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Maureen Chebet
Maureen Chebet

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Migrating a mobile banking app Platform to AWS with Zero Downtime: A Complete Guide

#aws #migration #devops #fintech

The Challenge

Your fintech company needs to migrate from on-premise infrastructure to AWS within 6 months. The requirements are strict:

  • Zero downtime during migration
  • 8TB PostgreSQL database to migrate
  • Legacy Spring Boot monolithic application
  • On-premise Jenkins CI/CD to transform
  • Compliance: PCI-DSS and GDPR requirements
  • Cost reduction: 40% savings target
  • Uptime improvement: From 99.9% to 99.99%

This isn't just a lift-and-shift. It's a transformation that must maintain business continuity while modernizing infrastructure, improving security, and reducing costs.

In this article, I'll walk through a comprehensive AWS migration strategy that ensures zero downtime, maintains compliance, and optimizes costs.

Migration Strategy Overview

Why AWS Database Migration Service (DMS) is Critical

For a zero-downtime migration of an 8TB database, AWS Database Migration Service (DMS) is the cornerstone. It supports continuous replication, allowing you to migrate with minimal downtime.

Migration Approach: Blue-Green Deployment

  1. Blue Environment: Current on-premise infrastructure (production)
  2. Green Environment: New AWS infrastructure (parallel)
  3. Gradual Cutover: Route traffic incrementally from Blue to Green
  4. Rollback Capability: Switch back to Blue if issues arise

High-Level Migration Phases 

Phase 1: Assessment & Planning (Weeks 1-4)
Phase 2: AWS Foundation Setup (Weeks 5-8)
Phase 3: Application Migration (Weeks 9-16)
Phase 4: Database Migration (Weeks 17-22)
Phase 5: CI/CD Migration (Weeks 19-22)
Phase 6: Cutover & Validation (Weeks 23-24)
Phase 7: Optimization & Decommissioning (Weeks 25-26)
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Phase 1: Assessment & Planning

Application Discovery

AWS Application Discovery Service helps inventory your on-premise infrastructure:

# Install discovery agent on on-premise servers
# Automatically discovers:
# - Server configurations
# - Network dependencies
# - Application dependencies
# - Performance metrics
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Key Metrics to Collect:

  • CPU, memory, disk utilization patterns
  • Network traffic patterns
  • Database query patterns
  • Application dependencies
  • Security requirements

Database Assessment

AWS Schema Conversion Tool (SCT) for PostgreSQL analysis:

# Analyze PostgreSQL database
aws dms describe-schemas \
  --endpoint-arn arn:aws:dms:region:account:endpoint:source-endpoint

# Key assessments:
# - Database size: 8TB
# - Table count and sizes
# - Index usage
# - Foreign key relationships
# - Stored procedures and functions
# - Custom data types
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Cost Estimation

AWS Pricing Calculator and AWS Cost Explorer:

# Estimate monthly costs
# Components to estimate:
# - EC2 instances (application servers)
# - RDS PostgreSQL (database)
# - EBS volumes (storage)
# - Data transfer costs
# - Backup storage
# - Monitoring and logging
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Target: 40% cost reduction

  • Reserved Instances: 30-40% savings
  • Right-sizing: 20-30% savings
  • Auto-scaling: 15-25% savings
  • Managed services: Reduced operational overhead

Phase 2: AWS Foundation Setup

Network Architecture

VPC Design for Compliance:

# VPC Structure
VPC: 10.0.0.0/16
  ├── Public Subnets (2 AZs)
  │   ├── NAT Gateways
  │   └── Load Balancers
  ├── Application Subnets (2 AZs)
  │   └── EC2 Instances (Private)
  ├── Database Subnets (2 AZs)
  │   └── RDS Instances (Private)
  └── Management Subnets
      └── Bastion Hosts
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Security Groups Configuration:

# Application Security Group
aws ec2 create-security-group \
  --group-name payment-app-sg \
  --description "Security group for payment application" \
  --vpc-id vpc-12345678

# Allow HTTPS from ALB
aws ec2 authorize-security-group-ingress \
  --group-id sg-12345678 \
  --protocol tcp \
  --port 8080 \
  --source-group sg-alb-12345678

