How do you implement AWS monitoring, logging, and alerting?

Long Answer

A robust AWS monitoring, logging, and alerting approach treats observability as a product: measurable, consistent, and cost-aware. Start with service SLOs (availability, p95 latency, error rate) and define golden signals (RED/USE) per workload. Across regions and accounts, standardize resource tags (env, service, owner, cost_center, confidentiality) so dashboards, budgets, and alert routing work predictably.

Metrics and traces. At the edge and compute layers—ALB, API Gateway, Lambda, ECS/EKS—publish CloudWatch metrics (requests, errors, duration, saturation). Adopt Embedded Metric Format (EMF) so JSON logs become near-real-time metrics without sidecar scrapers. Enable AWS X-Ray or OpenTelemetry to stitch traces across microservices, queues, and databases. Container Insights and Amazon Managed Prometheus can ingest kube metrics while CloudWatch stays the paging control plane.

Logging. Standardize JSON with keys like request_id, trace_id, tenant, status, latency_ms. Send app/stdout logs to CloudWatch Logs. Use subscription filters → Kinesis Firehose → S3 (data lake) and Amazon OpenSearch for fast search; mirror security-relevant streams to a third-party SIEM (Datadog/Splunk/New Relic). Partition S3 by account/region/service/date; apply lifecycle to Glacier Deep Archive for cheap retention. Encrypt with KMS; enable Object Lock and MFA delete to preserve forensics. Normalize schemas so logs, metrics, and traces correlate cleanly.

Monitoring and dashboards. Build per-service CloudWatch dashboards plus a global NOC view. Add anomaly detection bands to handle seasonality. Use CloudWatch Synthetics canaries from every region to detect black-box failures before users do. Track business KPIs (signups/min, orders/min) alongside infra metrics so on-call sees customer impact, not only host-level noise.

Alerting. Page on user-visible symptoms: SLO burn, elevated error rate, rising p95 latency, saturation. Create composite alarms that combine metric math (e.g., errors AND high latency) and log-derived metrics to reduce flapping. Wire alarms → SNS → PagerDuty; send non-urgent diagnostics to Slack/Jira. Document a runbook and owner on every alarm; include deep links to dashboard, trace, and log queries.

CloudTrail and security. Enable organization CloudTrails with data events (S3, Lambda) and send to S3 + CloudWatch Lake; forward high-risk detections to the SIEM. Aggregate GuardDuty, AWS Config, and Security Hub findings through EventBridge; invoke Lambda runbooks to close public S3 ACLs, quarantine EC2, rotate keys, or revoke compromised credentials. Log every remediation to an immutable audit bucket.

Multi-account governance. Use AWS Organizations with delegated admin for CloudWatch, CloudTrail, GuardDuty. Centralize telemetry in a logging account; allow cross-account ingestion via Kinesis roles. Enforce tagging, encryption, and MFA with SCPs; apply Config conformance packs for drift control. Validate IAM posture with Access Analyzer and alert on risky cross-account assumptions.

Third-party tools. Vendors (Datadog, Grafana, Splunk, New Relic) add cross-cloud correlation, RUM, APM, and dependency maps. Export CloudWatch metrics via OpenTelemetry; keep AWS services as the paging and governance plane, while vendors provide advanced UX and analytics.

Cost and reliability. Keep OpenSearch hot tiers lean; push long-tail logs to S3. Review alarms weekly; prune noisy rules; align thresholds to SLOs; auto-suppress during maintenance. Measure MTTA/MTTR, pages per incident, cost per GB ingested, and percent auto-remediated. This disciplined, layered design turns AWS monitoring, logging, and alerting into a durable, low-noise safety net.

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Table

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Common Mistakes

Paging on infrastructure minutiae instead of SLO symptoms, drowning teams in noise. Unstructured logs without trace_id/request_id, making correlation slow. Single-account thinking—no cross-account/region aggregation—so incidents straddle boundaries unseen. Skipping CloudTrail data events, hiding sensitive S3/Lambda activity from audits. One-metric-one-alert without composites or burn-rates, causing flapping. Keeping all logs hot in OpenSearch, exploding costs. Missing runbooks/owners on alarms, extending MTTR. Not testing PagerDuty/Slack/SIEM wiring, so alerts “fire” but no one is paged. Weak tagging and naming, breaking dashboards, budgets, and routing. Ignoring maintenance windows and anomaly bands, so planned changes trigger needless pages.

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Sample Answers (Junior / Mid / Senior)

Junior:
“I’d push JSON logs to CloudWatch Logs, stream them via Firehose to S3 and OpenSearch, and enable org-wide CloudTrail. For metrics, CloudWatch + X-Ray give visibility. Alerts route through SNS to PagerDuty; I’ll add Synthetics canaries.”

