Observability Engineering From Beginner to Master – Live Training

What will I Learn by end of this course?

• The 3 pillars of Observability: Metrics, Logs, and Traces

• Set up and configure Prometheus + Grafana for real-time monitoring

• Build centralized logging pipelines with ELK Stack and Loki

• Implement distributed tracing with OpenTelemetry, Jaeger, and Zipkin

• Use enterprise APM tools: Dynatrace, Datadog, and New Relic

• Monitor cloud-native workloads on AWS, Azure, and GCP

• Define SLOs, SLAs, error budgets, and build alerting workflows

• Integrate observability into CI/CD pipelines and DevOps workflows

• Apply observability in performance testing and capacity planning

• Tackle real-world capstone projects and prepare for interviews

Salient Features:

• 35 Hours of Live Training along with recorded videos

• Lifetime access to the recorded videos

• Course Completion Certificate

Course syllabus:

Module 1: Introduction to Observability

1.1 What is Observability?

• Monitoring vs. Observability — Key differences

• The 3 pillars: Metrics, Logs, and Traces

• Observability-Driven Development (ODD)

• The role of Observability in SRE, DevOps, and Performance Engineering

1.2 Observability Architecture

• Telemetry data types and collection strategies

• Push vs. Pull based metrics collection

• The Observability pipeline: Instrumentation → Collection → Storage → Visualization

• Overview of the OpenTelemetry (OTel) ecosystem

1.3 Tooling Landscape

• Open-source tools: Prometheus, Grafana, ELK, Jaeger, OpenTelemetry

• Enterprise tools: Dynatrace, Datadog, New Relic, AppDynamics

• Cloud-native tools: AWS CloudWatch, Azure Monitor, Google Cloud Operations

• When to use open-source vs. enterprise vs. cloud-native solutions

Module 2: Metrics & Monitoring with Prometheus & Grafana

2.1 Prometheus Fundamentals

• Prometheus architecture: Scraper, TSDB, Alertmanager

• Prometheus data model: metrics types — Counter, Gauge, Histogram, Summary

• PromQL — writing powerful queries from scratch

• Configuring scrape jobs and service discovery

2.2 Instrumenting Applications

• Instrumenting Java, Python, and Node.js apps with Prometheus client libraries

• Exposing /metrics endpoints and custom metrics

• Pushgateway for short-lived batch jobs

• Node Exporter, JMX Exporter, and community exporters

2.3 Grafana Dashboarding

• Installing and configuring Grafana

• Connecting Prometheus, InfluxDB, Loki, and cloud data sources

• Building dashboards: panels, variables, annotations, and drill-downs

• Grafana alerting: rules, notification channels, and silences

• Pre-built dashboards for JVM, Kubernetes, Node, and databases

2.4 Hands-On Lab

• Deploy a sample microservice application with Prometheus + Grafana

• Write PromQL queries, build a full operational dashboard

Module 3: Log Management with ELK Stack & Loki

3.1 Centralized Logging Concepts

• Why centralized logging matters in distributed systems

• Structured vs. unstructured logging best practices

• Log levels, correlation IDs, and log enrichment

3.2 ELK Stack Deep Dive

• Elasticsearch: indexing, shards, replicas, and mappings

• Logstash: pipelines, filters, grok patterns, and output plugins

• Kibana: Discover, Dashboards, Lens, and KQL queries

• Filebeat and Metricbeat as lightweight shippers

• Index lifecycle management (ILM) and data streams

3.3 Loki — Logs for Grafana

• Loki architecture and how it differs from Elasticsearch

• Promtail, Fluentd, and Fluentbit as log collectors

• LogQL — querying and filtering logs in Grafana

• Correlating logs and metrics in a single Grafana dashboard

3.4 Hands-On Lab

• Set up ELK stack, ship application logs, and build a Kibana dashboard

• Set up Loki + Promtail, correlate logs with Prometheus metrics

Module 4: Distributed Tracing with OpenTelemetry & Jaeger

4.1 Distributed Tracing Fundamentals

• What is distributed tracing and why it matters for microservices

• Traces, spans, context propagation, and trace IDs

• Sampling strategies: head-based, tail-based, adaptive

4.2 OpenTelemetry (OTel)

• OTel architecture: SDK, API, Collector, Exporters

• Auto-instrumentation vs. manual instrumentation

• Instrumenting Java, Python, and Node.js services with OTel

• OTel Collector pipeline: receivers, processors, exporters

4.3 Jaeger & Zipkin

• Jaeger architecture: Agent, Collector, Query, UI

• Deploying Jaeger (all-in-one and production mode)

• Searching traces, analyzing latency, and identifying bottlenecks

• Zipkin as an alternative — comparison with Jaeger

4.4 Hands-On Lab

• Instrument a multi-service app with OTel and visualize traces in Jaeger

• Trace a slow request end-to-end across 3 microservices

Module 5: APM Tools — Dynatrace & Datadog

5.1 Introduction to APM

• APM vs. observability — what APM adds beyond metrics and traces

• OneAgent vs. agentless monitoring approaches

• Business impact vs. technical metrics

5.2 Dynatrace

• Dynatrace architecture: OneAgent, ActiveGate, Smartscape

• AI-powered root cause analysis with Davis AI

• Full-stack monitoring: hosts, services, databases, containers

• Dynatrace Query Language (DQL) for advanced analytics

• Dynatrace in Kubernetes / cloud environments

5.3 Datadog

• Datadog agent installation and configuration

• Infrastructure monitoring, APM, and Log Management

• Service maps, flame graphs, and trace analytics

• Datadog dashboards, monitors, and SLO tracking

• Datadog Synthetic Monitoring and Real User Monitoring (RUM)

