Migration To Aws Migration Playbook

Published by Bles Software, a custom software and AI company based in Yehud-Monoson, Israel, building web apps, AI agents and API integrations for clients in Israel, the US, the UK and the EU.

Modernizing your technology estate is no longer a discretionary initiative. It is a strategic prerequisite for agility, cost efficiency, security, and growth. This migration to AWS playbook is designed for business owners, entrepreneurs, company decision makers, agency owners, and AI innovators who require a clear, ROI-driven path to the cloud that balances speed with safety. It translates the complexity of cloud transformation into an actionable, executive-friendly plan with the technical substance your teams need to execute.

The goal is simple: reduce time-to-value while minimizing risk. The method is rigorous: an end-to-end program that covers business case development, readiness, landing zone engineering, application and data migration at scale, security and compliance, operations, cost governance, and post-migration optimization. Whether you are running ERP and core databases, digital commerce platforms, analytics and AI workloads, or agency-built client applications, the guidance that follows will help you structure a predictable journey and avoid the most common pitfalls.

Executive Orientation: Why Now, Why AWS, and What “Good” Looks Like

Enterprises and fast-scaling companies pursue migration to AWS for a combination of resilience, elasticity, access to modern services, and measurable financial outcomes. Done well, migrations deliver a step change in operational excellence: fewer outages, faster deployments, lower per-transaction costs, and improved security posture. AWS amplifies those advantages with the breadth of its services (compute, storage, AI/ML, data, analytics, integration, DevOps), the maturity of its partner ecosystem, and the discipline of its cost and security frameworks.

From an executive vantage point, “good” looks like a migration that is business-case backed, wave-planned, monitored with KPIs, and secured end-to-end. It avoids scope sprawl and technical debt by pairing near-term efficiencies with modernization where it matters. It anticipates change management, and it sets a foundation for data and AI strategies you can monetize. Ultimately, “good” means a time-bounded program that de-risks execution, lands material savings within the first two quarters after cutover, and equips your teams to operate efficiently in the cloud.

Playbook Overview: Phases, Outcomes, and Guardrails

A successful migration follows a series of gated phases, each with clear deliverables and exit criteria. Expect to iterate, but resist the temptation to shortcut foundational steps that reduce risk later. The phases are:

Throughout, treat security and compliance as non-negotiable design inputs. Treat cost management as a continuous practice. Treat change management and upskilling as critical path items, not side projects.

Build the Business Case: ROI, TCO, and Funding Levers

Executives approve migrations that pencil out. Anchor the decision with a defensible financial model and a set of operational outcomes that matter to your board and customers. A robust business case ties infrastructure and licensing savings to productivity gains, revenue enablement, and risk reduction. It should quantify:

Use AWS’s tools and programs to sharpen the model. The AWS Migration Acceleration Program (MAP) can offset professional services and platform costs for eligible workloads. AWS TCO calculators and Cost Explorer, coupled with a tagging strategy, provide the baseline and projections you need. Include sensitivity analyses and explicit assumptions for usage growth, peak patterns, and modernization investments. Align KPIs with the business case: for example, target a 25–35% infrastructure cost reduction within nine months, 50% faster release cycles, and a 40% reduction in high-severity incidents.

Readiness Assessment and Migration Strategy

A migration to AWS succeeds or fails on the strength of its discovery and planning. Begin with an inventory that is both technical and business-centric. Tools can assist—AWS Application Discovery Service, native agent-based discovery, or third-party platforms—but interviews and workshops remain essential to capture business criticality, seasonality, regulatory constraints, and undocumented dependencies.

Classify each application by its strategic value, technical complexity, and change appetite. The classic migration strategies are your palette: rehost to shift quickly and de-risk capacity constraints; replatform to capture managed service benefits without rewriting; refactor when business drivers or technical debt justify code change; repurchase when a SaaS alternative is compelling; retire unused services; retain where dependencies or constraints preclude movement in the near term. Avoid trying to refactor everything. Time-box modernization to where it creates clear value, such as moving brittle on-premises queues to Amazon SQS or converting a fragile reporting database to a managed Amazon Aurora or Amazon Redshift footprint.

