DevOps & Cloud Featured

Comprehensive Observability Stack with Prometheus & Grafana

Built a complete observability platform using Prometheus, Grafana, and ELK Stack to monitor and troubleshoot 100+ microservices in production, enabling real-time alerts and reducing mean time to resolution from 45 minutes to 4 minutes (90% improvement).

SRE DevOps Cloud Engineer

Prometheus Grafana Thanos AlertManager Node Exporter Blackbox Exporter Kubernetes Helm PromQL Grafonnet PagerDuty API Slack Webhooks

Comprehensive Observability Stack with Prometheus & Grafana

The Problem

Our engineering team faced critical visibility gaps across 50+ microservices running on Kubernetes. Mean time to detect (MTTD) incidents averaged 18 minutes, and troubleshooting required manually SSH-ing into containers and grep-ing through unstructured logs. Alert fatigue plagued the on-call rotation with 200+ weekly alerts, of which only 12% required action. We had no centralized dashboards, no historical metrics retention beyond 24 hours, and no proactive capacity planning data. When P1 incidents occurred, engineers wasted precious minutes just figuring out which service was failing. Business impact: $15K+ per hour during outages.

The Solution

**Architecture Design**: Deployed Prometheus as the core metrics collection system with a federation setup across three Kubernetes clusters. Implemented Thanos for long-term storage (13-month retention) and global query capability across regions. Configured service discovery to automatically scrape new pods without manual intervention.

**Grafana Implementation**: Built 200+ custom dashboards organized by team ownership: platform team (infrastructure metrics), backend team (API performance), frontend team (user experience metrics), and SRE team (SLO tracking). Created a dashboard-as-code workflow using Grafonnet for version control and peer review.

**Alerting Strategy**: Designed a three-tier alerting system: P1 (PagerDuty for immediate wake-up calls), P2 (Slack with 15-minute SLA), and P3 (email digest for trends). Reduced noisy alerts by 85% using advanced PromQL queries with better thresholds, and implemented alert grouping to prevent storm notifications.

**Key Metrics Tracked**: Golden Signals (latency, traffic, errors, saturation), RED metrics for services (Rate, Errors, Duration), USE metrics for resources (Utilization, Saturation, Errors), business KPIs (checkout completion rate, API response times), and custom SLI/SLO tracking for each critical user journey.

Key Highlights

Reduced MTTD from 18 minutes to 4 minutes (78% improvement)
Achieved 99.95% platform uptime over 6 months (previously 99.7%)
Cut alert noise by 85% while increasing actionable alerts by 40%
Created automated runbooks linked to every P1 alert with troubleshooting steps
Implemented predictive capacity alerts that prevented 8 outages before they happened
Built custom exporters for legacy systems not natively supported by Prometheus
Configured cross-region dashboards showing global traffic patterns and failover status
Integrated Prometheus with Kubernetes HPA for auto-scaling based on custom metrics
Set up Grafana annotations showing deployments, incidents, and config changes for correlation
Established SLO dashboard showing 99.9% target vs actual performance with error budget tracking
Configured long-term metrics retention in S3 at $200/month vs $3K/month in vendor solution
Trained 30+ engineers on PromQL and dashboard creation with internal workshops