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SRE

1. What is Site Reliability Engineering (SRE)

✅ Definition

SRE is a discipline that applies software engineering practices to IT operations to create scalable and reliable systems.

👉 In simple terms:

✅ Key Idea

“Treat operations as a software problem.”

2. History of SRE (Google Model)

📍 Origin

✅ Why Google created SRE

✅ Google’s Approach

📌 Key Principle:

“Eliminate toil (manual repetitive work)”

3. Role Comparison

🔹 SRE vs DevOps vs System Administrator

AspectSREDevOpsSystem Admin
FocusReliability + automationCollaborationSystem maintenance
ApproachEngineering-drivenCulture/processManual operations
ToolsCode, automation, monitoringCI/CD toolsOS tools
Work StyleProactiveCollaborativeReactive
GoalMaintain uptime with codeFaster deliveryKeep systems running

🔍 Simple Analogy

4. Reliability in Software Systems

✅ What is Reliability?

Ability of a system to perform correctly over time without failure.

📌 Measured by:

🔧 Example (CSE Level)

Imagine:

❌ Without SRE:

✅ With SRE:

🔑 Key Reliability Concepts

5. SLI, SLO, SLA (Core Concepts)

🔹 5.1 Service Level Indicator (SLI)

✅ What

A metric that measures system performance.

📌 Examples:

🔧 Example:

🔹 5.2 Service Level Objective (SLO)

✅ What

A target value for an SLI.

📌 Example:

🔧 Meaning:

🔹 5.3 Service Level Agreement (SLA)

✅ What

A legal/business contract based on SLO.

📌 Example:

🔁 Relationship

 
SLI → Measurement
SLO → Target
SLA → Promise (with penalty)
 

6. When to Use SRE

✅ Use SRE when:

❌ Not needed when:

7. How SRE Works (Step-by-Step)

🔹 Step 1: Define Reliability

🔹 Step 2: Set Targets

🔹 Step 3: Monitor System

🔹 Step 4: Automate

🔹 Step 5: Incident Management

🔹 Step 6: Reduce Toil

8. Real-World Example (CSE / Setup Level)

🎯 Example: E-commerce Website

🧩 Setup:

❌ Without SRE:

✅ With SRE:

🔹 Reliability Setup:

🔹 Monitoring:

🔹 SLI:

🔹 SLO:

🔹 SLA:

9. Key SRE Principles (Advanced Level)

🔹 Error Budget

Example:

👉 If exceeded:

🔹 Toil Reduction

Examples:

🔹 Automation First

10. Summary (Quick Revision)

 

 

1. Monitoring vs Observability (Core Foundation)

🔹 Monitoring (WHAT is happening)

👉 Reactive approach

🔹 Observability (WHY it is happening)

👉 Proactive + investigative

🔑 Key Difference

MonitoringObservability
Known issuesUnknown issues
DashboardsRoot cause analysis
AlertsDeep debugging

2. Monitoring Fundamentals

✅ Goals

✅ Golden Signals (Google SRE)

  1. Latency – response time
  2. Traffic – number of requests
  3. Errors – failure rate
  4. Saturation – system load

3. Metrics, Logs, and Traces (3 Pillars)

🔹 3.1 Metrics

✅ What

Numerical data over time

📌 Examples:

🔧 Use case:

🔹 3.2 Logs

✅ What

Detailed event records

📌 Example:

 
ERROR: Payment failed at 10:32PM
 

🔧 Use case:

🔹 3.3 Traces

✅ What

Track request journey across services

📌 Example:

User → API → DB → Payment → Response

🔧 Use case:

🔁 Combined View

TypeUse
MetricsDetect issue
LogsInvestigate issue
TracesUnderstand flow

4. Alerting Strategies

🔹 Types of Alerts

1. Threshold-based

2. Rate-based

3. Anomaly-based

🔹 Good Alert Characteristics

❌ Bad Alerts

5. Observability Tools Overview

🔹 Prometheus

✅ What

🔧 Features

🔹 Grafana

✅ What

🔧 Features

🔹 ELK Stack

🔹 Elasticsearch

🔹 Logstash

🔹 Kibana

6. Project-Based Learning (Real Setup)

Now let’s go advanced level with real industry tools

🔥 PHASE 1: Project using Splunk

✅ What Splunk Does

🧩 Project Example: E-commerce App

🔧 Setup:

📊 What You Monitor:

🔍 Example Query:

 
error OR failed | stats count by service
 

🎯 Outcome:

🧠 When to Use Splunk

🚀 PHASE 2: Project using New Relic

✅ What New Relic Does

🧩 Setup

📊 Monitor:

🔍 Example Insight:

🎯 Features

🧠 When to Use

⚡ PHASE 3: Project using ELK Stack (Elasticsearch-based)

🧩 Setup Architecture

 
App → Logstash → Elasticsearch → Kibana
 

🔧 Step-by-Step

1. Install Elasticsearch

2. Install Logstash

3. Install Kibana

📊 What You Monitor

🔍 Example Use Case

Scenario:

Users report slow checkout

Investigation:

🎯 Outcome

7. Comparing All Tools (Advanced Insight)

ToolTypeBest For
SplunkPaidEnterprise log analysis
New RelicPaidFull observability (APM)
ELK StackOpen-sourceLog monitoring
PrometheusOpen-sourceMetrics monitoring
GrafanaOpen-sourceVisualization

8. End-to-End Architecture (Industry Level)

 
Users
  ↓
Application
  ↓
Metrics → Prometheus → Grafana
Logs → ELK / Splunk
Traces → New Relic
 

9. Real Interview-Level Insight

🔥 Key Concept:

“Monitoring tells you something is wrong, observability tells you why.”

🔥 Advanced Tip:

👉 Together = Complete observability stack

10. Summary

 

 

1. What is Incident Management

✅ Definition

Incident Management is the process of detecting, responding to, resolving, and learning from system failures.

👉 An incident = any event that disrupts normal service.

🔧 Example (CSE → Real World)

👉 All are incidents

2. Incident Response Process (Step-by-Step)

🔥 6-Stage Lifecycle

🔹 1. Detection

✅ What

Identify that something is wrong

🔧 How

📌 Example:

🔹 2. Triage

✅ What

Assess severity and impact

📊 Severity Levels:

LevelMeaning
SEV-1Full outage
SEV-2Major feature broken
SEV-3Minor issue

🔹 3. Response

✅ What

Take immediate action

🔧 Actions:

🔹 4. Mitigation

✅ What

Reduce impact (temporary fix)

📌 Example:

🔹 5. Resolution

✅ What

Fix root problem permanently

🔹 6. Recovery

✅ What

Bring system back to normal

🔁 Flow Summary

 
Detect → Triage → Respond → Mitigate → Resolve → Recover
 

3. Root Cause Analysis (RCA)

🔹 What is RCA

Process of identifying the real reason behind an incident

❗ Important

🔧 Example

❌ Symptom:

🔍 Investigation:

🎯 Root Cause:

🔹 RCA Techniques

1. 5 Whys Method

Example:

  1. Why slow? → DB slow
  2. Why DB slow? → Query heavy
  3. Why heavy? → No index
  4. Why no index? → Not added
  5. Why? → Missed in design

👉 Root cause found

2. Fishbone Diagram

🔹 RCA Output

4. Postmortem Culture

🔹 What is Postmortem

A document/report created after incident resolution

🔥 Key Principle:

“Blameless culture”

👉 Focus on system failure, not people

❌ Wrong Approach:

✅ Correct Approach:

🔧 Postmortem Structure

📄 1. Summary

⏱ 2. Timeline

🎯 3. Impact

🔍 4. Root Cause

🛠 5. Action Items

🔧 Example (Mini)