# Database Security Group
aws ec2 create-security-group \
  --group-name payment-db-sg \
  --description "Security group for payment database" \
  --vpc-id vpc-12345678

# Allow PostgreSQL from application only
aws ec2 authorize-security-group-ingress \
  --group-id sg-db-12345678 \
  --protocol tcp \
  --port 5432 \
  --source-group sg-app-12345678
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Identity and Access Management

IAM Roles for Least Privilege:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "rds-db:connect"
      ],
      "Resource": "arn:aws:rds-db:region:account:dbuser:db-instance-id/payment-app-user",
      "Condition": {
        "StringEquals": {
          "rds-db:database-name": "paymentdb"
        }
      }
    }
  ]
}
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AWS Secrets Manager for credential management:

import boto3
import json

secrets_client = boto3.client('secretsmanager')

def get_database_credentials():
    """Retrieve database credentials from Secrets Manager"""
    response = secrets_client.get_secret_value(
        SecretId='payment-db-credentials'
    )
    return json.loads(response['SecretString'])

# Credentials automatically rotated
# No hardcoded secrets in code
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Compliance Setup

AWS Config for compliance monitoring:

# Enable AWS Config
aws configservice put-configuration-recorder \
  --configuration-recorder name=default,roleArn=arn:aws:iam::account:role/config-role

# Configure rules for PCI-DSS compliance
aws configservice put-config-rule \
  --config-rule file://pci-dss-config-rule.json
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CloudTrail for audit logging:

# Enable CloudTrail
aws cloudtrail create-trail \
  --name payment-audit-trail \
  --s3-bucket-name payment-audit-logs \
  --is-multi-region-trail \
  --enable-log-file-validation
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Phase 3: Application Migration

Infrastructure as Code

AWS CloudFormation for reproducible infrastructure:

# application-stack.yaml
Resources:
  ApplicationLoadBalancer:
    Type: AWS::ElasticLoadBalancingV2::LoadBalancer
    Properties:
      Type: application
      Scheme: internet-facing
      Subnets:
        - !Ref PublicSubnet1
        - !Ref PublicSubnet2
      SecurityGroups:
        - !Ref ALBSecurityGroup
      LoadBalancerAttributes:
        - Key: idle_timeout.timeout_seconds
          Value: '60'

  ApplicationTargetGroup:
    Type: AWS::ElasticLoadBalancingV2::TargetGroup
    Properties:
      Port: 8080
      Protocol: HTTP
      VpcId: !Ref VPC
      HealthCheckPath: /health
      HealthCheckIntervalSeconds: 30
      HealthCheckTimeoutSeconds: 5
      HealthyThresholdCount: 2
      UnhealthyThresholdCount: 3

  ApplicationAutoScalingGroup:
    Type: AWS::AutoScaling::AutoScalingGroup
    Properties:
      MinSize: 2
      MaxSize: 10
      DesiredCapacity: 4
      VPCZoneIdentifier:
        - !Ref AppSubnet1
        - !Ref AppSubnet2
      LaunchTemplate:
        LaunchTemplateId: !Ref ApplicationLaunchTemplate
        Version: !GetAtt ApplicationLaunchTemplate.LatestVersionNumber
      TargetGroupARNs:
        - !Ref ApplicationTargetGroup
      HealthCheckType: ELB
      HealthCheckGracePeriod: 300
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Application Deployment

AWS Elastic Beanstalk (for simpler deployment) or ECS/EKS (for containerized):

# Option 1: Elastic Beanstalk
eb init -p java-11 payment-app --region us-east-1
eb create payment-app-prod \
  --instance-type t3.large \
  --min-instances 2 \
  --max-instances 10 \
  --envvars SPRING_PROFILES_ACTIVE=production

# Option 2: ECS (if containerizing)
aws ecs create-service \
  --cluster payment-cluster \
  --service-name payment-service \
  --task-definition payment-app:1 \
  --desired-count 4 \
  --launch-type FARGATE \
  --network-configuration "awsvpcConfiguration={subnets=[subnet-123,subnet-456],securityGroups=[sg-123],assignPublicIp=DISABLED}"
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Blue-Green Deployment Setup

AWS CodeDeploy for zero-downtime deployments:

# appspec.yml
version: 0.0
Resources:
  - TargetService:
      Type: AWS::ECS::Service
      Properties:
        TaskDefinition: <TASK_DEFINITION>
        LoadBalancerInfo:
          ContainerName: "payment-app"
          ContainerPort: 8080
Hooks:
  - BeforeInstall: "validate.sh"
  - AfterInstall: "health-check.sh"
  - AfterAllowTestTraffic: "smoke-tests.sh"
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Traffic Shifting Strategy:

# Gradually shift traffic from on-premise to AWS
# Week 1: 5% traffic to AWS
# Week 2: 10% traffic to AWS
# Week 3: 25% traffic to AWS
# Week 4: 50% traffic to AWS
# Week 5: 75% traffic to AWS
# Week 6: 100% traffic to AWS (cutover)

# Using Route 53 weighted routing
aws route53 change-resource-record-sets \
  --hosted-zone-id Z123456789 \
  --change-batch file://traffic-shift-5-percent.json
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Route 53 Weighted Routing Configuration:

{
  "Changes": [{
    "Action": "UPSERT",
    "ResourceRecordSet": {
      "Name": "payment.example.com",
      "Type": "A",
      "SetIdentifier": "on-premise",
      "Weight": 95,
      "AliasTarget": {
        "DNSName": "on-premise-lb.example.com",
        "EvaluateTargetHealth": true,
        "HostedZoneId": "Z123456789"
      }
    }
  }, {
    "Action": "UPSERT",
    "ResourceRecordSet": {
      "Name": "payment.example.com",
      "Type": "A",
      "SetIdentifier": "aws",
      "Weight": 5,
      "AliasTarget": {
        "DNSName": "aws-alb-123456789.us-east-1.elb.amazonaws.com",
        "EvaluateTargetHealth": true,
        "HostedZoneId": "Z123456789"
      }
    }
  }]
}
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Phase 4: Database Migration (The Critical Phase)

Pre-Migration Preparation

Database Optimization:

-- Analyze database before migration
ANALYZE;

-- Check for unused indexes
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes
WHERE idx_scan = 0
ORDER BY pg_relation_size(indexrelid) DESC;

-- Vacuum and reindex
VACUUM FULL ANALYZE;
REINDEX DATABASE paymentdb;
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Estimate Migration Time:

# Test migration speed with sample data
# Typical DMS throughput: 100-500 MB/s
# 8TB = 8,192 GB = 8,388,608 MB
# At 200 MB/s: ~11.6 hours for initial load
# Plus ongoing replication for cutover
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AWS DMS Setup

Step 1: Create Source Endpoint (On-Premise)

# Create source endpoint pointing to on-premise PostgreSQL
aws dms create-endpoint \
  --endpoint-identifier on-premise-postgres \
  --endpoint-type source \
  --engine-name postgres \
  --server-name on-premise-db.example.com \
  --port 5432 \
  --username postgres \
  --password 'secure-password' \
  --database-name paymentdb \
  --extra-connection-attributes "heartbeatFrequency=5;"
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Step 2: Create Target Endpoint (AWS RDS)

# Create RDS PostgreSQL instance
aws rds create-db-instance \
  --db-instance-identifier payment-db-aws \
  --db-instance-class db.r5.4xlarge \
  --engine postgres \
  --engine-version 13.7 \
  --master-username postgres \
  --master-user-password 'secure-password' \
  --allocated-storage 10000 \
  --storage-type gp3 \
  --storage-encrypted \
  --vpc-security-group-ids sg-db-12345678 \
  --db-subnet-group-name payment-db-subnet-group \
  --backup-retention-period 35 \
  --multi-az \
  --publicly-accessible false

# Create target endpoint
aws dms create-endpoint \
  --endpoint-identifier aws-rds-postgres \
  --endpoint-type target \
  --engine-name postgres \
  --server-name payment-db-aws.xxxxx.us-east-1.rds.amazonaws.com \
  --port 5432 \
  --username postgres \
  --password 'secure-password' \
  --database-name paymentdb
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Step 3: Create DMS Replication Instance

