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Datadog APM Monitoring

⬢ УРОВЕНЬ 2Технические
💰
Высокое
Влияние на зарплату
⏱
2 мес.
Время на освоение
📊
Средне
Сложность
👥
4
Профессии
Кратко

Application Performance Monitoring (APM) via Datadog tracks code execution: which endpoints are slow, which are erroring, which are consuming resources. Engineers instrument code with Datadog, get distributed traces (request flow across services), and real-time dashboards. Senior practitioners earn 15-20% premium because they ship systems that are observable, debuggable, and performant. Learning: 5-7 weeks (involves instrumentation + dashboard design + SLOs).

Что такое Datadog APM Monitoring

Application Performance Monitoring (APM) via Datadog is the continuous tracking of how applications behave: which endpoints are slow, which are erroring, which are consuming CPU/memory. Engineers instrument code with Datadog agents, which send performance data to Datadog. Dashboards show real-time performance metrics and distributed traces (request flow across services). Example: User makes API request → Request traces through 5 microservices → Datadog shows waterfall (which service was slow?) and logs from each service.

🔧 ИНСТРУМЕНТЫ И ЭКОСИСТЕМА
Datadog APM agentDatadog dashboard builderDatadog monitors and alertsDistributed tracing (Datadog trace explorer)Profiler integrationCustom metricsSynthetic monitoringLog correlation with tracesPython/Node.js/Go/Java instrumentationWebhook integrations

💰 Зарплата по регионам

РегионМладший специалистMiddleСтарший специалист
🇺🇸USA$80k$140k$215k
🇬🇧UK$50k$85k$130k
🇪🇺EU$55k$90k$138k
🇨🇦CANADA$75k$135k$205k

🎓 Сертификации

Datadog Academy, APM Essentials→Datadog Fundamentals→APM Best Practices (Datadog Docs)→

🎯 Профессии, где нужен Datadog APM Monitoring

Cloud Architect
Devops Engineer
Network And Computer Systems Administrators
Site Reliability Engineer

❓ Частые вопросы

▶What's the difference between logs, metrics, and traces?
Logs are messages (text events). Metrics are numbers (CPU %, response time). Traces are requests (flow: request → service A → service B → response). Use all three: logs for debugging, metrics for alerting, traces for performance debugging.
▶Do I need to instrument all my code?
No. Datadog auto-instruments common frameworks (Django, Flask, Rails, Express). You write minimal code. For custom code, add manual spans to trace business logic.
▶What's a span?
A unit of work in a trace. Example: HTTP request is a span. Database query is a span. Render page is a span. Spans have timing, tags, errors. Datadog shows request waterfall: which spans run in parallel, which are sequential.
▶How do I alert on performance?
Set monitors: 'Alert if p99 latency > 500ms' or 'Alert if error rate > 1%'. Datadog evaluates monitors every minute. Sends alerts to Slack, PagerDuty, etc.
▶Can I correlate logs with traces?
Yes. Add trace ID to logs. Datadog automatically links logs to the trace. Click log, see full request context. Huge for debugging.

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