5. Incident Command System (ICS)

🔹 What is ICS

A structured way to manage incidents using roles

🔥 Key Idea:

Clear roles = faster resolution

🔧 Roles in Incident

👨‍✈️ Incident Commander (IC)

🧑‍💻 Operations Lead

📢 Communication Lead

📋 Scribe

🔧 Example Flow

6. Escalation Policies

🔹 What is Escalation

Passing incident to higher-level experts when needed

🔧 When to Escalate

🔹 Types of Escalation

1. Time-based

2. Hierarchical

3. Functional

🔧 Example

7. Real Project Flow (Advanced)

🎯 Scenario: Production API Failure

🚨 Step 1: Detection

📊 Step 2: Triage

👨‍✈️ Step 3: Incident Command

🛠 Step 4: Response

🔧 Step 5: Mitigation

🔍 Step 6: RCA

📄 Step 7: Postmortem

🔁 Step 8: Prevention

8. Advanced SRE Concepts

🔹 Error Budget (Connection)

🔹 Runbooks

Example:

 
1. Check logs  
2. Restart service  
3. Verify health  
 

🔹 Automation in Incident Management

 

9. Interview-Level Insights

 

🔥 Key Statements

 

10. Quick Summary

 

1. What is Automation & Infrastructure

✅ Definition

Automation in infrastructure means using code and tools to create, manage, and scale systems instead of doing it manually.

🔥 Core Idea

“If you do it more than once → automate it.”

🔧 Example (Basic)

❌ Manual:

✅ Automated:

2. Infrastructure as Code (IaC)

🔹 What is IaC

Managing infrastructure using code instead of manual processes

🔧 Example

Instead of:

You write:

 
resource "aws_instance" "web" {
  ami           = "ami-123456"
  instance_type = "t2.micro"
}
 

✅ Benefits

🧠 When to Use

3. Configuration Management

🔹 What is it

Ensuring systems are in the desired state automatically

🔧 Example

✅ Tasks

🔁 Difference from IaC

IaCConfiguration Management
Creates infrastructureConfigures it
Example: Create VMInstall software

4. Automation Tools Overview

Tool TypeExamples
IaCTerraform
Config MgmtAnsible, Puppet, Chef

5. Terraform

🔹 What it does

🔧 How it works

  1. Write .tf file
  2. Run:
 
terraform init
terraform apply
 

🔥 Key Features

🧩 Example Use Case

👉 All in one file

🧠 When to Use

6. Ansible

🔹 What it does

🔧 How it works

 
- hosts: web
  tasks:
    - name: install nginx
      apt:
        name: nginx
        state: present
 

🔥 Features

🧩 Use Case

🧠 When to Use

7. Puppet

🔹 What it does

🔧 How it works

🔥 Features

🧩 Example

🧠 When to Use

8. Chef

🔹 What it does

🔧 How it works

🔥 Features

🧩 Example

🧠 When to Use

9. Real Project Flow (End-to-End)

🎯 Scenario: Deploy Scalable Web App

🔹 Step 1: Infrastructure (Terraform)

🔹 Step 2: Configuration (Ansible)

🔹 Step 3: Application Deployment

🔹 Step 4: Scaling

🔹 Step 5: Maintenance

🔁 Flow

 
Terraform → Infrastructure
Ansible → Configuration
App → Deployment
 

10. Tool Comparison (Advanced Insight)

ToolTypeAgentLanguage
TerraformIaCNoHCL
AnsibleConfigNoYAML
PuppetConfigYesDSL
ChefConfigYesRuby

11. Advanced Concepts

🔹 Idempotency

🔹 Immutable Infrastructure

🔹 Drift Detection

12. Real Industry Architecture

 
Developer → Git → CI/CD
             ↓
       Terraform → Infra
             ↓
       Ansible → Setup
             ↓
         Application
 

13. Interview-Level Insights

 

🔥 Key Points

14. Quick Summary

 

 