# Create replication instance (sized for 8TB migration)
aws dms create-replication-instance \
  --replication-instance-identifier payment-dms-instance \
  --replication-instance-class dms.r5.4xlarge \
  --allocated-storage 1000 \
  --vpc-security-group-ids sg-dms-12345678 \
  --replication-subnet-group-identifier payment-dms-subnet-group \
  --publicly-accessible false \
  --multi-az
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Step 4: Create Migration Task

# Full load + CDC (Change Data Capture) for zero downtime
aws dms create-replication-task \
  --replication-task-identifier payment-db-migration \
  --source-endpoint-arn arn:aws:dms:region:account:endpoint:on-premise-postgres \
  --target-endpoint-arn arn:aws:dms:region:account:endpoint:aws-rds-postgres \
  --replication-instance-arn arn:aws:dms:region:account:rep:payment-dms-instance \
  --migration-type full-load-and-cdc \
  --table-mappings file://table-mappings.json \
  --replication-task-settings file://task-settings.json
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Table Mappings Configuration:

{
  "rules": [
    {
      "rule-type": "selection",
      "rule-id": "1",
      "rule-name": "1",
      "object-locator": {
        "schema-name": "public",
        "table-name": "%"
      },
      "rule-action": "include"
    },
    {
      "rule-type": "transformation",
      "rule-id": "2",
      "rule-name": "2",
      "rule-action": "remove-column",
      "object-locator": {
        "schema-name": "public",
        "table-name": "audit_log",
        "column-name": "sensitive_data"
      }
    }
  ]
}
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Task Settings for Performance:

{
  "TargetMetadata": {
    "TargetSchema": "public",
    "SupportLobs": true,
    "FullLobMode": false,
    "LobChunkSize": 64,
    "LimitedSizeLobMode": true,
    "LobMaxSize": 32,
    "InlineLobMaxSize": 0,
    "LoadMaxFileSize": 0,
    "ParallelLoadThreads": 0,
    "ParallelLoadBufferSize": 0,
    "BatchApplyEnabled": false,
    "TaskRecoveryTableEnabled": false
  },
  "FullLoadSettings": {
    "TargetTablePrepMode": "DO_NOTHING",
    "CreatePkAfterFullLoad": false,
    "StopTaskCachedChangesApplied": false,
    "StopTaskCachedChangesNotApplied": false,
    "MaxFullLoadSubTasks": 8,
    "TransactionConsistencyTimeout": 600,
    "CommitRate": 10000
  },
  "Logging": {
    "EnableLogging": true,
    "LogComponents": [
      {
        "Id": "SOURCE_UNLOAD",
        "Severity": "LOGGER_SEVERITY_DEFAULT"
      },
      {
        "Id": "TARGET_LOAD",
        "Severity": "LOGGER_SEVERITY_DEFAULT"
      }
    ]
  }
}
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Migration Execution

Phase 1: Full Load (Initial Data Migration)

# Start replication task
aws dms start-replication-task \
  --replication-task-arn arn:aws:dms:region:account:task:payment-db-migration \
  --start-replication-task-type start-replication

# Monitor progress
aws dms describe-replication-tasks \
  --replication-task-arn arn:aws:dms:region:account:task:payment-db-migration
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Monitor Migration Progress:

import boto3
import time

dms = boto3.client('dms')

def monitor_migration(task_arn):
    """Monitor DMS task progress"""
    while True:
        response = dms.describe_replication_tasks(
            ReplicationTaskArn=task_arn
        )
        task = response['ReplicationTasks'][0]

        status = task['Status']
        progress = task.get('ReplicationTaskStats', {})

        print(f"Status: {status}")
        print(f"Tables Loaded: {progress.get('TablesLoaded', 0)}")
        print(f"Tables Loading: {progress.get('TablesLoading', 0)}")
        print(f"Tables Queued: {progress.get('TablesQueued', 0)}")

        if status == 'stopped':
            break

        time.sleep(60)

# Run monitoring
monitor_migration('arn:aws:dms:region:account:task:payment-db-migration')
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Phase 2: CDC (Change Data Capture) - Zero Downtime Window

Once full load completes, DMS automatically switches to CDC mode, capturing all changes from the source database in real-time.

