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