Enterprise Guide to Migrating AWS Amplify Applications to Native AWS Services

AWS Amplify has served many enterprises well as a rapid development platform, but growing businesses often hit limits that require migrating to native AWS services for better control, cost optimization, and scalability. This comprehensive guide walks enterprise development teams, DevOps engineers, and technical leaders through the complete process of moving from AWS Amplify to a custom native AWS architecture.

You’ll learn how to assess your current Amplify setup and map each component to its native AWS equivalent, from replacing Amplify Auth with Amazon Cognito to migrating your GraphQL APIs to API Gateway and Lambda. We’ll cover designing a migration strategy that minimizes risk and downtime, including how to implement proper security controls and execute data migration without disrupting your users. Finally, you’ll discover how to optimize your new native AWS infrastructure for both performance and cost, setting up monitoring systems that give you the visibility Amplify’s abstractions once hid.

Assess Your Current AWS Amplify Architecture and Dependencies

Audit Existing Amplify Services and Configurations

Start by creating a comprehensive inventory of all Amplify services currently running in your environment. This AWS Amplify architecture assessment forms the foundation of your migration strategy. Document each service, its configuration, and resource utilization patterns.

Review your amplify/backend folder structure to understand the services you’ve deployed. Common services include Authentication (Cognito), API (AppSync/API Gateway), Storage (S3), Functions (Lambda), Analytics (Pinpoint), and Hosting. Export configuration files and note any custom settings that deviate from default Amplify configurations.

Pay special attention to environment-specific configurations. Many organizations run multiple Amplify environments (development, staging, production), each with different settings. Document these variations to ensure your native AWS services migration maintains the same environment separation.

Check your Amplify CLI version and any custom plugins or extensions you’ve added. These modifications might require special consideration during migration planning. Also, review any Infrastructure as Code (IaC) implementations you’ve built on top of Amplify’s generated CloudFormation templates.

Identify Critical Application Components and Data Flows

Map out how data moves through your application ecosystem. Start with user interactions and trace the complete journey from frontend requests to backend processing and storage. This exercise reveals dependencies that aren’t immediately obvious from looking at individual services.

Document your API structure, including GraphQL schemas, REST endpoints, and any custom resolvers. Note which APIs handle critical business functions versus those supporting auxiliary features. This prioritization helps you sequence your migration activities effectively.

Examine data relationships between services. For example, how does user authentication flow from Cognito through AppSync to your data sources? Understanding these connections prevents breaking critical functionality during the migration process.

Create visual diagrams showing your current architecture. These serve as reference points throughout the migration and help team members understand the system’s complexity. Include data volumes, peak usage patterns, and performance requirements for each component.

Document Current Authentication and Authorization Mechanisms

Catalog your Cognito User Pool configuration, including user attributes, policies, and any custom authentication flows. Document multi-factor authentication settings, password policies, and social identity provider integrations. These configurations need precise replication in your target native AWS services.

Review your authorization patterns carefully. Amplify often implements role-based access control through Cognito Groups and IAM policies. Document which user types access which resources and how permissions cascade through your application layers.

Examine any custom authentication logic you’ve implemented. This might include pre-signup Lambda triggers, custom message templates, or complex authorization rules in AppSync resolvers. These customizations require careful planning to migrate without disrupting user experience.

Don’t overlook API keys, service-to-service authentication, and any hardcoded credentials that might exist in your current setup. These elements need secure migration paths to prevent security gaps during the transition.

Evaluate Third-Party Integrations and External Dependencies

Inventory all external services that connect to your Amplify application. This includes payment processors, email services, analytics platforms, content delivery networks, and any SaaS tools integrated through APIs or webhooks.

Document how these integrations authenticate with your Amplify services. Some might use API keys stored in Amplify’s parameter store, while others might rely on IAM roles or custom authentication mechanisms. Each integration method requires a specific migration approach.

Review any Amplify-specific features these integrations rely on. For instance, if you’re using Amplify’s built-in CI/CD pipeline to deploy integration updates, you’ll need alternative deployment strategies for your native AWS services implementation.

Check for dependencies on Amplify’s GraphQL subscriptions for real-time features. External services might subscribe to your AppSync endpoints for live updates. Plan how to maintain these connections during migration or provide alternative real-time communication channels.

Consider the timing of integration updates. Some third-party services might cache connection details or have specific update windows. Coordinate with these providers to ensure smooth transitions without service interruptions.

