Master in Site Reliability Engineering (SRE)

Master the engineering discipline of site reliability. Learn to apply software engineering principles to operations, ensuring highly reliable, scalable, and efficient production systems through automation, measurement, and incident management.

Enroll Now!

Who Should Enroll?

This course is ideal for:

Prerequisites

Essential SRE Tools & Technologies Covered

Prometheus / Grafana

ELK Stack / Splunk

Cloud Platforms (AWS/Azure/GCP)

Kubernetes

Ansible

Terraform

Linux / Operating Systems

Networking Tools

Chaos Engineering (Chaos Mesh)

Alerting Systems (PagerDuty)

Load Balancers

Python / Go

Distributed Tracing (Jaeger)

Automation Frameworks

Our hands-on approach focuses on mastering these industry-standard tools and practices for ultimate system reliability.

Job Roles After This Course

Comprehensive SRE Syllabus: Engineering for Unprecedented Reliability!

Module 1.1: SRE Foundations & Principles

  • Introduction to SRE: Definition, origins, and core philosophies (Google SRE book).
  • Key SRE Concepts: Toil, Error Budgets, SLI (Service Level Indicators), SLO (Service Level Objectives), SLA (Service Level Agreements).
  • Organizational Culture: Blameless Postmortems, Shared Ownership.
  • Building a Culture of Reliability: Incident Management principles, communication.
  • SRE vs. DevOps vs. Traditional Ops.
  • Lab: Defining SLIs/SLOs for a sample service, practicing blameless postmortem analysis.

Concepts Covered:

  • SRE Principles, SLI/SLO/SLA, Error Budget, Toil, Blameless Postmortems, Cultural Aspects.

Expected Outcomes:

  • Understand the core principles and philosophy of SRE.
  • Define and apply SLIs, SLOs, and Error Budgets.
  • Foster a culture of reliability and continuous improvement.

Module 1.2: Advanced Linux & Networking for SRE

  • Deep Dive into Linux System Internals: Processes, memory, I/O, networking stack.
  • Linux Performance Monitoring and Tuning: `strace`, `lsof`, `netstat`, `perf`.
  • Advanced Networking: BGP, DNS, Load Balancing (L4/L7), Proxies, CDNs.
  • Troubleshooting Network and System Issues: Packet analysis, tracing tools.
  • Scripting for Automation and System Management (Python/Bash).
  • Lab: Diagnosing system bottlenecks, network troubleshooting with `tcpdump`/Wireshark, advanced bash/python scripting for sysadmin tasks.

Tools Covered:

  • Linux Utilities (strace, lsof, netstat, iostat), tcpdump/Wireshark, Nginx/HAProxy (conceptual), Python/Bash.

Expected Outcomes:

  • Master advanced Linux system and network diagnostics.
  • Optimize system performance at the OS level.
  • Automate routine operational tasks with advanced scripting.

Module 2.1: Observability: Monitoring & Alerting

  • The Pillars of Observability: Metrics, Logs, Traces.
  • Prometheus: Architecture, data model, PromQL, exporters, alerting.
  • Grafana: Dashboarding, data source integration, alerting.
  • Logging Solutions: ELK Stack (Elasticsearch, Logstash, Kibana) for centralized logging.
  • Alerting Best Practices: Alert fatigue, on-call rotation, PagerDuty/Opsgenie integration.
  • Log Management and Analysis: Structuring logs, anomaly detection in logs.
  • Lab: Deploying Prometheus & Grafana, building dashboards, configuring alerts, setting up centralized logging with ELK.

Tools Covered:

  • Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), Alertmanager, PagerDuty/Opsgenie (conceptual).

Expected Outcomes:

  • Implement comprehensive monitoring and alerting systems.
  • Design effective dashboards for system health.
  • Manage and analyze logs for operational insights.

Module 2.2: Observability: Distributed Tracing & APM

  • Distributed Tracing: Understanding complex microservice interactions.
  • OpenTelemetry: Standards for telemetry data collection.
  • Jaeger/Zipkin: Implementing and analyzing traces.
  • Application Performance Monitoring (APM) Tools: Dynatrace, New Relic, Datadog (overview).
  • Synthetic Monitoring and Real User Monitoring (RUM).
  • Debugging in Distributed Systems.
  • Lab: Instrumenting a microservice for tracing, analyzing traces in Jaeger, simulating performance issues.

Tools & Concepts:

  • Jaeger, OpenTelemetry, Zipkin, APM tools (conceptual), Synthetic/RUM.

Expected Outcomes:

  • Implement distributed tracing for microservices.
  • Analyze performance bottlenecks in complex systems.
  • Utilize APM concepts for proactive issue detection.