1. What is Scalability & Performance

🔹 Scalability

Ability of a system to handle increasing load by adding resources

🔹 Performance

How fast and efficiently a system responds

🔁 Difference

ScalabilityPerformance
Handles more usersHandles requests faster
Adds resourcesOptimizes speed

🔧 Example

2. Load Balancing

🔹 What is Load Balancing

Distributing traffic across multiple servers

🔧 Why Needed

🔹 Types of Load Balancing

1. Round Robin

2. Least Connections

3. IP Hash

🔧 Example Setup

 
User → Load Balancer → Server1
                      Server2
                      Server3
 

🧠 When to Use

🔥 Real Tools

3. Capacity Planning

🔹 What is Capacity Planning

Predicting and preparing resources for future load

🔧 Key Factors

🔹 Types

1. Reactive

2. Proactive

🔧 Example

🧠 Formula Idea

Capacity ≈
Requests/sec × Response time × Safety factor

🔥 Goal

4. Performance Testing

🔹 What is Performance Testing

Testing system behavior under load

🔹 Types

1. Load Testing

2. Stress Testing

3. Spike Testing

4. Endurance Testing

🔧 Tools

🔧 Example

🧠 When to Use

5. Caching Strategies

🔹 What is Caching

Storing frequently used data for faster access

🔧 Why Important

🔹 Types of Caching

1. Client-side Cache

2. Server-side Cache

3. Database Cache

4. CDN Cache

🔧 Example

Without cache:

With cache:

🔥 Tools

🧠 Cache Strategies

Cache Aside

Write Through

Write Back

6. Distributed Systems Basics

🔹 What is Distributed System

Multiple machines working together as one system

🔧 Example

🔹 Key Concepts

1. Consistency

2. Availability

3. Partition Tolerance

🔥 CAP Theorem

You can only guarantee 2 out of 3

🔧 Example

7. Real Project (End-to-End Architecture)

🎯 Scenario: Scalable Web Application

🔹 Step 1: Load Balancer

🔹 Step 2: Multiple Servers

🔹 Step 3: Caching Layer

🔹 Step 4: Database Optimization

🔹 Step 5: Monitoring

🔁 Architecture

 
Users
  ↓
Load Balancer
  ↓
App Servers (Multiple)
  ↓
Cache (Redis)
  ↓
Database
 

8. Advanced Concepts (Interview Level)

🔹 Horizontal vs Vertical Scaling

TypeMeaning
VerticalAdd more power (CPU, RAM)
HorizontalAdd more servers

🔹 Auto Scaling

🔹 Latency Optimization

🔹 Bottleneck Identification

 

9. Real Industry Insight

 

🔥 Key Strategy

 

🔥 Common Mistakes

 

10. Quick Summary

 

 

1. What is Reliability Engineering

✅ Definition

Reliability Engineering ensures systems continue to function correctly even under failures.

🔥 Core Idea

“Failures will happen — design systems to survive them.”

🔧 Example

2. Error Budgets

🔹 What is Error Budget

The allowed amount of failure based on SLO

🔧 Example

👉 Allowed downtime:

🔹 Why Important

🔥 Rule

🧠 When to Use

3. Fault Tolerance

🔹 What is Fault Tolerance

System continues working even when parts fail

🔧 Example

🔹 Techniques

🔧 Real Case

🧠 When to Use

4. Redundancy and Failover

🔹 Redundancy

Having extra components as backup

🔧 Types

1. Active-Active

2. Active-Passive

🔹 Failover

Automatically switching to backup when failure occurs

🔧 Example

 
Primary Server → Fails
       ↓
Backup Server → Takes over
 

🧠 When to Use

🔥 Real Example

5. Chaos Engineering

🔹 What is Chaos Engineering

Intentionally breaking systems to test reliability

🔥 Core Idea

“Test failures before they happen in real life”

