Datadog 

Length: 2 days

Foundation Modules

Module 1: Introduction to Datadog

• What is Datadog and observability platform overview

• Three pillars of observability (metrics, logs, traces)

• Datadog vs. traditional monitoring solutions

• Key features and use cases

• Datadog architecture and components

• Platform navigation and user interface

• Hands-on: Datadog environment walkthrough

Module 2: Datadog Agent Installation and Configuration

• Understanding the Datadog Agent

• Agent architecture and components

• Installation methods (one-line installer, package managers)

• Agent configuration files and YAML structure

• Platform-specific installations (Windows, Linux, macOS)

• Agent status and troubleshooting

• Hands-on: Agent installation and configuration lab

Module 3: Infrastructure Monitoring

• Host monitoring and system metrics

• Process and service discovery

• Network performance monitoring

• Container monitoring (Docker, containerd)

• Cloud platform integrations (AWS, Azure, GCP)

• Agent checks and custom metrics collection

• Hands-on: Infrastructure monitoring setup

Module 4: Tagging Strategy and Integrations

• Importance of tagging in Datadog

• Creating effective tagging strategies

• Reserved and custom tags

• Overview of 900+ integrations

• Installing and configuring common integrations

• Auto-discovery for containerized environments

• Hands-on: Implementing tagging strategy and integrations

Module 5: Metrics and Data Visualization

• Understanding metric types (gauges, counters, histograms)

• Metric naming conventions and best practices

• Data aggregation and time series concepts

• Query language and metric explorer

• Creating custom metrics with DogStatsD

• Hands-on: Working with metrics and queries

Module 6: Foundation Review and Assessment

• Key concepts recap

• Troubleshooting common issues

• Knowledge check and Q&A

Advanced Modules

Module 7: Dashboards and Reporting

• Dashboard types and use cases

• Widget types and visualization options

• Template variables and dynamic dashboards

• Dashboard sharing and permissions

• Best practices for dashboard design

• Hands-on: Building comprehensive dashboards

Module 8: Monitoring and Alerting

• Monitor types and configuration

• Alert conditions and thresholds

• Notification channels and routing

• Alert fatigue prevention strategies

• Composite monitors and dependencies

• Hands-on: Creating effective monitoring rules

Module 9: Application Performance Monitoring (APM)

• APM fundamentals and distributed tracing

• Application instrumentation methods

• Service mapping and dependency visualization

• Performance metrics and bottleneck identification

• Error tracking and debugging

• Hands-on: APM setup and trace analysis

Module 10: Log Management

• Log collection and forwarding

• Log parsing and processing pipelines

• Log search and filtering techniques

• Log correlation with metrics and traces

• Log-based metrics and monitoring

• Hands-on: Complete log management workflow

Module 11: Synthetic Monitoring and RUM

• Synthetic test creation and configuration

• API monitoring and uptime checks

• Browser testing and user journey monitoring

• Real User Monitoring (RUM) implementation

• Performance benchmarking and SLA monitoring

• Hands-on: Synthetic tests and RUM setup

Module 12: Container and Kubernetes Monitoring

• Datadog Operator and Helm chart deployment

• Cluster Agent configuration

• Kubernetes integration and autodiscovery

• Container resource monitoring

• Pod and service monitoring

• Hands-on: Kubernetes monitoring setup

Module 13: Security Monitoring Basics

• Security monitoring fundamentals

• Cloud Security Posture Management (CSPM) overview

• Application Security Monitoring (ASM) basics

• Security dashboard creation

• Hands-on: Basic security monitoring configuration

Module 14: APIs and Automation

• API automation and programmatic control

• Datadog Terraform provider basics

• Webhook configurations and third-party integrations

• Cost optimization and resource management

• Hands-on: Basic automation and integration projects

Module 15: Best Practices and Troubleshooting

• Deployment and scaling best practices

• Performance optimization techniques

• Common troubleshooting scenarios

• Agent and integration debugging

• Cost management and optimization

Module 16: Certification Prep and Course Wrap-up

• Datadog certification program overview

• Study resources and documentation

• Practice assessments and exam preparation

• Career paths and next steps

• Course evaluation and feedback

• Q&A session and knowledge validation

Key Learning Objectives

By the end of this course, participants will be able to:

1. Deploy and Configure: Install and configure Datadog Agent across multiple platforms and environments

2. Monitor Infrastructure: Set up comprehensive infrastructure monitoring including hosts, containers, and cloud services

3. Implement APM: Configure application performance monitoring and distributed tracing

4. Manage Logs: Collect, process, and analyze logs effectively using Datadog Log Management

5. Create Dashboards: Build dynamic dashboards and visualizations for different stakeholders

6. Set Up Alerting: Configure intelligent monitoring and alerting systems

7. Leverage Integrations: Utilize Datadog's extensive integration ecosystem

8. Apply Security Monitoring: Implement basic security monitoring practices

9. Use Synthetic Monitoring: Set up synthetic tests and real user monitoring

10. Automate Operations: Use APIs and basic automation tools for Datadog deployments