Map dependencies across applications, data stores, and integration points. Dependency mapping guides wave planning and reduces the risk of breaking a hidden system during cutover. Document non-functional requirements early—RPO, RTO, latency, throughput, data residency, encryption, and auditing—because they drive architecture choices and service selection.

Engineering the AWS Landing Zone and Governance

Before you move workloads, engineer the foundation. A production-grade landing zone is a multi-account architecture with centralized governance that scales. Use AWS Organizations to segment environments by function—security, networking, shared services, and workload accounts—and apply Service Control Policies to enforce guardrails. AWS Control Tower can accelerate account provisioning with opinionated best practices. If you prefer a more custom approach, codify your landing zone with AWS CloudFormation or Terraform and version control.

Identity and access should follow least privilege from day one. Integrate corporate identity providers with AWS IAM Identity Center for single sign-on and role-based access. Define permission sets for personas—developers, operators, auditors—so people have only the access they need. Isolate break-glass accounts and implement MFA everywhere. Secrets should live in AWS Secrets Manager or AWS Systems Manager Parameter Store, never in code repositories.

Networking must anticipate growth and security segmentation. Build Amazon VPCs with clearly separated public and private subnets across multiple Availability Zones. Use AWS Transit Gateway to manage connectivity between VPCs at scale and simplify on-premises connectivity. For private, consistent network paths, establish AWS Direct Connect; for rapid initial connectivity, use site-to-site VPN. Plan egress patterns to control external calls and costs, and centralize inbound access with Application Load Balancer or Network Load Balancer as appropriate. Design DNS intentionally—Amazon Route 53 private hosted zones with Route 53 Resolver endpoints bridge hybrid name resolution cleanly.

Centralize logging and observability. Enable AWS CloudTrail across all accounts with organization trails. Aggregate Amazon VPC Flow Logs, application logs, and service logs to a dedicated logging account with Amazon S3 buckets configured for immutable retention and lifecycle policies. Layer in AWS Config for configuration state tracking and compliance checks, Security Hub for posture visibility, and Amazon Detective for investigation support. These pillars enforce accountability and accelerate audits.

Integration Patterns That Survive Migration

Applications connect to each other and to partners in diverse ways. Your migration plan must respect and evolve those patterns. Message-oriented systems benefit from managed queues and topics—Amazon SQS and Amazon SNS—to decouple producers and consumers. For event-centric architectures, Amazon EventBridge eliminates brittle cron jobs and cross-service polling, enabling reliable fan-out and auditability. Data ingestion at scale is well-served by Amazon Kinesis for streaming use cases or AWS Database Migration Service (DMS) for change data capture during database moves. APIs remain a backbone; Amazon API Gateway front-ends microservices securely, while AWS AppSync can simplify real-time and GraphQL needs. For legacy file transfers, preserve expected behaviors with Amazon S3 and SFTP via AWS Transfer Family.

The guiding principle is to reduce synchronous coupling and network chatiness. As you move systems, replace point-to-point connections with managed intermediaries. This improves reliability during phased cutovers and makes future modernization cheaper.

Data Migration Strategy: Databases, Files, and Analytics

Data is the headline risk in most migrations. Treat it with a dedicated plan. Categorize sources (relational, NoSQL, files, object stores, analytics warehouses) and target states. For transactional databases, pair AWS Schema Conversion Tool with AWS DMS to simplify conversion and replication. Homogeneous migrations—such as Microsoft SQL Server to Amazon RDS for SQL Server—tend to be straightforward; heterogeneous migrations—like Oracle to Amazon Aurora PostgreSQL—require careful assessment of data types, stored procedures, and performance tuning. Plan dual-running with CDC to minimize downtime, and test data fidelity with validation scripts and sampling that the business verifies.

Large file repositories and content libraries migrate effectively to Amazon S3 using multi-part uploads, AWS DataSync for accelerated transfer with integrity checks, or AWS Snowball for petabyte-scale offline movement where network constraints exist. Keep an eye on namespace immutability and ACL translation. Frame analytics modernization as part of the program: centralize raw, curated, and consumer data in an Amazon S3-based lake with AWS Glue for cataloging and transforms. Serve derived analytics with Amazon Redshift, Amazon Athena, or Amazon OpenSearch Service depending on the use case. For low-latency operational stores, align workloads to Amazon DynamoDB or ElastiCache. If regulatory constraints require, exploit S3 Object Lock for immutability and configure Glacier tiers for cost-efficient archival.