🔧 Example

🔹 Popular Tool

🔧 Example Experiment

🧠 When to Use

⚠️ Important

6. Disaster Recovery (DR) Planning

🔹 What is Disaster Recovery

Plan to restore systems after major failure

🔥 Examples of Disasters

🔹 Key Metrics

1. RTO (Recovery Time Objective)

How fast system should recover

2. RPO (Recovery Point Objective)

How much data loss is acceptable

🔧 Example

🔹 DR Strategies

1. Backup & Restore

2. Pilot Light

3. Warm Standby

4. Multi-site Active

🔧 Architecture Example

 
Region A → Primary
Region B → Backup
 

🧠 When to Use

7. Real Project (End-to-End Reliability Setup)

🎯 Scenario: Scalable Banking App

🔹 Step 1: Define SLO

🔹 Step 2: Error Budget

🔹 Step 3: Fault Tolerance

🔹 Step 4: Redundancy

🔹 Step 5: Failover

🔹 Step 6: Chaos Testing

🔹 Step 7: Disaster Recovery

🔁 Architecture

 
Users
  ↓
Load Balancer
  ↓
Multiple App Servers
  ↓
Primary DB ↔ Replica DB
  ↓
Backup Region
 

8. Advanced Concepts (Interview Level)

🔹 Graceful Degradation

Example:

🔹 Circuit Breaker Pattern

🔹 Retry with Backoff

🔹 Bulkhead Isolation

9. Real Industry Insights

🔥 Key Principles

 

🔥 Common Mistakes

 

10. Quick Summary

 

1. What is CI/CD

🔹 CI (Continuous Integration)

Developers frequently merge code into a shared repository, and it is automatically tested.

🔹 CD (Continuous Deployment/Delivery)

Code is automatically built, tested, and deployed to production or staging.

🔥 Core Idea

“Automate the path from code → production”

🔧 Example

Without CI/CD:

With CI/CD:

2. CI/CD Pipeline

🔹 What is a Pipeline

A sequence of automated steps that code goes through

🔧 Typical Pipeline Stages

1. Code Commit

2. Build

3. Test

4. Deploy

5. Monitor

🔁 Pipeline Flow

 
Code → Build → Test → Deploy → Monitor
 

🔧 Tools

3. Deployment Strategies

🔹 What

Methods used to release new versions safely

🔧 Why

4. Blue-Green Deployment

🔹 What

Maintain two environments:

🔧 How it works

 
Users → Blue (v1)
       ↓ switch
Users → Green (v2)
 

✅ Advantages

❌ Disadvantages

🧠 When to Use

5. Canary Deployment

🔹 What

Release new version to small percentage of users

🔧 Example

🔁 Flow

 
Users → 5% (new)
        95% (old)
 

✅ Advantages

❌ Disadvantages

🧠 When to Use

6. Rolling Updates

🔹 What

Gradually update servers one by one

🔧 Example

 
Server1 → update
Server2 → update
Server3 → update
 

✅ Advantages

❌ Disadvantages

🧠 When to Use

7. Version Control (Git)

🔹 What is Git

Tracks code changes and enables collaboration

🔧 Key Concepts

🔹 Repository

🔹 Commit

🔹 Branch

🔹 Merge

🔧 Example Flow

 
git add .
git commit -m "feature added"
git push origin main
 

🔥 Branching Strategy

Git Flow

🧠 Why Git is Important

8. Real Project (End-to-End CI/CD Setup)

🎯 Scenario: Deploy Node.js App

🔹 Step 1: Code (Git)

🔹 Step 2: CI Pipeline

🔹 Step 3: CD Pipeline

🔹 Step 4: Deployment Strategy

🔹 Step 5: Monitoring

🔁 Architecture

 
Developer → Git → CI/CD Tool
                    ↓
              Build & Test
                    ↓
              Deploy Strategy
                    ↓
                Production
 

9. Advanced Concepts

🔹 Continuous Delivery vs Deployment

DeliveryDeployment
Manual approvalFully automatic

🔹 Pipeline as Code

🔹 Rollback Strategy

🔹 Feature Flags

10. Real Industry Insights

🔥 Best Practices

🔥 Common Mistakes

 

11. Quick Summary

 

 

1. What is Security & Compliance

🔹 Security

Protect systems, data, and users from unauthorized access and attacks

🔹 Compliance

Following rules, standards, and regulations (legal/business requirements)

🔥 Core Idea

“Build systems that are secure by design and provably compliant.”