Phase 3: Cutover

# 1. Stop application writes to on-premise database (brief window)
# 2. Wait for CDC to catch up (verify lag is near zero)
aws dms describe-replication-tasks \
  --replication-task-arn arn:aws:dms:region:account:task:payment-db-migration \
  --query 'ReplicationTasks[0].ReplicationTaskStats.CdcLatency'

# 3. Update application configuration to point to AWS RDS
# 4. Resume application writes to AWS RDS
# 5. Monitor for issues
# 6. If issues, rollback by pointing back to on-premise
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Verification Script:

import psycopg2
import boto3

def verify_data_consistency():
    """Compare record counts between source and target"""

    # Connect to on-premise (source)
    source_conn = psycopg2.connect(
        host="on-premise-db.example.com",
        database="paymentdb",
        user="postgres",
        password="password"
    )

    # Connect to AWS RDS (target)
    target_conn = psycopg2.connect(
        host="payment-db-aws.xxxxx.us-east-1.rds.amazonaws.com",
        database="paymentdb",
        user="postgres",
        password="password"
    )

    source_cur = source_conn.cursor()
    target_cur = target_conn.cursor()

    # Compare key tables
    tables = ['transactions', 'customers', 'accounts']

    for table in tables:
        source_cur.execute(f"SELECT COUNT(*) FROM {table}")
        target_cur.execute(f"SELECT COUNT(*) FROM {table}")

        source_count = source_cur.fetchone()[0]
        target_count = target_cur.fetchone()[0]

        if source_count != target_count:
            print(f"WARNING: {table} count mismatch: {source_count} vs {target_count}")
        else:
            print(f"✓ {table}: {source_count} records match")

    source_conn.close()
    target_conn.close()
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Phase 5: CI/CD Migration

Migrating from Jenkins to AWS

Option 1: AWS CodePipeline + CodeBuild

# buildspec.yml
version: 0.2
phases:
  pre_build:
    commands:
      - echo Logging in to Amazon ECR...
      - aws ecr get-login-password --region $AWS_DEFAULT_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com
      - COMMIT_HASH=$(echo $CODEBUILD_RESOLVED_SOURCE_VERSION | cut -c 1-7)
      - IMAGE_TAG=${COMMIT_HASH:=latest}
  build:
    commands:
      - echo Build started on `date`
      - echo Building the Docker image...
      - docker build -t $IMAGE_REPO_NAME:$IMAGE_TAG .
      - docker tag $IMAGE_REPO_NAME:$IMAGE_TAG $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/$IMAGE_REPO_NAME:$IMAGE_TAG
  post_build:
    commands:
      - echo Build completed on `date`
      - echo Pushing the Docker image...
      - docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/$IMAGE_REPO_NAME:$IMAGE_TAG
      - echo Writing image definitions file...
      - printf '[{"name":"payment-app","imageUri":"%s"}]' $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/$IMAGE_REPO_NAME:$IMAGE_TAG > imagedefinitions.json
artifacts:
  files:
    - imagedefinitions.json
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CodePipeline Pipeline Definition:

{
  "pipeline": {
    "name": "payment-app-pipeline",
    "roleArn": "arn:aws:iam::account:role/CodePipelineServiceRole",
    "artifactStore": {
      "type": "S3",
      "location": "payment-app-artifacts"
    },
    "stages": [
      {
        "name": "Source",
        "actions": [{
          "name": "SourceAction",
          "actionTypeId": {
            "category": "Source",
            "owner": "AWS",
            "provider": "CodeCommit",
            "version": "1"
          },
          "outputArtifacts": [{"name": "SourceOutput"}],
          "configuration": {
            "RepositoryName": "payment-app",
            "BranchName": "main"
          }
        }]
      },
      {
        "name": "Build",
        "actions": [{
          "name": "BuildAction",
          "actionTypeId": {
            "category": "Build",
            "owner": "AWS",
            "provider": "CodeBuild",
            "version": "1"
          },
          "inputArtifacts": [{"name": "SourceOutput"}],
          "outputArtifacts": [{"name": "BuildOutput"}],
          "configuration": {
            "ProjectName": "payment-app-build"
          }
        }]
      },
      {
        "name": "Deploy",
        "actions": [{
          "name": "DeployAction",
          "actionTypeId": {
            "category": "Deploy",
            "owner": "AWS",
            "provider": "ECS",
            "version": "1"
          },
          "inputArtifacts": [{"name": "BuildOutput"}],
          "configuration": {
            "ClusterName": "payment-cluster",
            "ServiceName": "payment-service"
          }
        }]
      }
    ]
  }
}
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Option 2: AWS CodePipeline with GitHub Actions

