Multi-Cloud Migration Platform

Production-Style AWS + Azure Infrastructure Modernization with Terraform, CI/CD, Monitoring, and Cost Optimization

Enterprise-grade multi-cloud migration project demonstrating how to design, provision, secure, automate, validate, and optimize workloads across AWS and Microsoft Azure using Infrastructure as Code, CI/CD pipelines, Python automation, and cross-cloud networking.

📌 Executive Summary

This project simulates a real-world enterprise migration from a legacy on-premises environment to a multi-cloud architecture spanning AWS and Azure.

It is designed to reflect how modern platform and cloud engineering teams build production-ready infrastructure that is:

Scalable
Highly available
Secure by design
Automated end-to-end
Cost-aware
Operationally observable

The implementation includes:

Terraform-based infrastructure provisioning
AWS + Azure workload deployment
Site-to-Site VPN connectivity
CI/CD automation with Jenkins and GitHub Actions
Migration orchestration scripts
Smoke testing and validation
Cloud cost visibility and reporting
Monitoring and operational dashboards

🚀 Business Outcomes

This implementation is designed to emulate measurable engineering and business impact:

~30% infrastructure cost reduction
~96% faster deployment cycles
~99% faster environment provisioning
Improved availability and resiliency
Reduced manual operational overhead
Faster recovery and rollout consistency

🧱 Technology Stack

Cloud Platforms

Amazon Web Services (AWS)
Microsoft Azure

Infrastructure as Code

Terraform

CI/CD & Automation

Jenkins
GitHub Actions
Python

Core Services

AWS

VPC
Application Load Balancer (ALB)
EC2 Auto Scaling Group
RDS PostgreSQL
S3
IAM
CloudWatch
VPN Gateway
Secrets Manager

Azure

VNet
Load Balancer
Virtual Machine Scale Set (VMSS)
PostgreSQL Flexible Server
Blob Storage
Network Security Groups
Key Vault
Azure VPN Gateway
Monitor / Diagnostics

🏗️ Solution Architecture

This project models a dual-cloud landing zone where workloads are deployed across AWS and Azure with private connectivity between both environments.

High-Level Architecture

                     ┌──────────────────────────────┐
                     │  On-Premises Infrastructure  │
                     │  (Legacy / Source Workloads) │
                     └──────────────┬───────────────┘
                                    │
                              Migration / Cutover
                                    │
              ┌─────────────────────┼─────────────────────┐
              │                                           │
              ▼                                           ▼
  ┌─────────────────────────────┐             ┌─────────────────────────────┐
  │             AWS             │             │            Azure            │
  │         Region: ap-south-1  │             │      Region: centralindia   │
  │                             │             │                             │
  │  VPC 10.10.0.0/16           │             │  VNet 10.20.0.0/16         │
  │  ├─ Public / Private Subnets│             │  ├─ Public / Private Subnets│
  │  ├─ ALB                     │             │  ├─ Azure Load Balancer     │
  │  ├─ EC2 Auto Scaling Group  │             │  ├─ VM Scale Set            │
  │  ├─ RDS PostgreSQL          │             │  ├─ PostgreSQL Flexible Sv. │
  │  ├─ S3 Buckets              │             │  ├─ Blob Storage            │
  │  ├─ CloudWatch Monitoring   │             │  ├─ Azure Monitoring        │
  │  └─ VPN Gateway             │             │  └─ VPN Gateway             │
  └─────────────────────────────┘             └─────────────────────────────┘
              │                                           │
              └───────────────────────────────────────────┘
                               IPSec Site-to-Site VPN

                           CI/CD + Validation + Smoke Tests

🎯 Project Objectives

This project was built to demonstrate practical implementation of:

Multi-cloud landing zone design
Infrastructure modularization using Terraform
Production-style networking and segmentation
Compute scaling and high availability
Database deployment and secure configuration
Storage lifecycle and cloud-native services
Deployment automation via CI/CD
Migration validation and smoke testing
Operational visibility and cost reporting

📁 Repository Structure

multi-cloud-migration/
├── terraform/
│   ├── aws/
│   │   ├── main.tf
│   │   ├── variables.tf
│   │   ├── outputs.tf
│   │   └── modules/
│   │       ├── vpc/
│   │       ├── compute/
│   │       ├── database/
│   │       └── storage/
│   │
│   └── azure/
│       ├── main.tf
│       ├── variables.tf
│       ├── outputs.tf
│       └── modules/
│           ├── vnet/
│           ├── compute/
│           ├── database/
│           └── storage/
│
├── pipelines/
│   ├── jenkins/
│   │   └── Jenkinsfile
│   └── github-actions/
│       └── deploy.yml
│
├── scripts/
│   ├── migrate.py
│   ├── smoke_test.py
│   └── cost_report.py
│
├── monitoring/
│   └── cloudwatch_dashboard.tf
│
├── .gitignore
└── README.md

⚙️ Key Capabilities

1) Infrastructure Provisioning

Provision modular cloud infrastructure across AWS and Azure using reusable Terraform modules.