🔧 Example

2. Security Best Practices

🔹 1. Principle of Least Privilege (PoLP)

Give only required permissions

🔧 Example

🔹 2. Defense in Depth

Multiple layers of security

🔧 Layers:

🔹 3. Regular Updates & Patching

🔹 4. Encryption

Types:

🔹 5. Secrets Management

🔹 6. Monitoring & Logging

🔹 7. Backup & Recovery

3. Access Control & Authentication

🔹 Authentication (AuthN)

Verifying identity

🔧 Methods:

🔹 Authorization (AuthZ)

What user is allowed to do

🔹 Access Control Models

1. RBAC (Role-Based Access Control)

2. ABAC (Attribute-Based Access Control)

🔹 Multi-Factor Authentication (MFA)

Use multiple verification methods

🔧 Example

🔥 Real Tools

4. Secure Deployment Practices

🔹 What

Ensuring deployments do not introduce vulnerabilities

🔧 Key Practices

🔹 1. CI/CD Security (DevSecOps)

🔹 2. Image Scanning

🔹 3. Infrastructure Security

🔹 4. Secrets in CI/CD

🔹 5. HTTPS Everywhere

🔹 6. Zero Trust Model

Never trust, always verify

🔧 Example

CI/CD pipeline:

 
Code → Scan → Build → Scan → Deploy
 

5. Compliance & Auditing

🔹 What is Compliance

Following standards/regulations

🔹 Common Standards

🔹 Auditing

Tracking and verifying actions in the system

🔧 Example Logs

 
User A logged in at 10:30  
Admin deleted record at 11:00  
 

🔹 Why Auditing is Important

🔹 Audit Types

1. Internal Audit

2. External Audit

6. Real Project (End-to-End Security Setup)

🎯 Scenario: Secure Web Application

🔹 Step 1: Authentication

🔹 Step 2: Access Control

🔹 Step 3: Secure Deployment

🔹 Step 4: Encryption

🔹 Step 5: Monitoring

🔹 Step 6: Compliance

🔹 Step 7: Auditing

 

🔁 Architecture

 
User → Auth → App → DB
         ↓
       Logs → Audit System
 

 

7. Advanced Concepts (Interview Level)

 

🔹 Zero Trust Security

 

🔹 Identity & Access Management (IAM)

 

🔹 Security Automation

 

🔹 Threat Modeling

 

🔹 Vulnerability Management

 

8. Real Industry Insights

 

🔥 Best Practices

 

🔥 Common Mistakes

 

9. Quick Summary

 

 

1. What is Cloud Computing

🔹 Definition

Delivering computing resources (servers, storage, networking) over the internet

🔥 Core Idea

“Don’t buy servers — rent them on demand”

🔧 Example

2. Cloud Platforms

🔹 Amazon Web Services (AWS)

✅ Features

🔹 Microsoft Azure

✅ Features

🔹 Google Cloud Platform (GCP)

✅ Features

🔁 Comparison

PlatformStrength
AWSWide services
AzureEnterprise
GCPData & AI

🧠 When to Use

3. Containers and Docker

🔹 What are Containers

Lightweight environments that package app + dependencies

🔥 Problem Solved

“Works on my machine” ❌
“Works everywhere” ✅

🔹 Docker

🔧 What Docker Does

🔧 Example Dockerfile

 
FROM node:18
WORKDIR /app
COPY . .
RUN npm install
CMD ["node", "app.js"]
 

