Datadog Boot Camp
Gain visibility and insight into all of your applications and services.
Datadog is a visibility tool that integrates into your organization's value delivery stack with astonishing data analysis and visualization results. This boot camp will equip all attendees with operational knowledge of the features in Datadog.
We'll take the top-down approach at using Datadog—taking you from definitions and use cases to setting up and configuring all monitoring features. We'll use an ephemeral organization instance to demonstrate the powerful logs, end-to-end traces, metrics, and reporting features of Datadog. All this, before turning to your specific industry use case for a value-added experience.
Duration
3 days/24 hours of instructionPublic Classroom Pricing
$2750(USD)
GSA Price: $2007.85
Group Rate: $2550
Private Group Pricing
Have a group of 5 or more students? Request special pricing for private group training today.
Download the Course Brochure
Part 1: Getting Started With Datadog
- What is Datadog?
- Discussion: Problem specification for Datadog
- Datadog as a solution for low visibility across multistack environments
- Datadog application performance monitoring (APM) and Continuous Profiler
- Request tracing, log management, metric collection, and analysis with AI
- Real user monitoring and traffic observing on premises, in the cloud, and hybrid
- Datadog as a trend visualization tool; dashboards explained
- Alerts and collaboration on monitoring and keeping systems up
- Security and compliance monitoring with Datadog
- Datadog's usage tiers and pricing models (how to get what you need)
- Exercise: Datadog Agent installation on all environments.
- Installing Datadog on workstations (Linux and Windows)
- Installing Datadog in the cloud
- Setting up distributed instances over a hybrid infrastructure
Part 2: Datadog Integrations
- Exploration of available integrations
- Exercise: Activating "Agent Autodiscovery"
- Installation of multiple integrations; mirroring your stack
- Exercise: Configuring infrastructure and database integrations
- Connecting AWS and MongoDB using the agent
Part 3: Datadog Dashboards
- Exercise: Exploring Datadog default dashboards
- Creating custom dashboards using metrics widgets
- Introduction to Datadog Notebooks
- Exercise: Creating a postmortem notebook
Part 4: Monitoring Logs With Datadog
- Discussion: Collecting and managing logs with Datadog
- Discovering and mastering tags
- How to configure Datadog to collect logs from different environments
- Creating log integration pipelines
- Making sure Datadog collects the right logs
- Exercise: Setting up log collection between Docker and a Ruby on Rails app
- Fresh environment preparation (Docker)
- Configuring Datadog for log collection
- Ruby on Rails: Datadog integration configuration
- Analyzing collected logs
Part 5: Using Datadog as an APM Tool (Tracing)
- Discussion: Exploring Datadog tracing terminology and use cases
- Discovering how to use Datadog's APM features
- Setting up environments for monitoring
- Optimizing Datadog for end-to-end tracing (latency and errors)
- How to analyze trace data
- Exercise: Configuring APM for the environment and app in Part 4
- Analyzing trace data for the app
Part 6: Continuous Profiling With Datadog
- Discovering the advantages of profiling
- A demonstration of how to set up, run, test, and clean profiles
Part 7: Synthetic Testing
- How to conduct synthetic tests with Datadog
- Exercise: Testing APIs and browser tests with Datadog
Part 8: Monitoring Kubernetes With Datadog
- How to configure the Datadog agent for K8s monitoring
- Setting up logs in K8s
- Switching on K8s APM with Datadog
- Kubernetes end-to-end monitoring with Datadog
- Collect and manage logs with Datadog
- Optimize Datadog for end-to-end tracing
- Test APIs and browser tests with Datadog
- Install multiple integrations; mirroring your stack
- Activate and utilize "Agent Autodiscovery"
- Configure infrastructure and database integrations
- Creating a postmortem notebook
- Monitor Kubernetes With Datadog