JobCannon
เชฌเชงเชพ เช•เซŒเชถเชฒเซเชฏเซ‹

Monitoring & Observability

Know what's happening in production: logs, metrics, traces, alerts

โฌข เชŸเชฟเชฏเชฐ 2เชŸเซ‡เช•เชจเชฟเช•เชฒ
+$30k-
เชชเช—เชพเชฐ เชชเชฐ เช…เชธเชฐ
6 เชฎเชนเชฟเชจเชพ
เชถเซ€เช–เชตเชพเชจเซ‹ เชธเชฎเชฏ
เช•เช เชฟเชจ
เชฎเซเชถเซเช•เซ‡เชฒเซ€
4
เช•เชฐเชฟเชฏเชฐ
เชเช• เชจเชœเชฐเชฎเชพเช‚

Monitoring & Observability helps you understand what's happening in production systems through logs, metrics, and traces. Career path: Practitioner (basic logs/alerts, CloudWatch/Datadog, $120-150k) โ†’ Specialist (distributed tracing, SLOs, incident response, $150-190k) โ†’ Architect (observability platform design, OpenTelemetry, multi-service correlation, $190-250k+). Salary premium: $30k-$65k above base backend (DevOps/SRE tier). Essential for production reliability and incident response. Competes with application performance monitoring (APM) specialists but requires broader systems thinking.

Monitoring & Observability เชถเซเช‚ เช›เซ‡

Monitoring and observability are how you understand what's happening in production systems. Monitoring tells you when something is wrong (alerts). Observability tells you why it's wrong (debugging with logs, metrics, traces). Essential for DevOps, SRE, and backend roles. - Production reliability: Can't fix what you can't see

๐Ÿ”ง เชŸเซ‚เชฒเซเชธ เช…เชจเซ‡ เช‡เช•เซ‹เชธเชฟเชธเซเชŸเชฎ
DatadogPrometheusGrafanaNew RelicSplunkELK StackHoneycombOpenTelemetryPagerDutySentryJaegerCloudWatch

๐Ÿ“‹ เชคเชฎเซ‡ เชถเชฐเซ‚ เช•เชฐเซ‹ เชคเซ‡ เชชเชนเซ‡เชฒเชพเช‚

Devops Ci CdBackend DevelopmentKubernetes Docker

๐Ÿ’ฐ เชชเซเชฐเชฆเซ‡เชถ เชชเซเชฐเชฎเชพเชฃเซ‡ เชชเช—เชพเชฐ

เชชเซเชฐเชฆเซ‡เชถเชœเซเชจเชฟเชฏเชฐเชฎเชงเซเชฏเชฎเชธเชฟเชจเชฟเชฏเชฐ
USA$110k$155k$220k
UKยฃ65kยฃ100kยฃ150k
EUโ‚ฌ70kโ‚ฌ105kโ‚ฌ160k
CANADAC$115kC$160kC$235k

๐ŸŽ“ เชชเซเชฐเชฎเชพเชฃเชชเชคเซเชฐเซ‹

Datadog Certified AssociateSplunk Admin CertificationAWS Certified DevOps Engineer - Professional

๐ŸŽฏ Monitoring & Observability เชจเซ‹ เช‰เชชเชฏเซ‹เช— เช•เชฐเชคเซ€ เช•เชฐเชฟเชฏเชฐ

