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
Blue Environment: Current on-premise infrastructure (production)
Green Environment: New AWS infrastructure (parallel)
Gradual Cutover: Route traffic incrementally from Blue to Green
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)
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
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
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
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
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
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"
}
}
}
]
}
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
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
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
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
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}"
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"
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
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"
}
}
}]
}
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;
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
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;"
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
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
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
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"
}
}
]
}
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"
}
]
}
}
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
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')
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
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()
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
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"
}
}]
}
]
}
}
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
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
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
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"
}
}
]
}
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}")
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
Auto-Scaling for Cost Efficiency
{
"TargetTrackingScalingPolicies": [
{
"TargetValue": 70.0,
"PredefinedMetricSpecification": {
"PredefinedMetricType": "ECSServiceAverageCPUUtilization"
},
"ScaleInCooldown": 300,
"ScaleOutCooldown": 60
}
]
}
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}")
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
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
PCI-DSS Compliance
AWS PCI-DSS Compliance Checklist:
Network Segmentation: VPC with private subnets
Encryption: At rest and in transit
Access Control: IAM roles, security groups
Monitoring: CloudWatch, CloudTrail, GuardDuty
Vulnerability Management: Security scanning in CI/CD
Audit Logging: CloudTrail, VPC Flow Logs
AWS GuardDuty for threat detection:
# Enable GuardDuty
aws guardduty create-detector \
--enable \
--finding-publishing-frequency FIFTEEN_MINUTES
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'
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
}
}]
}
)
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
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
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
Success Metrics
Migration Success Criteria
Zero Downtime: No customer-facing downtime during migration
Data Integrity: 100% data consistency between source and target
Performance: Response times ≤ on-premise baseline
Cost Reduction: 40% cost savings achieved
Uptime: 99.99% availability (vs. 99.9% before)
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
}
Lessons Learned & Best Practices
Critical Success Factors
Thorough Planning: Spend adequate time in assessment phase
Incremental Migration: Don't try to migrate everything at once
Comprehensive Testing: Test every component before cutover
Monitoring: Real-time visibility is essential
Rollback Readiness: Always have a rollback plan
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:
AWS DMS enables zero-downtime database migration for large databases
Blue-Green deployment with gradual traffic shifting minimizes risk
Infrastructure as Code ensures reproducibility and rollback capability
Comprehensive monitoring provides visibility throughout migration
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.
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