# .github/workflows/deploy.yml
name: Deploy to AWS

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v1
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1

      - name: Build and push Docker image
        run: |
          docker build -t payment-app:${{ github.sha }} .
          aws ecr get-login-password | docker login --username AWS --password-stdin ${{ secrets.AWS_ACCOUNT_ID }}.dkr.ecr.us-east-1.amazonaws.com
          docker push ${{ secrets.AWS_ACCOUNT_ID }}.dkr.ecr.us-east-1.amazonaws.com/payment-app:${{ github.sha }}

      - name: Deploy to ECS
        run: |
          aws ecs update-service \
            --cluster payment-cluster \
            --service payment-service \
            --force-new-deployment
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Security Scanning in CI/CD

AWS CodeBuild with Security Scanning:

# buildspec-with-security.yml
version: 0.2
phases:
  pre_build:
    commands:
      - echo Running security scans...
      - |
        # SAST scanning
        docker run --rm -v "$PWD:/src" returntocorp/semgrep semgrep --config=auto /src
      - |
        # Dependency scanning
        docker run --rm -v "$PWD:/src" owasp/dependency-check --scan /src --format JSON
  build:
    commands:
      - echo Building application...
      - mvn clean package
  post_build:
    commands:
      - echo Scanning container image...
      - |
        # Container image scanning
        aws ecr start-image-scan \
          --repository-name payment-app \
          --image-id imageTag=latest
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Phase 6: Monitoring & Validation

CloudWatch Dashboards

Application Health Dashboard:

# Create CloudWatch dashboard
aws cloudwatch put-dashboard \
  --dashboard-name payment-app-health \
  --dashboard-body file://dashboard.json
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Dashboard Configuration:

{
  "widgets": [
    {
      "type": "metric",
      "properties": {
        "metrics": [
          ["AWS/ApplicationELB", "RequestCount", {"stat": "Sum"}],
          [".", "TargetResponseTime", {"stat": "Average"}],
          [".", "HTTPCode_Target_5XX_Count", {"stat": "Sum"}]
        ],
        "period": 300,
        "stat": "Average",
        "region": "us-east-1",
        "title": "Application Load Balancer Metrics"
      }
    },
    {
      "type": "metric",
      "properties": {
        "metrics": [
          ["AWS/RDS", "DatabaseConnections", {"stat": "Average"}],
          [".", "CPUUtilization", {"stat": "Average"}],
          [".", "FreeableMemory", {"stat": "Average"}]
        ],
        "period": 300,
        "stat": "Average",
        "region": "us-east-1",
        "title": "RDS Database Metrics"
      }
    }
  ]
}
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Validation Tests

Automated Validation Script:

import boto3
import requests
import time

def validate_migration():
    """Comprehensive validation of migrated system"""

    results = {
        "application_health": False,
        "database_connectivity": False,
        "transaction_processing": False,
        "performance_metrics": False
    }

    # 1. Application Health Check
    try:
        response = requests.get("https://payment.example.com/health", timeout=5)
        if response.status_code == 200:
            results["application_health"] = True
    except Exception as e:
        print(f"Application health check failed: {e}")

    # 2. Database Connectivity
    rds = boto3.client('rds')
    try:
        response = rds.describe_db_instances(
            DBInstanceIdentifier='payment-db-aws'
        )
        if response['DBInstances'][0]['DBInstanceStatus'] == 'available':
            results["database_connectivity"] = True
    except Exception as e:
        print(f"Database connectivity check failed: {e}")

    # 3. Transaction Processing Test
    # Send test transaction and verify
    try:
        test_response = requests.post(
            "https://payment.example.com/api/transactions",
            json={"amount": 1.00, "currency": "USD"},
            timeout=10
        )
        if test_response.status_code == 200:
            results["transaction_processing"] = True
    except Exception as e:
        print(f"Transaction processing test failed: {e}")