Map Amplify Services to Native AWS Equivalents

Transform Amplify Auth to Amazon Cognito Implementation

Amplify Auth serves as a wrapper around Amazon Cognito, so migrating this service involves refactoring your authentication implementation to use Cognito directly. The transition requires updating your application to use the AWS Cognito SDK instead of Amplify’s auth library.

Start by examining your current Amplify Auth configuration, including user pools, identity pools, and any custom authentication flows. Your user data already lives in Cognito, so the migration focuses on changing how your application interacts with these services. Replace Amplify’s Auth.signIn() and similar methods with direct Cognito API calls using AWS.CognitoIdentityServiceProvider.

Key considerations include maintaining existing user sessions during the transition and ensuring multi-factor authentication (MFA) settings carry over seamlessly. You’ll need to update your frontend code to handle authentication responses differently, as native Cognito returns more detailed response objects than Amplify’s simplified interface.

Amplify Auth Feature	Native Cognito Equivalent	Migration Notes
Auth.signIn()	cognitoIdentityServiceProvider.adminInitiateAuth()	Requires additional error handling
Auth.currentAuthenticatedUser()	cognitoIdentityServiceProvider.getUser()	Manual token management needed
Social sign-in	Cognito Identity Providers	Direct federation setup required

Convert Amplify API to Amazon API Gateway and AWS Lambda

Amplify API automatically provisions API Gateway and Lambda functions behind the scenes. For native AWS services mapping, you’ll recreate these resources manually while gaining full control over configuration and optimization.

Begin by documenting all your existing API endpoints, including HTTP methods, request/response schemas, and authentication requirements. Each Amplify API endpoint translates directly to an API Gateway resource with corresponding Lambda function integrations.

The migration process involves creating new API Gateway stages, configuring custom domain names, and setting up proper CORS policies. You’ll replace Amplify’s automatic GraphQL schema generation with manual API Gateway resource definitions. This gives you the flexibility to implement advanced features like request validation, response caching, and custom authorizers.

Lambda functions require minimal changes since Amplify already uses them under the hood. However, you’ll gain access to advanced configuration options like provisioned concurrency, dead letter queues, and detailed CloudWatch metrics that weren’t accessible through Amplify’s abstractions.

Migrate Amplify DataStore to Amazon DynamoDB with AppSync

Amplify DataStore provides offline-first data synchronization built on top of DynamoDB and AppSync. Migrating to native AWS services means setting up AppSync GraphQL APIs and DynamoDB tables manually while preserving your data synchronization capabilities.

Export your existing GraphQL schema from Amplify and recreate it in AppSync with custom resolvers. The data migration involves copying existing DynamoDB table structures and implementing conflict resolution strategies that match your current DataStore behavior.

Native AppSync offers advanced features like real-time subscriptions with custom filters, pipeline resolvers for complex data operations, and direct integration with other AWS services. You’ll configure VTL (Velocity Template Language) resolvers to handle data transformations that DataStore managed automatically.

The migration also requires updating your client-side code to use AppSync SDK instead of DataStore APIs. This means replacing DataStore.save() operations with GraphQL mutations and implementing manual conflict resolution logic where needed.

Replace Amplify Storage with Amazon S3 and CloudFront

Amplify Storage simplifies file upload and retrieval using S3 buckets with optional CloudFront distribution. The native AWS services migration involves recreating these storage configurations with explicit S3 bucket policies and CloudFront distributions.

Create new S3 buckets with appropriate lifecycle policies, versioning settings, and access controls that match your current Amplify Storage configuration. Set up CloudFront distributions for global content delivery, ensuring proper cache behaviors and origin access identities.

The client-side migration requires replacing Amplify’s Storage.put() and Storage.get() methods with direct S3 SDK calls. You’ll need to implement presigned URL generation for secure file uploads and configure proper CORS settings for browser-based applications.

Consider implementing S3 Transfer Acceleration for improved upload performance and setting up CloudWatch monitoring for storage metrics that weren’t visible through Amplify’s interface.

Transition Amplify Functions to Standalone AWS Lambda Services

Amplify Functions are essentially AWS Lambda functions with simplified deployment and configuration. The migration to standalone Lambda services involves extracting your function code and recreating the deployment pipeline with full AWS Lambda capabilities.

Package your existing function code with proper dependency management using tools like AWS SAM or Serverless Framework. This migration unlocks advanced Lambda features like layers for shared code, environment-specific configurations, and custom runtime environments.

