Day 5 Telemetry Pipeline - vinoji2005/GitHub-Repository-Structure-90-Days-Observability-Mastery GitHub Wiki

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📘 Day 5 — Telemetry Pipeline: How Logs, Metrics & Traces Flow in an Observability Platform

GitHub Wiki Format

🎯 Learning Objectives

By the end of Day 5, readers will clearly understand:

  • What a telemetry pipeline is

  • Why every monitoring system uses a collection → processing → storage → visualization pipeline

  • The components of a telemetry pipeline (agent, collector, processor, backend, dashboard)

  • How logs, metrics, and traces flow through the pipeline

  • Differences across Prometheus, ELK, Datadog, Cloud providers, and OpenTelemetry

  • Real architecture examples

  • Hands-on labs

  • Interview questions

This is a foundational chapter in your 90-day journey.

1️⃣ What Is a Telemetry Pipeline?

A telemetry pipeline is the pathway through which logs, metrics, traces, and events travel inside an observability ecosystem.

In simple terms:

Telemetry Pipeline = How monitoring data is collected, processed, sent, stored, and visualized.

Telemetry includes:

  • Logs

  • Metrics

  • Traces

  • Events

Every tool — Prometheus, Datadog, App Insights, Kibana, X-Ray, Dynatrace — uses some form of this pipeline.

2️⃣ Common Telemetry Pipeline Architecture (Vendor-Neutral) 





[ Application / VM / Container ]
                ↓
        ┌──────────────────┐
        │   Agent / SDK    │
        └──────────────────┘
                ↓
        ┌──────────────────┐
        │   Collector      │
        └──────────────────┘
                ↓
        ┌──────────────────┐
        │   Processor      │
        └──────────────────┘
                ↓
        ┌──────────────────────────┐
        │   Backend / Storage      │
        └──────────────────────────┘
                ↓
        ┌──────────────────────────┐
        │ Dashboards / Alerts      │
        └──────────────────────────┘

Pipeline Stages:

Stage | Purpose -- | -- Agent / SDK | Collects raw telemetry from systems Collector | Receives data in one place Processor | Filtering, sampling, enrichment, batching Exporter | Sends to backend systems Backend | Metrics DB, Log index, Trace store Query Layer | PromQL, KQL, Lucene, SQL Dashboards | Grafana, Kibana, Datadog, Workbooks Alerts | Alertmanager, PagerDuty

OTel = one collector → many backends.

5️⃣ Pipeline Flow for Logs, Metrics, & Traces (Visual Text)

Logs:





App → Filebeat/FluentBit/OTel → Collector → Log Index → Search (Kibana)

Metrics:





App → Prometheus Exporter → Prometheus → Grafana → Alerts

Traces:





App → OTel SDK → Collector → Trace Store → Jaeger / Tempo

This is why modern observability platforms integrate all three.

6️⃣ Telemetry Pipeline in Real Tools

✔ Prometheus Stack

  • node_exporter

  • cadvisor

  • Prometheus server

  • grafana

  • alertmanager

✔ ELK Stack

  • Filebeat

  • Metricbeat

  • Logstash

  • Elasticsearch

  • Kibana

✔ Azure

  • Azure Monitor Agent (AMA)

  • Log Analytics

  • Metrics DB

  • App Insights

✔ AWS

  • CloudWatch Agent

  • CloudWatch Logs

  • CloudWatch Metrics

  • X-Ray traces

✔ GCP

  • Ops Agent

  • Logging

  • Monitoring

  • Trace / Profiler

This chapter is vendor-neutral so everyone can follow.

7️⃣ Hands-On Labs (Day 5)

🔧 Lab 1 — Spin Up an OTel Collector 





docker run -p 4317:4317 -p 55681:55681 otel/opentelemetry-collector:latest

🔧 Lab 2 — Send Test Metrics to Collector 





otel-load-generator --otlp-endpoint=localhost:4317

🔧 Lab 3 — Inspect Collector Pipelines

Open config:





receivers:
  otlp:
    protocols:
      grpc:
      http:
exporters:
logging:
loglevel: debug

🔧 Lab 4 — Build a Mini Observability Pipeline

Choose one:

Option A — Prometheus pipeline

Option B — Elastic logs pipeline

Option C — AWS CloudWatch pipeline

Option D — Azure Monitor pipeline

Depending on what readers want to practice.

8️⃣ Real-World Architecture Example 





Apps → OTel SDK → OTel Collector → Kafka → 
       → Mimir (metrics)
       → Loki (logs)
       → Tempo (traces)
Grafana → Dashboards + Alerts
PagerDuty → Incident notifications

This pipeline is used in:

  • Enterprises

  • Cloud-native architectures

  • Kubernetes platforms

  • Multi-cloud deployments

9️⃣ Interview Questions (Day 5)

Beginner

  1. What is a telemetry pipeline?

  2. What is the role of an agent?

  3. What are logs, metrics, and traces?

Intermediate

  1. How do collectors differ from agents?

  2. Why are logs expensive?

  3. Explain the difference between batch vs stream processing.

Senior

  1. Design a telemetry pipeline for 200+ microservices.

  2. How do you reduce telemetry cost?

  3. What is sampling and why is it needed?

Architect

  1. Build a multi-cloud observability pipeline using OTel.

  2. How do you prevent pipeline backpressure?

  3. How do you enrich telemetry with metadata?

🔟 Your Learning Notes 





What I learned today:
Pipelines I want to build:
Parts I still need clarity on:
Tools I want to explore:
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