5.4 New Relic Overview

• New Relic One platform and NRQL basics

• Browser monitoring, mobile monitoring, and synthetics

5.5 Hands-On Lab

• Install Dynatrace OneAgent, monitor a Java web app, and analyze a problem

• Configure Datadog APM, trace a slow API, and build an SLO dashboard

Module 6: Cloud-Native Observability — AWS, Azure, and GCP

6.1 AWS Observability

• Amazon CloudWatch: metrics, logs, dashboards, and alarms

• CloudWatch Container Insights for ECS and EKS

• AWS X-Ray for distributed tracing

• CloudWatch Synthetics for endpoint monitoring

• AWS CloudTrail for audit logging

6.2 Azure Monitor

• Azure Monitor architecture: Metrics, Logs (Log Analytics), and Alerts

• Application Insights for APM and user behavior analytics

• KQL (Kusto Query Language) for log analysis

• Azure Workbooks and dashboards

6.3 Google Cloud Operations

• Cloud Monitoring: metrics, uptime checks, and dashboards

• Cloud Logging and Log-based metrics

• Cloud Trace and Cloud Profiler

6.4 Hands-On Lab

• Monitor a containerized app on AWS using CloudWatch + X-Ray

• Set up Application Insights on Azure for a web app

Module 7: Alerting, Incident Management & SLOs/SLAs

7.1 SRE Concepts

• SLI (Service Level Indicator), SLO (Objective), and SLA (Agreement)

• Error budgets: definition, calculation, and burn rate alerts

• Reliability vs. velocity tradeoffs

7.2 Prometheus Alertmanager

• Alerting rules: syntax, labels, and severity levels

• Alertmanager configuration: routes, receivers, inhibition, silences

• Alert grouping and deduplication strategies

7.3 Incident Management Integration

• PagerDuty: on-call schedules, escalation policies, and runbooks

• OpsGenie integration with Prometheus and Grafana

• Post-incident reviews and blameless retrospectives

7.4 Hands-On Lab

• Write alert rules in Prometheus, route to Alertmanager, notify via Slack

• Build SLO dashboards in Grafana with error budget burn rate panels

Module 8: Observability in CI/CD & DevOps Pipelines

8.1 Shift-Left Observability

• Embedding observability from the development phase

• Instrumentation standards as part of Definition of Done

• Observability gates in CI/CD pipelines

8.2 Jenkins & GitHub Actions Integration

• Emitting build metrics to Prometheus / InfluxDB from Jenkins

• GitHub Actions: monitoring workflow performance and failures

• Grafana dashboards for pipeline health and deployment frequency

8.3 GitOps and Continuous Deployment

• ArgoCD observability: sync status, health checks, and alerts

• Canary deployments and feature flags with observability gates

• Deployment frequency, lead time, MTTR, and change failure rate (DORA metrics)

8.4 Hands-On Lab

• Build a Jenkins pipeline that pushes build metrics to InfluxDB and Grafana

• Instrument a GitHub Actions workflow and alert on failures

Module 9: Performance Engineering & Observability

9.1 Observability-Driven Performance Testing

• Correlating JMeter/Gatling test results with application metrics

• Using Grafana k6 for performance testing with built-in metrics export

• Real-time Grafana dashboards during load tests

9.2 Identifying Performance Bottlenecks

• CPU throttling, memory pressure, GC overhead — identifying via metrics

• DB slow query detection via Prometheus exporters and APM traces

• Network latency and connection pool saturation analysis

9.3 Capacity Planning with Observability Data

• Using Prometheus + Grafana for trend analysis and forecasting

• Horizontal vs. vertical scaling decisions driven by observability

• Kubernetes HPA/VPA and its interaction with observability data

9.4 Hands-On Lab

• Run a JMeter load test on a microservice and correlate with Dynatrace/Grafana

• Identify and document a performance bottleneck using traces and metrics

Module 10: Capstone Project & Interview Preparation

10.1 Capstone Project

• Deploy a multi-tier microservices application (frontend, backend, database)

• Instrument with OpenTelemetry (metrics, logs, traces)

• Set up end-to-end observability stack: Prometheus + Grafana + Loki + Jaeger

• Define SLOs, configure alerting, and integrate with Slack notifications

• Simulate incidents and troubleshoot using the observability stack

• Present findings with a documented runbook

10.2 Interview Preparation

• Top 50 Observability interview questions and model answers

• Scenario-based questions: How do you debug a latency spike?

• Tool comparison questions: Prometheus vs. Datadog, ELK vs. Loki

• Resume tips for Observability / SRE / DevOps roles

• Mock interview sessions with feedback

Frequently Asked Questions (FAQs) – Observability:

1. Who can join this course?
DevOps Engineers, SREs, Cloud Engineers, Developers, and Monitoring professionals.

2. Are prerequisites required?
Basic Linux and Cloud knowledge is helpful but not mandatory.

3. Which tools will be covered?
Prometheus, Grafana, ELK, Loki, OpenTelemetry, Jaeger, Dynatrace, Datadog, AWS CloudWatch, Azure Monitor, and more.

4. Is the training practical?
Yes, the course includes hands-on labs and real-time use cases.

5. Will there be a project?
Yes, a capstone project is included for practical experience.

6. Is interview preparation included?
Yes, interview questions, mock sessions, and resume tips are provided.

7. Will recordings be available?
Yes, session recordings will be shared.

8. Will I receive course materials?
Yes, study materials and lab guides will be provided.

9. Is this course beginner-friendly?
Yes, the course starts from fundamentals and progresses to advanced topics.

10. Will I receive a certificate?
Yes, a Course Completion Certificate will be provided after successful completion.

How can I enroll for this course?

For any other details, Call me or Whatsapp me on +91-9940012962

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