Module 3.1: Automation & Infrastructure as Code (IaC)

  • Principles of Automation: Eliminating toil, repeatable processes.
  • Configuration Management with Ansible: Playbooks, roles, idempotency for system setup.
  • Infrastructure as Code (IaC) with Terraform: Managing cloud resources, state management.
  • CI/CD for Infrastructure: Integrating IaC and configuration management into pipelines.
  • Automated Testing for Infrastructure: Linting, unit tests, integration tests for IaC.
  • Runbooks and Automation: Creating automated responses to common incidents.
  • Lab: Automating server provisioning with Ansible, deploying cloud infrastructure with Terraform and CI/CD.

Tools Covered:

  • Ansible, Terraform, Jenkins/GitLab CI (for IaC pipelines), Cloud providers (AWS/Azure/GCP).

Expected Outcomes:

  • Automate infrastructure provisioning and configuration.
  • Implement CI/CD for infrastructure changes.
  • Reduce toil through automation of operational tasks.

Module 3.2: Reliability Engineering & Resiliency

  • High Availability (HA) and Disaster Recovery (DR) Strategies.
  • Fault Tolerance and Resiliency Patterns: Circuit breakers, retries, fallbacks.
  • Load Balancing and Service Discovery.
  • Database Reliability Engineering (DBRE) best practices.
  • Chaos Engineering: Principles, tools, and safely introducing failures (Chaos Mesh/Gremlin).
  • Capacity Planning and Scaling Strategies.
  • Lab: Designing HA architectures, simulating failures with Chaos Mesh, implementing a retry mechanism.

Concepts & Tools:

  • HA/DR, Circuit Breakers, Load Balancers (HAProxy/Nginx), Chaos Engineering (Chaos Mesh), DBRE.

Expected Outcomes:

  • Design and implement highly available and resilient systems.
  • Apply fault tolerance patterns to increase system robustness.
  • Conduct basic chaos engineering experiments to find weaknesses.

Module 4.1: Incident Management & Postmortems

  • Incident Management Lifecycle: Detection, Response, Resolution, Post-mortem.
  • On-Call Management and Best Practices.
  • Communication during Incidents: Internal and external.
  • Conducting Effective Blameless Postmortems: Analysis, action items, learning.
  • Error Budgets in Practice: How to use them for decision-making.
  • Root Cause Analysis (RCA) techniques.
  • Lab: Participating in simulated incident response, writing a blameless postmortem report.

Concepts & Tools:

  • Incident Management, On-Call, Blameless Postmortems, RCA, Communication tools (Slack/Teams).

Expected Outcomes:

  • Manage and respond to production incidents effectively.
  • Lead and document blameless postmortems for continuous learning.
  • Improve system stability through proactive incident analysis.

Module 4.2: Advanced SRE Topics & Future Trends

  • Performance Engineering and Optimization: Profiling, caching, database tuning.
  • Cost Optimization in Cloud SRE: Rightsizing, reserved instances, Spot Instances.
  • Security for SRE: Securing infrastructure, supply chain security, compliance.
  • AIOps: Leveraging AI/ML for monitoring, anomaly detection, predictive analytics.
  • Career Path in SRE: Roles, responsibilities, continuous learning.
  • Capstone Project: Apply SRE principles to an existing system: identify toil, implement observability, design for reliability, propose an incident response plan.

Concepts & Tools:

  • Performance Tuning, Cloud Cost Optimization, AIOps (conceptual), Security for SRE.

Expected Outcomes:

  • Optimize system performance and cloud costs.
  • Understand the role of security in SRE.
  • Explore advanced topics like AIOps and the future of SRE.
  • Apply all SRE principles to a comprehensive project.

Our comprehensive modules ensure you gain the deep understanding and practical skills required to excel as a Site Reliability Engineer.

Student Testimonials

"This SRE Master's program is phenomenal! I now have a solid grasp on SLIs, SLOs, and how to truly measure and improve system reliability."

- Rahul Sharma

"The deep dive into Prometheus and Grafana was incredibly practical. I can now build robust monitoring dashboards and effective alerts."

- Emily Chen

"Vishwa Sir's insights into blameless postmortems and incident management transformed how my team approaches outages. It's a game-changer for operations."

- David Rodriguez

"Learning about Chaos Engineering with practical labs was a huge eye-opener. This course truly prepares you for real-world production challenges."

- Sarah Khan

"As a developer, I never fully understood the 'Ops' side. This SRE course filled all the gaps, teaching me how to build more reliable and resilient software."

- Alex Tan

"Thanks to Vishwa Sir's exceptional mentorship and the comprehensive curriculum, I'm now a confident SRE. The capstone project was the perfect culmination!"

- Olivia Martinez

Have Questions? Reach Out!