2) Multi-Cloud Networking

Establish private communication between both cloud environments using Site-to-Site VPN.

3) Compute and Scaling

Deploy autoscaling application workloads using:

AWS EC2 Auto Scaling Group
Azure Virtual Machine Scale Set

4) Database Layer

Provision managed PostgreSQL services on both cloud platforms with secure configuration and private access.

5) Storage Layer

Implement cloud-native object storage using:

Amazon S3
Azure Blob Storage

6) CI/CD Automation

Support deployment workflows using both:

Jenkins
GitHub Actions

7) Validation & Smoke Testing

Validate infrastructure and endpoints after deployment using automation scripts.

8) Cost Optimization

Generate cloud cost visibility and operational savings insights using reporting automation.

🔁 CI/CD Workflow

This repository includes two deployment approaches:

Jenkins Pipeline

A production-style Jenkins pipeline capable of:

Running Terraform plan
Executing controlled Terraform apply
Supporting destroy workflows
Deploying AWS only, Azure only, or both
Running parallelized cloud deployments

GitHub Actions Workflow

A lightweight GitHub-native CI/CD workflow for automated deployment and validation.

Deployment Flow

Validate → Plan → Approval Gate → Apply → Smoke Test → Report

🛡️ Security Design

Security is treated as a foundational design principle throughout the project.

Security Controls Included

Secrets externalized (not hardcoded in code)
Private database placement
Least privilege design
Network segmentation
Security Groups / NSGs
Encryption at rest
Managed secret stores
Metadata service hardening
Environment separation

Security Services Used

Security Area	AWS	Azure
Secrets Management	Secrets Manager	Key Vault
Network Isolation	Security Groups	NSGs
Encryption	KMS-backed services	Azure SSE
Database Protection	Private subnet	Delegated subnet
Access Control	IAM	RBAC

📊 Monitoring & Observability

This project includes baseline operational visibility to support reliability and troubleshooting.

Monitoring Coverage

Infrastructure health
Resource-level visibility
Basic alarms / dashboarding
Post-deployment validation
Endpoint smoke checks

Included Components

CloudWatch Dashboard
Terraform-managed monitoring resources
Python-based smoke test validation

🌐 Cross-Cloud Connectivity

The AWS and Azure environments are interconnected using IPSec Site-to-Site VPN, enabling private communication between workloads deployed in each cloud.

Connectivity Model

AWS VPC (10.10.0.0/16)  ←── IPSec VPN ──→  Azure VNet (10.20.0.0/16)

This is particularly useful for scenarios such as:

Hybrid application migration
Service-to-service communication
Database replication patterns
Gradual cutover strategies
Disaster recovery / failover planning

💰 Cost Optimization Outcomes

This project demonstrates cloud cost optimization principles such as:

Managed service selection
Autoscaling infrastructure
Object storage lifecycle controls
Operational automation
Reduced provisioning overhead

Example Impact Metrics

Metric	Legacy / Manual	Automated Cloud Model	Improvement
Monthly Infrastructure Cost	~$5,000	~$3,500	~30% lower
Deployment Time	~4 hours	~8 minutes	~96% faster
Environment Provisioning	~2 weeks	~20 minutes	~99% faster
Availability Target	~99.5%	~99.95%	Improved

🚀 Getting Started

These steps are intended for local testing / demonstration purposes.

Prerequisites

Install the following tools:

Terraform
AWS CLI
Azure CLI
Python 3.x

Configure Cloud Access

aws configure
az login

▶️ Deployment Steps

1. Run migration assessment

python3 scripts/migrate.py --phase assess

2. Deploy AWS infrastructure

cd terraform/aws
terraform init
terraform apply

3. Deploy Azure infrastructure

cd terraform/azure
terraform init
terraform apply

4. Validate endpoints

python3 scripts/smoke_test.py

5. Generate cost visibility report

python3 scripts/cost_report.py

🧪 Validation Strategy

Post-deployment validation is performed through:

Infrastructure provisioning checks
Endpoint reachability validation
Smoke test execution
Output verification
Basic workload readiness checks

🧹 Cleanup

To tear down the infrastructure:

terraform -chdir=terraform/azure destroy
terraform -chdir=terraform/aws destroy

📚 What This Project Demonstrates

This repository demonstrates practical engineering capability in:

Cloud architecture
Platform engineering
Infrastructure as Code
Terraform modularization
CI/CD implementation
Multi-cloud networking
Automation scripting
Monitoring and validation
Cost-aware infrastructure design

👨‍💻 Author

Swanand Awatade
Cloud & DevOps Engineer

Areas of Focus

AWS
Azure
Kubernetes
Terraform
CI/CD
DevSecOps
Monitoring
Platform Engineering

⭐ Why This Project Matters

This is not just a “Terraform demo.”

It is a portfolio-grade cloud engineering project built to reflect how infrastructure teams design and operate production-style multi-cloud environments with automation, security, reliability, and business outcomes in mind.

Name		Name	Last commit message	Last commit date
monitoring		monitoring
pipelines		pipelines
scripts		scripts
terraform		terraform
.gitignore		.gitignore
README.md		README.md

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