Mid:
“I define SLOs, then create composite/burn-rate alerts for error rate and p95. Logs flow to S3/OpenSearch and a SIEM. CloudTrail with data events plus GuardDuty/Config feed EventBridge rules; Lambda runbooks auto-close public S3 or quarantine EC2. Tags drive dashboards and routing.”

Senior:
“Multi-account governance with delegated admins; a central logging account aggregates telemetry. OpenTelemetry exports unify metrics, logs, and traces in Grafana/Datadog. Security Hub findings route through EventBridge to runbooks with audits. Our AWS monitoring, logging, and alerting pages only on symptoms; causes get tickets. We prune noise weekly and manage cost with lifecycle tiers.”

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Evaluation Criteria

Look for a multi-account, multi-region design using CloudWatch metrics/alarms (incl. anomaly detection, composites, burn-rates), CloudTrail with data events, and centralized logs (CloudWatch → Firehose → S3/OpenSearch) under KMS and lifecycle policies. Strong answers route GuardDuty/Config/Security Hub via EventBridge to remediation Lambdas with immutable audits. Expect tagging standards, dashboards, and IaC to reproduce alarms, trails, and rules. Third-party APM/SIEM tools add correlation and RUM while AWS remains the paging/governance plane. Scoring favors noise reduction tied to SLOs, runbooks and ownership on every alert, measured MTTA/MTTR improvements, budgets and quotas for cost control, and black-box canaries. Red flags: per-metric paging, missing data events, no cross-account aggregation, or hoarding logs in hot storage.

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Preparation Tips

Spin up two accounts in AWS Organizations. Enable org CloudTrail with data events, GuardDuty, and Config conformance packs. Stream CloudWatch Logs via Firehose to S3 (partitioned by account/region/service/date) and OpenSearch; encrypt with KMS and set lifecycle to Glacier Deep Archive. Expose RED/USE metrics and X-Ray traces; build SLO dashboards and composite/burn-rate alarms with anomaly bands. Wire EventBridge for GuardDuty/Config to trigger remediation Lambdas and write audits. Integrate Grafana or Datadog via OpenTelemetry; verify cross-account views, tags, and on-call routing. Run a game-day: latency spike, error storm, public S3, and an expired cert. Confirm pages, traces, dashboards, SIEM alerts, and document noise cuts, cost per GB, and MTTR deltas.

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Real-world Context

A fintech split prod/non-prod accounts, centralized CloudTrail and logs, and adopted SLO burn-rate alerts—pages fell 45% while MTTR improved 30%. An e-commerce platform streamed logs to S3/OpenSearch with lifecycle to Glacier, cutting log costs 60% yet keeping 13-month forensics for audits and threat hunting. A SaaS vendor routed GuardDuty findings through EventBridge to Lambda; compromised instances were quarantined in minutes, with Slack/Jira updates linking dashboards and traces. A media company added Synthetics and X-Ray; anomaly bands halved flapping and stabilized launches. A marketplace layered OpenTelemetry with Grafana/Datadog; cross-account views reduced noisy-neighbor isolation time in EKS by 40% and improved incident reviews via consistent tags.

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Key Takeaways

• Treat AWS monitoring, logging, and alerting as a product with SLOs.
  
• Centralize logs to S3/OpenSearch; keep hot tiers lean and encrypted.
  
• Enable org-wide CloudTrail data events; route findings via EventBridge.
  
• Page on symptoms with composite/burn-rate alerts; attach runbooks/owners.
  
• Use vendors for correlation/APM while AWS stays the control plane.

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Practice Exercise

Scenario:
You run multi-region APIs on API Gateway, ALB, Lambda, and ECS/EKS. Data spans RDS and DynamoDB. Leadership demands low-noise paging, 12-month forensics, and automated security response across accounts.

Tasks:

1. Define availability and latency SLOs; identify RED/USE metrics. Instrument CloudWatch (including EMF) and X-Ray/OpenTelemetry; publish per-service dashboards and a NOC view.
   
2. Standardize JSON logs; stream CloudWatch Logs via Firehose to S3 (partitioned) and OpenSearch; encrypt with KMS, enable Object Lock/MFA, and set lifecycle to Glacier Deep Archive.
   
3. Enable org CloudTrail with data events; integrate GuardDuty, Config, and Security Hub. Build EventBridge rules to trigger remediation Lambdas (close public S3, quarantine EC2, rotate keys) and write immutable audits.
   
4. Create composite/burn-rate alarms and anomaly bands; route via SNS → PagerDuty; send investigations to Slack/Jira with links to runbooks, traces, and saved OpenSearch queries.
   
5. Tag resources (env, service, owner, cost_center) to drive alert routing and budgets.
   
6. Run a game-day; record pages/incident, MTTA/MTTR, OpenSearch cost/GB, percent auto-remediated, and noise reduction.
   
   

Deliverable:
A concise deck + screenshots proving a scalable AWS monitoring, logging, and alerting stack that reduces pages, accelerates MTTR, keeps costs predictable, and withstands audits.