Set up proper IAM roles and policies for each function, replacing Amplify’s automatic permission management with explicit resource access controls. Implement CloudWatch logging and monitoring with custom metrics that provide deeper insights into function performance.

The transition also enables integration with other AWS services like EventBridge for event-driven architectures, Step Functions for complex workflows, and VPC configurations for private resource access.

Design Your Migration Strategy and Timeline

Prioritize Services Based on Business Impact and Complexity

Start your AWS Amplify migration by ranking services according to their business criticality and technical complexity. Authentication services typically deserve top priority since they affect user access across your entire application. If your Amplify Auth breaks during migration, users can’t log in, making this a high-impact, moderate-complexity migration that should happen early in your timeline.

APIs come next, especially those powering core business functions. A payment processing API carries more weight than a notification service, so tackle revenue-generating endpoints first. Database migrations often rank as high complexity but vary in business impact depending on the data they store. Customer data and transaction records need careful handling, while logging data can often wait.

Storage services usually fall into the medium priority range unless they handle critical assets like user documents or images that directly impact user experience. Analytics and monitoring services often rank lower in business impact but can be surprisingly complex due to data retention requirements and integration dependencies.

Create a migration matrix that plots each service on business impact (high, medium, low) versus complexity (simple, moderate, complex). This visual approach helps stakeholders understand why certain services get migrated first and sets realistic expectations about timeline and resource allocation.

Consider dependencies between services when setting priorities. Your API Gateway might depend on Lambda functions, which depend on database connections. Breaking these dependency chains incorrectly can cascade into system-wide outages.

Create Detailed Rollback Plans for Each Migration Phase

Every migration step needs a tested rollback plan that can restore service within your defined recovery time objectives. Document the exact steps to revert each service back to Amplify, including specific commands, configuration files, and database restore procedures.

Test rollback procedures before executing migrations. Set up scenarios that simulate common failure points like authentication errors, database connection failures, or API timeouts. Your rollback plan should work even when the person executing it is under pressure during a production incident.

Create rollback triggers that define when to abort a migration. Set specific metrics like response time thresholds, error rates, or user complaint volumes that automatically signal when to reverse course. For example, if API response times increase by more than 200ms or error rates exceed 1%, initiate rollback procedures immediately.

Document rollback ownership clearly. Assign specific team members to execute rollback procedures and ensure they have the necessary permissions and access. During high-stress migrations, clarity about who does what prevents confusion and reduces recovery time.

Prepare rollback communication plans that notify stakeholders about service restoration status. Templates for internal team updates and customer-facing communications should be ready to deploy when rollbacks happen.

Establish Testing Environments That Mirror Production Workloads

Build testing environments that accurately reflect your production traffic patterns and data volumes. Synthetic testing with minimal data often misses performance bottlenecks that only appear under real-world conditions. Load testing with production-scale datasets reveals issues with query performance, memory usage, and connection pooling that small test datasets hide.

Configure your test environment with the same AWS instance types, storage configurations, and network settings as production. Using t3.micro instances for testing when production runs on c5.xlarge instances creates a false sense of security about performance characteristics.

Implement automated testing pipelines that validate functionality, performance, and security after each migration step. These tests should cover user authentication flows, API responses, database queries, and file storage operations. Automated tests catch regressions quickly and provide confidence that migrations won’t break existing functionality.

Create test data that represents your actual user behavior patterns. If your production system handles image uploads during peak hours, your test environment should simulate similar upload volumes and file sizes. Anonymous production data often works better than synthetic test data for uncovering real-world issues.

Set up monitoring in test environments that matches your production monitoring stack. This approach helps validate that your monitoring and alerting systems will work correctly after migration. Testing without proper monitoring is like driving blindfolded – you won’t know when something goes wrong until it’s too late.

Plan for test environment refresh cycles that keep test data current with production schemas and configurations. Stale test environments often provide false confidence about migration readiness and can miss critical compatibility issues.

Implement Security and Access Controls in Native Services

Configure IAM Roles and Policies for Granular Permissions

Creating a robust security foundation starts with implementing least-privilege access principles through carefully crafted IAM policies. When migrating from AWS Amplify to native services, you’ll need to establish separate service roles for each component instead of relying on Amplify’s bundled permissions model.

