Skip to main contentSkip to user menuSkip to navigation

DataDog

Master DataDog: full-stack observability, APM, infrastructure monitoring, and alerting strategies.

40 min readIntermediate
Not Started
Loading...

What is Datadog?

Datadog is a unified observability platform providing infrastructure monitoring, application performance monitoring, log management, and analytics for modern cloud applications. It offers real-time visibility into your entire technology stack with customizable dashboards, intelligent alerting, and powerful analytics capabilities.

Used by organizations to monitor performance, troubleshoot issues, and optimize applications across cloud, hybrid, and on-premises environments. Datadog integrates with 500+ technologies and provides machine learning-powered insights for proactive monitoring and incident response.

Datadog Cost Calculator

50 hosts
100 GB
10 services
$1,210
Monthly Cost
$15
Per Host/Month
$1.50
Per GB Logs

Unified Observability Platform

Datadog provides unified observability by correlating metrics, traces, and logs in a single platform. This correlation enables faster troubleshooting and comprehensive understanding of system behavior.

Infrastructure Monitoring

Real-time monitoring of servers, containers, and cloud resources

APM & Distributed Tracing

End-to-end request tracing across microservices

Log Management

Centralized log aggregation and analysis

# Datadog APM Integration
from ddtrace import tracer
from ddtrace.contrib.flask import TraceMiddleware

app = Flask(__name__)
TraceMiddleware(app, tracer, service="web-app")

@app.route('/api/users/<user_id>')
def get_user(user_id):
    # Custom span creation
    with tracer.trace("database.query", service="postgres") as span:
        span.set_tag("query.table", "users")
        span.set_tag("user.id", user_id)
        
        user = db.query("SELECT * FROM users WHERE id = %s", user_id)
        
        if user:
            span.set_tag("user.found", True)
            return {"user": user}
        else:
            span.set_tag("error", True)
            return {"error": "User not found"}, 404

Platform Capabilities

Infrastructure Monitoring

  • • Real-time infrastructure discovery
  • • Container & Kubernetes monitoring
  • • Custom metrics collection
  • • Network performance monitoring

APM & Tracing

  • • Distributed tracing
  • • Service dependency mapping
  • • Code-level profiling
  • • Error tracking & analysis

Log Management

  • • Centralized log aggregation
  • • Automatic log parsing
  • • Real-time log streaming
  • • Log-based metrics & alerts

Dashboards & Analytics

  • • Customizable dashboards
  • • Template variables
  • • Business intelligence reporting
  • • Real-time collaboration

Alerting & Incident Management

  • • ML-powered anomaly detection
  • • Multi-condition alerts
  • • Incident management
  • • Automated response workflows

Integrations & APIs

  • • 700+ integrations
  • • REST & GraphQL APIs
  • • Terraform provider
  • • Custom app development

Real-World Examples

A

Airbnb

Travel & Hospitality

Monitors 100K+ hosts globally with unified observability, reducing MTTR by 50% and providing real-time insights into user booking experiences across 220+ countries.

Infrastructure • APM • Real-time Analytics
N

The New York Times

Media & Publishing

Processes 500M+ page views monthly with distributed tracing, enabling rapid deployment cycles and maintaining 99.9% uptime during breaking news events.

APM • Log Management • DevOps Monitoring
P

PagerDuty

DevOps & Monitoring

Manages incident response for 12K+ customers using Datadog's alerting and anomaly detection, reducing false positives by 70% with intelligent alert correlation.

Incident Management • Alerting • ML Analytics
A

Atlassian

Software Development

Monitors microservices architecture serving 180K+ customers across Jira, Confluence, and Bitbucket with unified dashboards and cross-team collaboration.

Microservices • Team Collaboration • Performance Optimization

Advanced Monitoring Features

Service Maps

Visualize dependencies and communication patterns between microservices

Anomaly Detection

ML-powered detection of unusual patterns in metrics and logs

Live Processes

Real-time process trees with resource usage and command details

# Datadog Agent Configuration
api_key: YOUR_API_KEY_HERE
site: datadoghq.com

# Infrastructure monitoring
process_config:
  enabled: "true"
  
network_config:
  enabled: true

# Container monitoring
container_include:
  - "name:web-*"
  - "image:nginx"
  
container_exclude:
  - "name:dd-agent"

# Log collection
logs_enabled: true
logs_config:
  container_collect_all: true
  
# APM configuration  
apm_config:
  enabled: true
  env: production

Best Practices

✅ Do

  • Use structured logging (JSON) for automatic attribute extraction and filtering
  • Implement proper tagging strategy for cost allocation and resource filtering
  • Use composite monitors to reduce alert fatigue through correlation
  • Leverage template variables in dashboards for dynamic context switching
  • Use StatsD for custom metrics with minimal application performance impact

❌ Don't

  • Send sensitive data in logs or metrics - use proper sanitization
  • Create too many low-threshold alerts - focus on actionable conditions
  • Ignore high cardinality tags that can increase costs significantly
  • Use direct HTTP API calls for high-frequency metrics - prefer StatsD
  • Deploy without proper monitoring - instrument from day one
No quiz questions available
Quiz ID "datadog" not found