🔧 Commands

 
docker build -t app .
docker run -p 3000:3000 app
 

🔥 Benefits

🧠 When to Use

4. Kubernetes Basics

🔹 What is Kubernetes

Tool to manage containers at scale

🔥 Core Idea

“Automate container deployment, scaling, and management”

🔧 Key Components

🔹 Pod

🔹 Node

🔹 Cluster

🔹 Deployment

🔹 Service

🔧 Example Flow

 
User → Service → Pod → Container
 

🔥 Features

🧠 When to Use

5. Microservices Architecture

🔹 What is Microservices

Breaking application into small independent services

🔧 Example

Instead of:

Use:

🔁 Architecture

 
User → API Gateway
        ↓
  User Service
  Payment Service
  Order Service
 

🔥 Benefits

❌ Challenges

🧠 When to Use

6. Real Project (End-to-End Setup)

🎯 Scenario: Scalable Web Application

🔹 Step 1: Cloud Platform

🔹 Step 2: Containerization

🔹 Step 3: Orchestration

🔹 Step 4: Microservices

🔹 Step 5: Scaling

🔁 Architecture

 
Users
  ↓
Cloud (AWS/Azure/GCP)
  ↓
Kubernetes Cluster
  ↓
Containers (Docker)
  ↓
Microservices
 

7. Advanced Concepts (Interview Level)

🔹 Container vs VM

ContainerVM
LightweightHeavy
Fast startupSlow
Shared OSFull OS

🔹 Service Mesh

🔹 Auto Scaling

🔹 CI/CD Integration

8. Real Industry Insights

🔥 Best Practices

🔥 Common Mistakes

 

9. Quick Summary

 

🔧 10. Practical / Lab Work (End-to-End Project)

🎯 Project Goal

Build a mini production-ready system with:

🧩 Project Architecture

 
User
 ↓
App (Docker)
 ↓
Kubernetes
 ↓
Monitoring (Prometheus + Grafana)
 ↓
CI/CD Pipeline
 

1. Setting Up Monitoring Dashboards

🔹 Tools Used

🔧 Step-by-Step

1. Install Prometheus

 
docker run -d -p 9090:9090 prom/prometheus
 

2. Install Grafana

 
docker run -d -p 3000:3000 grafana/grafana
 

3. Connect Grafana → Prometheus

4. Create Dashboard

Track:

🎯 Outcome

2. Automating Infrastructure Deployment

🔹 Tool Used

🔧 Step-by-Step

1. Install Terraform

2. Create main.tf

 
provider "aws" {
  region = "ap-south-1"
}

resource "aws_instance" "web" {
  ami           = "ami-12345"
  instance_type = "t2.micro"
}
 

3. Run Commands

 
terraform init
terraform apply
 

🎯 Outcome

3. Creating CI/CD Pipelines

🔹 Tool Used

🔧 Step-by-Step

1. Create Workflow File

.github/workflows/deploy.yml

 
name: CI/CD Pipeline

on:
  push:
    branches: [ "main" ]

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v2

      - name: Build Docker Image
        run: docker build -t app .

      - name: Run Tests
        run: echo "Tests passed"

      - name: Deploy
        run: echo "Deploying app"
 

🎯 Outcome

4. Simulating System Failures

🔹 Goal

Test system reliability

🔧 Methods

🔹 1. Kill Container

 
docker kill <container_id>
 

👉 Check:

🔹 2. CPU Stress Test

 
stress --cpu 4 --timeout 60
 

👉 Check:

🔹 3. Network Failure

🔹 Tool Example

🎯 Outcome

5. Full End-to-End Flow

🔁 Practical Workflow

 
Code → GitHub
     ↓
CI/CD Pipeline
     ↓
Docker Container
     ↓
Kubernetes Deployment
     ↓
Monitoring (Prometheus + Grafana)
     ↓
Failure Testing
 

6. Mini Project (Resume Ready)

🎯 Project Title

“Scalable & Reliable Web App with Monitoring and CI/CD”

 

🔧 Features

 

 

 

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