Start by creating dedicated service roles for your core services. Lambda functions require execution roles with specific permissions for accessing DynamoDB tables, S3 buckets, or other AWS resources. API Gateway needs invoke permissions for Lambda functions and proper resource-based policies. For DynamoDB, establish roles that allow only necessary read/write operations on specific tables and indexes.

Cross-service authentication becomes critical when moving away from Amplify’s integrated auth system. Configure assume-role policies that allow your services to communicate securely. For example, your API Gateway should assume a role that grants limited Lambda invoke permissions, while Lambda functions should have roles that provide only the database access they require.

User authentication and authorization need special attention during AWS Amplify migration. Replace Amplify’s user pools with Amazon Cognito user pools configured with custom attribute mappings, group-based access controls, and integration with your existing identity providers. Set up fine-grained policies that map Cognito groups to specific API endpoints and data access patterns.

Consider implementing attribute-based access control (ABAC) for complex permission scenarios. This approach allows dynamic permission evaluation based on user attributes, resource tags, and environmental factors, providing more flexibility than traditional role-based models.

Set Up VPC and Networking Security for Isolated Environments

Network isolation becomes your responsibility when migrating away from Amplify’s managed infrastructure. Design a VPC architecture that segments your application components based on their security requirements and data sensitivity levels.

Create separate subnets for different application tiers: public subnets for load balancers and API gateways, private subnets for Lambda functions and application servers, and isolated subnets for database resources. This three-tier approach ensures that sensitive data processing occurs in protected network segments with no direct internet access.

Configure security groups as virtual firewalls for each service. Database security groups should only allow inbound traffic from application server security groups on specific ports. Lambda functions in VPC require careful security group configuration to maintain internet access for external API calls while restricting unnecessary inbound traffic.

Implement VPC endpoints for AWS services to keep traffic within the AWS network backbone. S3 VPC endpoints, DynamoDB VPC endpoints, and interface endpoints for services like Secrets Manager eliminate the need for internet routing, reducing attack surface and improving performance.

Network Access Control Lists (NACLs) provide an additional layer of subnet-level filtering. Configure NACLs to deny traffic that shouldn’t exist between subnets, such as direct communication between public and database tiers.

Implement Encryption at Rest and in Transit Across All Services

Data protection requires comprehensive encryption coverage across all storage and transmission points. Each AWS service offers specific encryption capabilities that need individual configuration during your migration from Amplify’s default settings.

DynamoDB encryption uses AWS KMS keys for protecting data at rest. Create customer-managed KMS keys with proper key rotation policies instead of relying on AWS-managed keys. This approach provides audit trails and granular access control over who can decrypt your data. Configure point-in-time recovery with encryption to ensure backup data remains protected.

S3 bucket encryption requires both default encryption settings and bucket policies that deny unencrypted object uploads. Implement S3 Bucket Key to reduce KMS costs while maintaining security. For highly sensitive data, consider client-side encryption where data gets encrypted before leaving your application.

Lambda environment variables containing sensitive information need KMS encryption. Store database connection strings, API keys, and other secrets in AWS Systems Manager Parameter Store or AWS Secrets Manager with automatic rotation enabled.

API Gateway enforces HTTPS-only communication through SSL/TLS certificates managed by AWS Certificate Manager. Configure minimum TLS versions and cipher suites that meet your security requirements. For internal communications, establish mutual TLS (mTLS) authentication between services.

RDS instances require encryption at rest configuration during creation time. Enable automated backups with encryption and configure SSL connections for all database clients. CloudWatch Logs encryption ensures that application logs remain protected with KMS keys.

Establish Monitoring and Logging with CloudTrail and CloudWatch

Comprehensive observability replaces Amplify’s simplified monitoring with enterprise-grade logging and alerting capabilities. CloudTrail provides complete API audit trails for all AWS service interactions, creating an immutable record of administrative and programmatic access to your resources.

Configure CloudTrail with S3 bucket logging and CloudWatch Logs integration for real-time analysis. Enable multi-region trails to capture cross-region activity and data events for S3 bucket access and Lambda function invocations. Set up log file integrity validation to detect tampering attempts.

CloudWatch metrics and alarms form your operational monitoring foundation. Create custom metrics for business-specific indicators like user registration rates, API response times per endpoint, and database query performance. Lambda functions benefit from X-Ray tracing integration for detailed performance analysis and error tracking.

Centralized logging through CloudWatch Logs aggregates application logs from multiple sources. Structure your log entries with consistent formats that enable automated parsing and alerting. Implement log retention policies that balance compliance requirements with storage costs.