Cloud Architect
Devops Engineer
Observability Engineer
Site Reliability Engineer

โš– เชธเชพเชฅเซ‡ เชธเชฐเช–เชพเชฎเชฃเซ€ เช•เชฐเซ‹

Devops Ci CdKubernetes DockerCybersecurity

โ“ FAQ

Monitoring vs Observability, are they the same thing?
Monitoring tells you WHEN something is wrong (alerts, thresholds). Observability tells you WHY it's wrong (logs, metrics, traces, debugging). Modern systems need both: monitoring + observability. Monitoring = metrics-driven (do we have a problem?). Observability = data-driven (why do we have a problem?). Build observability first, monitoring alerts follow.
Should I start with Prometheus or Datadog?
Prometheus: free, open-source, pull-based metrics, best for self-hosted Kubernetes clusters. Requires operational overhead (storage, alerting setup). Datadog: SaaS, push-based, includes logs + traces + APM, $$ per metric. For learning: start Prometheus (free). For production SaaS: Datadog or New Relic (operational simplicity). Many use both: Prometheus for internal metrics, Datadog for external monitoring.
What are the 3 pillars of observability?
Logs (what happened): request logs, error messages, application events. Metrics (how much): CPU usage, request count, latency, error rates. Traces (where time spent): distributed tracing across microservices, showing request flow through services. All three together give you the picture: metrics show the symptom, logs show the context, traces show the path.
How do I handle high cardinality metrics without breaking my bill?
High cardinality = many unique label combinations (e.g., per-user metrics). Problem: cardinality explosions double your Datadog bill. Solution: (1) avoid unbounded labels, (2) use tag groups/exclusions, (3) pre-aggregate on client side, (4) use sampling for noisy metrics, (5) switch to count metrics instead of gauge. Monitor cardinality growth in Datadog metrics explorer before it surprises you.
What makes a good SLO (Service Level Objective)?
SLO = target uptime % (e.g., 99.9% = 'three nines'). Start with 99% (3.7h downtime/month). Good SLOs: (1) based on user impact not infrastructure, (2) achievable but aspirational, (3) business-aligned (not arbitrary), (4) tracked with error budgets. Don't just copy Google's SLOs. If you're doing 99.99%, you need on-call rotations and expense approval.
How do I debug a distributed trace that's slow?
Use Jaeger or Honeycomb. Find slowest span: (1) end-to-end latency via trace timeline, (2) identify service with worst duration, (3) check service logs at that timestamp, (4) look for blocking calls (DB queries, network waits), (5) check concurrent spans (parallelism opportunities). Correlation: trace ID in logs helps jump between traces and logs. Tag traces with user ID / request ID early.
What's the cost-benefit of OpenTelemetry vs proprietary agents?
OpenTelemetry: vendor-agnostic, no lock-in, more instrumentation work, future-proof. Proprietary (Datadog agent, New Relic): easier setup, built-in optimizations, vendor lock-in. Best practice: instrument with OpenTelemetry, export to your chosen backend (Datadog/Honeycomb/GCP). Gives you portability: switch backends without re-instrumenting.

เช–เชพเชคเชฐเซ€ เชจเชฅเซ€ เช•เซ‡ เช† เช•เซŒเชถเชฒเซเชฏ เชคเชฎเชพเชฐเชพ เชฎเชพเชŸเซ‡ เช›เซ‡?

เช•เชฐเชฟเชฏเชฐ เชฎเซ‡เชš เชŸเซ‡เชธเซเชŸ เช†เชชเซ‹ โ€” เช…เชฎเซ‡ เชฏเซ‹เช—เซเชฏ เชŸเซเชฐเซ‡เช•เซเชธ เชธเซ‚เชšเชตเซ€เชถเซเช‚.

เชฎเชพเชฐเชพ เชถเซเชฐเซ‡เชทเซเช -เชซเชฟเชŸ เช•เซŒเชถเชฒเซเชฏเซ‹ เชถเซ‹เชงเซ‹ โ†’

เชคเชฎเชพเชฐเซ‹ เช†เชฆเชฐเซเชถ เช•เชฐเชฟเชฏเชฐ เชชเชพเชฅ เชถเซ‹เชงเซ‹

2,521 เช•เชพเชฐเช•เชฟเชฐเซเชฆเซ€เช“เชฎเชพเช‚ เช•เซŒเชถเชฒเซเชฏ-เช†เชงเชพเชฐเชฟเชค เชฎเซ‡เชšเชฟเช‚เช—. เชฎเชซเชค.

เช•เชฐเชฟเชฏเชฐ เชฎเซ‡เชš เชŸเซ‡เชธเซเชŸ เช†เชชเซ‹ โ€” เชฎเชซเชค โ†’