. Includes RUM and Synthetic Tests

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.

Estimated Course Length: 2 Hours

NOTE: The new version of this course can be found here. This version will no longer be available after 2/11/2022. If you have saved the link to this course, please update the link to https://learn.datadoghq.com/course/view.php?id=82