Dashboard creation in CloudWatch provides visual monitoring for operations teams. Build role-specific dashboards showing relevant metrics and trends. Operations dashboards might focus on system health and performance, while business dashboards highlight user engagement and transaction volumes.

Security monitoring requires specialized CloudWatch rules and SNS notifications for suspicious activities. Monitor failed authentication attempts, unusual data access patterns, and administrative actions outside normal business hours. Integration with AWS Security Hub provides centralized security finding management across all your AWS resources.

Execute Data Migration with Zero Downtime

Design parallel data synchronization strategies

Setting up parallel data synchronization is your safety net during AWS Amplify migration. Think of it as running two systems side by side while gradually shifting traffic from old to new. This approach keeps your production environment stable while you validate everything works correctly in the native AWS services setup.

Start by implementing AWS Database Migration Service (DMS) for continuous data replication between your existing Amplify-managed databases and new native services. DMS captures ongoing changes in real-time, so your new environment stays current with production data. For DynamoDB migrations, use DynamoDB Streams to trigger Lambda functions that replicate data to your new tables.

Application-level synchronization works well for complex data transformations. Build lightweight sync services that read from source systems and write to target systems, handling any schema changes or data format conversions needed. These services can run on scheduled intervals or respond to events, depending on your data freshness requirements.

Consider using Amazon EventBridge to orchestrate data flows between systems. Create custom events when data changes occur in your source systems, then route those events to appropriate handlers that update your target systems. This event-driven approach provides flexibility and makes it easier to add new synchronization targets later.

Monitor synchronization lag closely using CloudWatch metrics. Set up alerts when replication falls behind acceptable thresholds. Document rollback procedures for each data store, including steps to redirect traffic back to original systems if issues arise.

Implement blue-green deployment patterns for seamless transitions

Blue-green deployment eliminates downtime by running two identical production environments. Your current Amplify setup becomes the “blue” environment, while your new native AWS services setup becomes the “green” environment. You can switch traffic between them instantly using DNS routing or load balancer configuration changes.

Amazon Route 53 provides weighted routing policies that let you gradually shift traffic percentages between environments. Start with 5% of traffic going to your green environment, monitor performance and error rates, then increase the percentage over time. If problems occur, you can redirect traffic back to blue immediately.

Application Load Balancer target groups offer another switching mechanism. Configure separate target groups for blue and green environments, then use listener rules to control traffic distribution. This approach works particularly well when your application architecture includes API Gateway or other load-balanced services.

Database migrations require special attention in blue-green deployments. Use read replicas to keep both environments synchronized during the transition period. For write operations, implement application logic that can write to both environments temporarily, then switch to write-only to green once validation completes.

Container-based applications benefit from Amazon ECS or EKS blue-green deployments. These services provide built-in deployment strategies that handle traffic shifting automatically. Configure health checks and rollback triggers to ensure problematic deployments revert automatically.

Test your switching mechanisms thoroughly before migration day. Practice the entire cutover process, including rollback scenarios. Document exact steps and timing for each component switch, and assign specific team members to handle different parts of the process.

Validate data integrity throughout the migration process

Data validation catches problems early, before they affect users. Build comprehensive validation checks that run continuously during migration, comparing data between source and target systems. These checks should verify record counts, data values, and business logic constraints.

Create automated comparison scripts that sample data from both environments and flag discrepancies. For large datasets, statistical sampling provides good coverage without overwhelming system resources. Focus validation efforts on critical business data first, then expand to less critical information.

Implement checksum validation for binary data and file transfers. AWS S3 provides built-in integrity checking for object uploads, but add your own verification layers for important files. Store checksums in DynamoDB or RDS for quick lookup and comparison.

Business logic validation ensures your new native services produce the same results as Amplify services. Create test scenarios that exercise key workflows, then compare outputs between systems. Pay special attention to calculations, aggregations, and data transformations that might behave differently in new environments.

Set up automated alerts for validation failures. Configure CloudWatch alarms that trigger when data discrepancies exceed acceptable thresholds. Include relevant team members in alert notifications, and provide enough context in alert messages to enable quick troubleshooting.

Keep detailed logs of all validation activities. Record what was checked, when checks ran, and what discrepancies were found. This audit trail becomes valuable for post-migration reviews and helps identify patterns in data quality issues. Use structured logging formats that make it easy to search and analyze validation results later.