    # 4. Performance Metrics
    cloudwatch = boto3.client('cloudwatch')
    end_time = time.time()
    start_time = end_time - 3600  # Last hour

    try:
        response = cloudwatch.get_metric_statistics(
            Namespace='AWS/ApplicationELB',
            MetricName='TargetResponseTime',
            Dimensions=[
                {'Name': 'LoadBalancer', 'Value': 'app/payment-alb/123456789'}
            ],
            StartTime=start_time,
            EndTime=end_time,
            Period=300,
            Statistics=['Average']
        )

        if response['Datapoints']:
            avg_response_time = sum([d['Average'] for d in response['Datapoints']]) / len(response['Datapoints'])
            if avg_response_time < 0.5:  # Less than 500ms
                results["performance_metrics"] = True
    except Exception as e:
        print(f"Performance metrics check failed: {e}")

    return results

# Run validation
validation_results = validate_migration()
print("Migration Validation Results:")
for check, passed in validation_results.items():
    status = "✓ PASS" if passed else "✗ FAIL"
    print(f"{check}: {status}")
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Phase 7: Cost Optimization

Reserved Instances Strategy 

# Purchase Reserved Instances for baseline capacity
aws ec2 purchase-reserved-instances-offering \
  --reserved-instances-offering-id 12345678-1234-1234-1234-123456789012 \
  --instance-count 4

# RDS Reserved Instances
aws rds purchase-reserved-db-instances-offering \
  --reserved-db-instances-offering-id 12345678-1234-1234-1234-123456789012 \
  --db-instance-count 1
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Auto-Scaling for Cost Efficiency 

{
  "TargetTrackingScalingPolicies": [
    {
      "TargetValue": 70.0,
      "PredefinedMetricSpecification": {
        "PredefinedMetricType": "ECSServiceAverageCPUUtilization"
      },
      "ScaleInCooldown": 300,
      "ScaleOutCooldown": 60
    }
  ]
}
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Cost Monitoring

AWS Cost Explorer API:

import boto3
from datetime import datetime, timedelta

ce = boto3.client('ce')

def get_daily_costs():
    """Get daily AWS costs"""
    end_date = datetime.now().strftime('%Y-%m-%d')
    start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')

    response = ce.get_cost_and_usage(
        TimePeriod={
            'Start': start_date,
            'End': end_date
        },
        Granularity='DAILY',
        Metrics=['UnblendedCost'],
        GroupBy=[
            {'Type': 'DIMENSION', 'Key': 'SERVICE'}
        ]
    )

    return response

# Monitor costs
costs = get_daily_costs()
for result in costs['ResultsByTime']:
    print(f"Date: {result['TimePeriod']['Start']}")
    for group in result['Groups']:
        service = group['Keys'][0]
        amount = group['Metrics']['UnblendedCost']['Amount']
        print(f"  {service}: ${amount}")
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Security & Compliance

Encryption

Encryption at Rest:

# RDS encryption
aws rds create-db-instance \
  --db-instance-identifier payment-db-aws \
  --storage-encrypted \
  --kms-key-id arn:aws:kms:region:account:key/key-id

# EBS encryption
aws ec2 create-volume \
  --size 100 \
  --volume-type gp3 \
  --encrypted \
  --kms-key-id arn:aws:kms:region:account:key/key-id
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Encryption in Transit:

# Application Load Balancer with SSL/TLS
Resources:
  ApplicationLoadBalancer:
    Properties:
      Listeners:
        - Protocol: HTTPS
          Port: 443
          Certificates:
            - CertificateArn: arn:aws:acm:region:account:certificate/cert-id
          DefaultActions:
            - Type: forward
              TargetGroupArn: !Ref ApplicationTargetGroup
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PCI-DSS Compliance

AWS PCI-DSS Compliance Checklist:

  1. Network Segmentation: VPC with private subnets
  2. Encryption: At rest and in transit
  3. Access Control: IAM roles, security groups
  4. Monitoring: CloudWatch, CloudTrail, GuardDuty
  5. Vulnerability Management: Security scanning in CI/CD
  6. Audit Logging: CloudTrail, VPC Flow Logs

