🔷 Introduction

Modern cloud platforms, especially multi-tenant systems like SDV, Digital Twin, simulators, CI platforms, and Kubernetes clusters, generate massive volumes of logs, metrics, events, and traces.

Traditional logging (tail logs → dump into files → grep) is no longer sufficient.

Observability today is about:

• Understanding system behavior,

• Predicting issues before they happen,

• Providing real-time insights into usage, performance, failures, throttling, and dependencies.

This guide explains how to design and implement enterprise-grade observability using:

• Azure App Insights

• Grafana

• OpenTelemetry

• Azure Monitor + Kusto (KQL)

• Log pipelines

• Usage tracking systems

This is the exact architecture used by cloud-native and automotive SDV platforms.

🔷 1. Why Traditional Logging Fails

Traditional logging introduces several problems:

❌ No correlation between services

You cannot trace a single request across microservices.

❌ No distributed metrics

VM-level metrics don’t explain app-level failures.

❌ Limited querying ability

Grep cannot search across millions of logs.

❌ No real-time alerts

Systems cannot predict failures.

❌ No usage visibility

You can’t understand how developers or APIs are using the platform.

Modern cloud platforms require correlated observability.

🔷 2. Pillars of Modern Observability

1. Metrics

Provide numerical insights into:

• CPU, memory, I/O

• Request rate

• Error rate

• Latency

• Queue length

• Pod restarts

• CI/CD pipeline health

2. Logs

Structured logs for:

• API requests

• System events

• Exceptions

• K8s logs

• Airflow tasks

• Pipeline runs

3. Traces

Distributed tracing connects requests across services:

With trace IDs, you can follow the entire journey.

4. Events

Include:

• Deployment events

• Autoscaling

• Network policy changes

• API throttling events

5. Usage Analytics

Tracks:

• How many users

• Which APIs are used

• Platform-wide adoption

• Workspace usage

• Simulator run counts

Usage data drives investment decisions.

🔷 3. High-Level Observability Architecture

A complete enterprise pipeline looks like this:

🔷 4. Step-by-Step Implementation Guide

STEP 1 — Instrument Applications with OpenTelemetry

Why OpenTelemetry?

Because it’s vendor-neutral, supports all cloud providers, and integrates with:

• Python

• NodeJS

• Go

• Java

• .NET

• C++ (via wrappers)

• K8s operators

Example instrumentation (Python):

Every incoming/outgoing request is now traced.

STEP 2 — Configure an OpenTelemetry Collector

This component receives telemetry and pushes to different backends.

Basic collector config:

This collector forwards traces to Azure Monitor/App Insights.

STEP 3 — Application Insights Setup

App Insights provides:

• End-to-end transaction maps

• Dependency graphs

• Failure analytics

• SQL dependency charts

• Request/response times

• Exception breakdown

Enable sampling

To lower cost and improve performance:

STEP 4 — Configure Kubernetes Observability

Use:

Prometheus + Grafana

For:

• CPU, memory, pod restarts

• Node utilization

• Ingress metrics

• Per-namespace traffic

• Autoscaling signals

• Resource quota alerts

Prometheus Scraping Config (Example):

STEP 5 — Kusto (KQL) for Log Analytics

Kusto Query Language is extremely powerful.

Example: Identify failing APIs

Example: Identify slow APIs

Example: Track user adoption

STEP 6 — Create Grafana Dashboards

Dashboards needed:

Infra Dashboard

• Node CPU/Mem

• Pod restarts

• Net traffic

App Dashboard

• Request count

• Error rate

• API latency

CI/CD Dashboard

• Pipeline duration

• Failure rate

• Stage breakdown

Usage Dashboard

• Active users

• Top APIs

• Platform usage trends

STEP 7 — Alerts & Auto-Remediation

Alerts on:

• 5XX spike

• Latency spike

• Pod restart loops

• Node pressure

• CI/CD failure spike

• Simulator job stuck

Auto-remediation examples:

• Restart failing pods

• Clear stuck Airflow tasks

• Drain unhealthy nodes

• Auto-scale busy workloads

Use:

• Azure Alerts

• Prometheus Alertmanager

• PagerDuty / Slack

STEP 8 — Build a Usage Tracking System

Usage data is crucial for product decisions.

Track:

• User logins

• API calls

• Simulator runs

• Workspace hours

• Pipeline executions

Push data to:

• App Insights custom metrics

• Kusto tables

• Grafana panels

STEP 9 — Create an Observability Strategy Document

Include:

• Telemetry design

• Export patterns

• Sampling rules

• Logging structure

• Naming standards

• Data retention rules

• Privacy considerations

This keeps teams aligned.

🔷 5. Real-World Observability Flow Example (SDV Platform)

Example: VDK Simulation Workflow

1. User triggers simulation

2. API Gateway logs request

3. Backend logs job creation

4. Kubernetes schedules simulation pod

5. Pod emits telemetry to OTel collector

6. Collector → App Insights

7. Sim results pushed to Kusto

8. Grafana dashboard updates

9. Alerts triggered if job stalls

10. Usage stats updated

This forms a closed-loop observability system.

🔷 6. Best Practices Checklist

Logging

✔ Use JSON structured logs
✔ Avoid logging secrets
✔ Add correlation IDs
✔ Add trace IDs

Metrics

✔ Use consistent naming schema
✔ Monitor DORA metrics
✔ Track per-tenant cost

Traces

✔ Trace all internal/external calls
✔ Include dependency mapping

Dashboards

✔ Split infra and app dashboards
✔ Include business KPIs

🔷 Conclusion

Observability is not just operational monitoring — it is a strategic enabler.
With the right architecture:

• Engineering becomes faster

• Failures become predictable

• Issues resolve faster

• Costs become controlled

• Platform usability becomes measurable

Cloud-native observability transforms raw data into engineering intelligence, powering high-performance teams and reliable systems.