Optimize Performance and Cost Management

Right-size resources based on actual usage patterns

After completing your AWS Amplify migration to native AWS services, you’ll likely find that the default resource configurations don’t match your actual needs. Most organizations discover they’re either over-provisioned (wasting money) or under-provisioned (hurting performance).

Start by analyzing your CloudWatch metrics from the past 30-90 days. Look at CPU utilization, memory usage, network throughput, and storage patterns across your EC2 instances, RDS databases, and Lambda functions. You’ll probably notice that many resources run at 20-30% capacity during normal operations, with occasional spikes.

EC2 Instance Optimization:

• Review instance types against actual CPU and memory usage
• Consider switching from general-purpose to compute-optimized or memory-optimized instances based on workload patterns
• Test smaller instance types in non-production environments first

Database Right-sizing:

• Monitor RDS performance metrics to identify over-allocated storage and compute
• Consider Aurora Serverless for unpredictable database workloads
• Evaluate read replica needs based on actual read/write patterns

Lambda Function Tuning:

• Adjust memory allocations based on execution duration and actual memory consumption
• Review timeout settings to prevent unnecessary costs from hung functions

Implement auto-scaling policies for variable workloads

Auto-scaling becomes your best friend when dealing with unpredictable traffic patterns. Unlike AWS Amplify’s built-in scaling, native AWS services give you granular control over when and how your infrastructure scales.

Application Load Balancer Auto Scaling:
Create target tracking policies that maintain optimal performance metrics:

• CPU utilization between 50-70%
• Request count per target
• Average response time thresholds

Database Auto Scaling:

• Enable Aurora Auto Scaling for read replicas during peak traffic
• Set up DynamoDB auto-scaling for read/write capacity units
• Configure RDS Proxy for connection pooling during traffic spikes

Predictive Scaling:
Set up predictive auto-scaling for workloads with known patterns. If your application sees traffic spikes every Monday morning or during specific business hours, predictive scaling pre-provisions resources before demand hits.

Configure cost monitoring and budget alerts

Cost visibility often disappears during AWS migration projects. Setting up comprehensive monitoring prevents budget surprises and helps identify optimization opportunities quickly.

Create multiple budget categories:

• Service-level budgets for EC2, RDS, Lambda, and S3
• Environment-based budgets separating production, staging, and development costs
• Team or project budgets for cost accountability

CloudWatch Billing Alerts:

• Set up daily spend alerts at 50%, 80%, and 100% of monthly budget
• Create anomaly detection for unusual spending patterns
• Configure SNS notifications to reach finance and engineering teams

Cost Explorer Integration:

• Schedule weekly cost reports showing service breakdowns
• Track month-over-month spending trends
• Identify the most expensive resources and optimization opportunities

Leverage Reserved Instances and Savings Plans for predictable workloads

Once your AWS migration stabilizes and you understand baseline resource needs, Reserved Instances and Savings Plans can cut costs by 30-70% compared to on-demand pricing.

Reserved Instance Strategy:

• Start with 1-year terms for flexibility during the first year post-migration
• Focus on “base load” instances that run 24/7
• Use Convertible RIs if you might need to change instance types

Compute Savings Plans:

• Ideal for mixed workloads using EC2, Lambda, and Fargate
• Provide flexibility to change instance families, sizes, and regions
• Cover up to 66% discount for consistent compute usage

Implementation Timeline:

Month	Action	Expected Savings
1-3	Monitor usage patterns	0%
4-6	Purchase 1-year Convertible RIs	20-40%
7-12	Optimize and adjust commitments	30-50%
12+	Move to 3-year terms for stable workloads	40-70%

Remember that Reserved Instances and Savings Plans require commitment. Start conservatively with about 60-70% of your baseline usage to avoid over-committing while your architecture continues evolving post-migration.

Establish Monitoring and Maintenance Procedures

Set up comprehensive application and infrastructure monitoring

Monitoring your migrated AWS infrastructure requires a multi-layered approach that goes far beyond basic uptime checks. CloudWatch becomes your primary monitoring hub, collecting metrics from all your native AWS services. Start by configuring detailed monitoring for EC2 instances, RDS databases, Lambda functions, and API Gateway endpoints. Set up custom dashboards that display key performance indicators relevant to your specific application architecture.