AWS GuardDuty for threat detection:

# Enable GuardDuty
aws guardduty create-detector \
  --enable \
  --finding-publishing-frequency FIFTEEN_MINUTES
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GDPR Compliance

Data Residency:

# Ensure RDS is in correct region
aws rds describe-db-instances \
  --db-instance-identifier payment-db-aws \
  --query 'DBInstances[0].AvailabilityZone'
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Data Retention Policies:

import boto3

def configure_s3_lifecycle():
    """Configure S3 lifecycle for GDPR data retention"""
    s3 = boto3.client('s3')

    s3.put_bucket_lifecycle_configuration(
        Bucket='payment-audit-logs',
        LifecycleConfiguration={
            'Rules': [{
                'Id': 'DeleteAfter7Years',
                'Status': 'Enabled',
                'Expiration': {
                    'Days': 2555  # 7 years
                }
            }]
        }
    )
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Rollback Strategy

Database Rollback 

# If issues occur, point application back to on-premise database
# DMS can reverse replication if needed
aws dms create-replication-task \
  --replication-task-identifier payment-db-rollback \
  --source-endpoint-arn arn:aws:dms:region:account:endpoint:aws-rds-postgres \
  --target-endpoint-arn arn:aws:dms:region:account:endpoint:on-premise-postgres \
  --replication-instance-arn arn:aws:dms:region:account:rep:payment-dms-instance \
  --migration-type cdc-only \
  --table-mappings file://table-mappings.json
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Application Rollback 

# Route traffic back to on-premise using Route 53
aws route53 change-resource-record-sets \
  --hosted-zone-id Z123456789 \
  --change-batch file://rollback-traffic.json
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Rollback Decision Matrix:

IF error_rate > 1% OR response_time > 2s:
    → Rollback to on-premise
ELSE IF data_integrity_issues:
    → Rollback to on-premise
ELSE IF security_incident:
    → Rollback to on-premise
ELSE:
    → Continue monitoring
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Success Metrics

Migration Success Criteria

  1. Zero Downtime: No customer-facing downtime during migration
  2. Data Integrity: 100% data consistency between source and target
  3. Performance: Response times ≤ on-premise baseline
  4. Cost Reduction: 40% cost savings achieved
  5. Uptime: 99.99% availability (vs. 99.9% before)
  6. Compliance: PCI-DSS and GDPR requirements met

Key Performance Indicators 

# Migration KPIs
migration_kpis = {
    "downtime_minutes": 0,  # Target: 0
    "data_consistency_percent": 100,  # Target: 100%
    "cost_reduction_percent": 40,  # Target: 40%
    "uptime_percent": 99.99,  # Target: 99.99%
    "migration_duration_days": 180,  # Target: < 180 days
    "rollback_incidents": 0  # Target: 0
}
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Lessons Learned & Best Practices

Critical Success Factors

  1. Thorough Planning: Spend adequate time in assessment phase
  2. Incremental Migration: Don't try to migrate everything at once
  3. Comprehensive Testing: Test every component before cutover
  4. Monitoring: Real-time visibility is essential
  5. Rollback Readiness: Always have a rollback plan
  6. Team Training: Ensure team is trained on AWS services

Common Pitfalls to Avoid

  • Underestimating database migration time
  • Not testing rollback procedures
  • Ignoring network latency between on-premise and AWS
  • Forgetting to update DNS TTL before cutover
  • Not monitoring costs during migration
  • Skipping security and compliance validation

Conclusion

Migrating a fintech platform to AWS with zero downtime is complex but achievable with the right strategy. Key takeaways:

  1. AWS DMS enables zero-downtime database migration for large databases
  2. Blue-Green deployment with gradual traffic shifting minimizes risk
  3. Infrastructure as Code ensures reproducibility and rollback capability
  4. Comprehensive monitoring provides visibility throughout migration
  5. Cost optimization through Reserved Instances and auto-scaling achieves savings targets

The migration journey transforms not just infrastructure, but also processes, security posture, and operational efficiency. With careful planning and execution, you can achieve zero downtime, maintain compliance, and realize significant cost savings.

Additional Resources

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