Building a Production-Grade Observability Stack on Kubernetes

In modern DevOps practices, observability isn’t optional, it is foundational. While monitoring tells you when something is wrong, observability tells you why. In this post, I share how I engineered and deployed a robust observability stack on Kubernetes, tailored for real-world production workloads, secured with Zero Trust access, and modularly automated using Helm.

This was part of a broader initiative to demonstrate end-to-end DevOps thinking: instrumentation, deployment automation, secure access, and performance tuning.

Project Objective

To build a cloud-native observability system with full coverage across logs, metrics, and traces that can scale with microservice applications, support multi-tenant deployments, and reflect best practices in both architecture and operations.

Why Observability Matters

With applications decomposed into dozens (or hundreds) of services, visibility becomes a critical engineering concern. My goal was to enable:

• Proactive alerting across infrastructure and app tiers
• Traceability of service-to-service calls for debugging latency
• Log aggregation for centralized auditing and compliance
• Secure dashboard access for internal and remote teams

Architecture Overview

The observability stack includes:

• OpenTelemetry Collector: Aggregates traces from instrumented services of an application
• Jaeger: Trace storage and visualization
• Prometheus: Metric scraping and storage
• Grafana: Metric visualization and alerting
• Elasticsearch & Kibana (ECK): Log aggregation and analysis
• Cloudflare Zero Trust: Secure access via OAuth
• Ingress NGINX: Exposes services externally with TLS via my Cloudflare tunnel setup

Deployment Strategy

1. Namespace Isolation

Each component is deployed to its own namespace for modular management and resource isolation such as:

• observability-system
• opentelemetry-system
• elastic-system
• prometheus-system

2. Helm-Powered Installations

I used Helm 3 and curated values.yaml files to drive declarative deployments. Key highlights:

• OpenTelemetry Collector installed with the OpenTelemetry Operator
• Jaeger Operator deployed with production strategy (jaeger-all-in-one avoided)
• Prometheus Operator manages both Prometheus and Alertmanager
• Grafana customized with dashboards
• Elasticsearch + Kibana bootstrapped using Elastic Cloud on Kubernetes (ECK)

All Helm releases were stored in a version-controlled GitHub repo to allow reproducibility and team collaboration.

3. Security Layer

• Access to the Grafana, Kibana, and Jaeger dashboards was locked behind Cloudflare Zero Trust, authenticated via GitHub or Google OAuth — no public endpoints, no basic auth, no exposure.
• Ingress NGINX handles TLS termination via Cloudflare tunnel.
• Secrets are managed via Kubernetes Secret objects (future: migrate to Vault)

Stack Components Deep Dive

OpenTelemetry Collector

Configured for multi-signal routing using receivers and exporters. Traces sent to Jaeger via gRPC, metrics pushed to Prometheus, and logs enriched and forwarded to ECK.

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  jaeger:
    endpoint: jaeger-collector.observability.svc:14250

Prometheus + Grafana

Prometheus scrapes metrics from services and kubelets. Grafana is pre-configured with:

• CPU/memory dashboards
• Service-specific metrics
• Jaeger trace embedding

ECK (Elasticsearch & Kibana)

• Elasticsearch cluster: 1 node (dev), auto-scalable
• Logs ingested via Elastic’s OpenTelemetry distribution (EDOT)
• Kibana dashboards for logs per service

What This Demonstrates

This project wasn’t just about standing up a monitoring stack; it was about end-to-end observability engineering:

• Architectural thinking: how telemetry flows across a distributed system
• Security maturity: protecting dashboards and backend systems
• Deployment automation: reproducible, Helm-powered infrastructure
• Real-world constraints: tuning performance, cleanup strategies, etc.

It reflects my approach to DevOps: design with scale, security, and simplicity in mind.

Resources & Tools

• Helm charts (customized, available upon request)
• OpenTelemetry.io
• Grafana Labs
• Jaeger
• ECK
• EDOT

Next Steps

This observability stack now powers my entire applications deployment stack, allowing trace visualization, dashboarding, and real-time logs for each application service. It provides full insight into microservice-based applications. With proactive alerting, distributed tracing, and structured logging, issues are caught earlier and resolved faster. For a live demo or Helm access, feel free to contact me.

Want to learn how this integrates with my Twitter-Clone microservice app? Check out the walkthrough here.