§ The Problem

Development teams were spending 2+ hours per deployment managing infrastructure: provisioning VMs, configuring networking, setting up load balancers, and debugging deployment failures. This created a bottleneck where platform engineers became gatekeepers, and developers couldn't iterate quickly.

§ Solution Architecture

Built a declarative, GitOps-driven platform where developers define application requirements in YAML, and the platform handles infrastructure provisioning, deployment orchestration, and rollback safety automatically.

§ Key Technical Decisions

§ Code Sample: Deployment Orchestration

# Simplified deployment orchestrator with health checks and rollback
import subprocess
import time
from typing import Dict, Optional

class DeploymentOrchestrator:
    def __init__(self, app_name: str, namespace: str):
        self.app_name = app_name
        self.namespace = namespace
        self.rollback_revision = None
    
    def deploy(self, manifest_path: str) -> bool:
        """Deploy application with automatic rollback on failure"""
        # Store current revision for rollback
        self.rollback_revision = self._get_current_revision()
        
        # Apply Kubernetes manifest
        if not self._apply_manifest(manifest_path):
            return False
        
        # Wait for rollout to complete
        if not self._wait_for_rollout():
            self._perform_rollback()
            return False
        
        # Run health checks
        if not self._run_health_checks():
            self._perform_rollback()
            return False
        
        return True
    
    def _run_health_checks(self, timeout: int = 60) -> bool:
        """Validate deployment health"""
        checks = [
            self._check_pod_health(),
            self._check_service_endpoints(),
            self._smoke_test_api()
        ]
        return all(checks)
    
    def _perform_rollback(self):
        """Automatic rollback to previous stable revision"""
        print(f"Deployment failed. Rolling back to revision {self.rollback_revision}")
        subprocess.run([
            "kubectl", "rollout", "undo",
            f"deployment/{self.app_name}",
            f"--to-revision={self.rollback_revision}",
            f"-n {self.namespace}"
        ])

§ What I Learned

• Developer UX matters more than technical elegance: Initially over-engineered with complex abstractions. Simplified to "push code, get deployment" model increased adoption 3x.
• Rollback safety is non-negotiable: First version had manual rollback. After 2 production incidents, automated rollback on health check failures became mandatory.
• Observability from day one: Added structured logging and metrics early. Debugging production issues without logs would've been impossible.
• Drift detection matters: Manual changes to infrastructure caused subtle bugs. Added Terraform drift detection in CI to catch manual modifications.

§ The Problem

Running microservices across AWS and Azure with inconsistent deployment practices, no service-to-service authentication, and manual certificate management. Teams were deploying via kubectl commands, leading to configuration drift and "works on my machine" deployment failures.

§ Solution Architecture

Implemented GitOps-driven Kubernetes platform with Istio service mesh for traffic management, observability, and mTLS. ArgoCD monitors Git repositories and automatically syncs cluster state, eliminating manual kubectl operations.

§ Key Challenges & Solutions

§ What I Learned

• Service mesh adds complexity—justify it: Istio solved real problems (mTLS, observability) but increased debugging difficulty. Worth it for 50+ services, overkill for 5.
• GitOps prevents drift, but needs discipline: Teams tried manual hotfixes during incidents. Strict Git-only policy prevented production surprises.
• Multi-cloud is hard—abstract when necessary: AWS and Azure Kubernetes differences required abstraction layer. Helm charts with cloud-specific values worked well.

§ The Problem

Production incidents took 45+ minutes to detect because teams were SSH-ing into servers to grep logs. No centralized logging, no structured metrics, no way to trace requests across microservices. "It's slow" bug reports had no data to debug.

§ Three Pillars of Observability

§ Real Incident: Database Connection Pool Exhaustion

§ What I Learned

• Alert fatigue kills observability: Started with 200+ alerts. Reduced to 15 high-signal alerts. Ignored alerts are worse than no alerts.
• Dashboards for humans, not metrics junkies: Built role-specific dashboards (developer, SRE, business). One "system health" dashboard everyone understands.
• Structured logging is mandatory: Grep-ing unstructured logs is painful. JSON logs with consistent fields enable powerful queries.
• Retention costs add up: 90-day Prometheus retention cost more than compute. Downsampled old metrics, kept high-res for 7 days.