Application Performance Monitoring (APM) tools like AWS X-Ray provide deep insights into request flows and bottlenecks across your distributed services. Configure X-Ray tracing for your Lambda functions and API Gateway to track performance degradation patterns. This becomes especially valuable after your AWS Amplify migration when you need to validate that performance hasn’t regressed.

Create meaningful alerts that trigger on business-critical metrics, not just technical thresholds. For example, alert on cart abandonment rates or login failure spikes rather than just CPU usage. Use SNS to route these alerts to appropriate team channels, ensuring the right people get notified about issues they can actually fix.

Log aggregation through CloudWatch Logs or Amazon OpenSearch Service centralizes troubleshooting efforts. Structure your logs with consistent formatting and implement log rotation policies to manage costs effectively.

Create automated backup and disaster recovery processes

Automated backup strategies protect against both human error and infrastructure failures. RDS automated backups with point-in-time recovery give you flexibility to restore to any moment within your retention period. Configure backup windows during low-traffic periods to minimize performance impact.

DynamoDB point-in-time recovery and on-demand backups provide granular data protection options. Enable continuous backups for critical tables and schedule periodic full backups to S3 for long-term archival. Cross-region replication adds an extra layer of protection for mission-critical data.

S3 Cross-Region Replication automatically copies objects to different AWS regions, protecting against regional outages. Implement lifecycle policies to transition older backups to cheaper storage classes like Glacier or Deep Archive.

Create and regularly test disaster recovery runbooks. Document exact steps for restoring services in priority order, including database restoration procedures and DNS failover processes. Practice these procedures quarterly with your entire team to identify gaps and reduce recovery time objectives.

AWS Backup provides centralized backup management across multiple services, simplifying compliance and governance requirements. Configure backup policies that automatically include new resources as your infrastructure grows.

Implement CI/CD pipelines for ongoing deployments

CodePipeline orchestrates your deployment workflow from source code changes through production releases. Create separate pipelines for different components of your native AWS services architecture, allowing teams to deploy independently while maintaining overall system stability.

CodeBuild compiles your applications and runs automated tests in isolated environments. Configure build specifications that include unit tests, integration tests, and security scans. Store build artifacts in S3 with versioning enabled for easy rollback capabilities.

CodeDeploy manages blue-green deployments for EC2 instances and Lambda functions, reducing deployment risks through automated rollback triggers. Configure deployment configurations that match your risk tolerance and traffic patterns.

Integrate infrastructure as code through CloudFormation or CDK templates in your pipeline. This ensures environment consistency and makes infrastructure changes traceable through version control. Use CloudFormation stack policies to prevent accidental deletion of critical resources.

Implement approval gates for production deployments, requiring manual sign-off from designated team members. Use CodeStar Notifications to keep stakeholders informed about deployment status without overwhelming them with technical details.

Document operational procedures for your team

Create comprehensive runbooks that cover common operational scenarios your team will encounter. Document step-by-step procedures for scaling resources during traffic spikes, investigating performance issues, and responding to security incidents. Include screenshots and CLI commands to reduce ambiguity.

Maintain an architecture decision record that explains why specific AWS migration best practices were chosen over alternatives. This historical context helps future team members understand the reasoning behind current configurations and avoid repeating past mistakes.

Develop escalation procedures that clearly define when to engage different team members or external support. Include contact information, expertise areas, and availability schedules to ensure rapid response during critical incidents.

Create onboarding documentation for new team members, explaining how your migrated architecture differs from the original Amplify setup. Include hands-on exercises that let them practice common tasks in a safe environment before working on production systems.

Document all custom configurations, including security group rules, IAM policies, and resource naming conventions. This documentation becomes invaluable during audits or when planning future architecture changes. Keep these documents in version control alongside your application code to ensure they stay current.

Moving from AWS Amplify to native AWS services is a big step, but breaking it down into manageable phases makes the transition smooth and successful. Start by understanding what you currently have, then carefully map each Amplify component to its native AWS counterpart. Your migration strategy and timeline should prioritize critical systems while maintaining security standards throughout the process.

The real win comes after migration when you can fine-tune performance and cut costs using the full power of native AWS services. Don’t forget to set up proper monitoring and maintenance routines – they’re what keep your new architecture running smoothly long-term. Take your time with each step, test thoroughly, and you’ll end up with a more flexible, cost-effective system that gives you complete control over your cloud infrastructure.

The post Enterprise Guide to Migrating AWS Amplify Applications to Native AWS Services first appeared on Business Compass LLC.

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