Overview

We led the full lifecycle design, implementation, and deployment of a comprehensive observability platform using Datadog across a complex, distributed enterprise-grade environment spanning AWS EC2, ECS, EKS, and serverless Lambda functions. The goal was to unify application performance monitoring (APM), real user monitoring (RUM), logs, infrastructure metrics, custom alerts, and dashboards across multiple environments (dev, staging, production) ensuring end-to-end visibility into system health and user experience.

Challenge: 

The existing infrastructure lacked unified monitoring, resulting in fragmented visibility across 15+ microservices, delayed incident response (MTTR >4 hours), and limited insight into user experience issues affecting business KPIs.

To achieve this, we implemented multi-layered Datadog integrations:

• Application Performance Monitoring (APM):

  1. We instrumented backend services using the Datadog SDK to generate distributed traces across microservices, capturing service dependencies, request flows, and performance bottlenecks.
  2. For serverless workloads (AWS Lambda), we integrated via a custom Serverless Framework plugin that auto-injected the required Datadog Lambda layer at deployment time, enabling cold-start monitoring and execution tracing.
  3. On ECS, we deployed a sidecar container (Datadog Agent) in task definitions, securely retrieving API keys from AWS Secrets Manager with IAM role-based authentication.
  4. On EKS, we deployed the Datadog Operator, which managed DaemonSet-based agents across all nodes (20+ nodes), enabling automatic instrumentation and telemetry collection with zero application code changes.
     
     

• Real User Monitoring (RUM):

  1. We integrated the Datadog RUM agent directly into the frontend JavaScript codebase (React/Angular), capturing user interactions, page load times, JavaScript errors, and Core Web Vitals.
  2. Our Frontend application was using S3 + CloudFront CDN with cache invalidation strategies, ensuring updated assets were published post-integration.
  3. We enabled session replay, click tracking, heatmaps, and frontend error capture allowing immediate debugging of UX issues in production with full user journey context and performance correlation.
     
     

• Logs Aggregation & Correlation:

  1. We centralized logs from all environments using Datadog Agents on ECS/EC2 and Kubernetes clusters, processing 500GB+ daily log volume. We configured advanced log parsing rules, custom log pipelines, and structured logging (JSON format) at the application level.
  2. We leveraged log correlation with APM traces using trace IDs, enabling seamless root-cause analysis across distributed services and reducing debugging time by 70%.
     
     

• Infrastructure & Custom Monitoring:

  1. We installed Datadog agents on 25+ Linux servers and monitored databases (MongoDB, PostgreSQL, Redis) via native integrations with custom query monitoring.
  2. We created over 80+ custom monitors (thresholds, anomaly detection, service checks, SLO tracking) to track CPU, memory, disk usage, request latency, error rates, database performance, and application-specific business metrics.
  3. We implemented intelligent alerting via email, Slack, PagerDuty, and webhook integrations with escalation policies and alert fatigue reduction strategies.
     
     

• Dashboards & Business KPIs:

  1. We designed 15+ interactive, role-specific dashboards for DevOps, SREs, product teams, and business stakeholders.
  2. We integrated Datadog anomaly detection to automatically flag deviations in key metrics like transaction success rate, response time, throughput, and revenue-impacting events.
  3. We used runtime metrics derived from traces and logs to monitor critical business workflows, A/B test performance, and conversion funnel analytics.

Results:

• The solution reduced mean time to detect (MTTD) from 45 minutes to 3 minutes and mean time to resolve (MTTR) by over 75% (from 4+ hours to <1 hour), enabled proactive issue identification preventing 12+ production incidents, and provided actionable insights during Black Friday traffic spikes (3x normal load).
• All configurations were version-controlled via Terraform/Helm and automated via CI/CD pipelines, ensuring consistency, auditability, and disaster recovery capabilities.
• This project demonstrates deep expertise in cloud-native observability, distributed tracing, infrastructure-as-code (IaC), SRE practices, and real-time monitoring strategy — essential for modern, scalable applications handling enterprise-scale traffic and business-critical workloads.

Skills

• Datadog (APM, RUM, Logs, Monitors, Dashboards)
• AWS (ECS, EKS, Lambda, EC2, S3, CloudFront, Secrets Manager)
• Serverless Framework & CI/CD Pipelines
• Kubernetes & Helm (EKS)
• Distributed Tracing & OpenTelemetry Concepts
• Infrastructure as Code (IaC)
• Log Management & Correlation
• Real User Monitoring (RUM)
• Anomaly Detection & Alerting Strategies
• Microservices Observability
• System Design & Scalable Architecture