Define RPO and RTO per data domain and ensure the migration method aligns. Critical systems may justify dual-write or blue/green strategies to reduce risk. Less critical stores can tolerate scheduled cutovers. Encrypt in transit and at rest throughout with AWS Key Management Service, and be explicit about key ownership and rotation policies.

Application Migration at Scale: Waves, Factories, and Runbooks

The difference between a migration that drifts and one that delivers is a factory mindset. Wave-based migration groups applications by dependency and business priority, then drives each wave through a consistent pipeline: environment build, data migration, application deployment, test, cutover, stabilization. Automation is your ally. AWS Application Migration Service (MGN) handles lift-and-shift server replication and orchestration. Infrastructure as code stands up target environments repeatedly. CI/CD pipelines deploy binaries predictably. Runbooks ensure that tests and operational checks do not vary from one application to another.

Not every workload should rehost as-is. Make strategically bounded improvements during the move where the payoff is immediate. Typical examples include externalizing session state so instances can scale horizontally, migrating from self-managed databases to Amazon RDS or Amazon Aurora, replacing self-managed reverse proxies with Application Load Balancer, or introducing containerization for services already loosely coupled. Amazon ECS or Amazon EKS provide a robust home for containerized workloads. Serverless patterns—AWS Lambda for discrete functions, AWS Step Functions for orchestration—remove operational burden for spiky or event-driven workloads.

For agencies and product companies, multi-tenant architectures and CI/CD for client environments deserve special attention. Automate environment provisioning with template-driven IaC, standardize observability, and enforce per-tenant isolation in multi-account or per-namespace designs. For AI innovators, plan GPU-backed compute with Amazon EC2 P- or G-instances, managed ML with Amazon SageMaker, and data pipelines that avoid bottlenecks during training runs. In all cases, design for horizontal scalability and budget guardrails.

Security and Compliance by Design

Security is not a phase. Treat it as a thread that runs through everything. The AWS shared responsibility model puts you in charge of securing what you build in the cloud, while AWS secures the underlying infrastructure. Build security controls into the landing zone and migration factory so they are automatically applied to each new workload.

Identity and access management starts with centralized SSO and role-based access. Implement least privilege policies with IAM, and enforce SCPs to prevent risky actions like public S3 bucket creation. Encrypt all data at rest with KMS CMKs where you require key control, and enforce TLS 1.2+ for data in transit. Deploy Web Application Firewall (AWS WAF) and AWS Shield for internet-facing applications. Isolate workloads with security groups and network ACLs that default to deny. Manage secrets programmatically and rotate credentials on set schedules.

Compliance is achievable in the cloud with the right evidence trail. AWS Config, CloudTrail, and Security Hub together provide continuous compliance monitoring and centralized findings. Amazon Macie helps discover and protect sensitive data in S3. If you are operating in regulated markets—HIPAA, PCI DSS, SOC 2, ISO 27001, or government-grade controls—map controls to AWS managed services and document shared responsibilities with your auditors. For data residency, choose AWS Regions aligned to your obligations and enforce region deny guardrails where required. Plan incident response with playbooks that integrate Amazon Detective for root cause analysis and AWS Systems Manager for orchestrated remediation.

Operations and Observability: Running What You Move

Your post-migration operating model is as important as the move itself. Adopting SRE principles can codify reliability goals and drive continuous improvement. For observability, start with metrics, logs, and traces that matter to the business. Amazon CloudWatch provides native metrics and alerts; CloudWatch Logs and log subscriptions centralize application logs. AWS X-Ray or OpenTelemetry-compatible tracing expose bottlenecks in distributed systems. Aggregate dashboards and alert routing should reflect ownership: alerts flow to teams that can act, with well-defined runbooks.

Operations must include patching and backup by default. Leverage AWS Systems Manager Patch Manager for OS patches and AWS Backup to standardize and audit backup policies across EBS, RDS, DynamoDB, and others. Test restores regularly, not just backups. For disaster recovery, articulate strategies per workload: backup and restore for lower criticality, pilot light or warm standby for mid-tier, and active/active for the top tier, with Amazon Route 53 health checks for automated failover. Validate RPO and RTO in exercises so stakeholders trust the numbers.

Automate drift detection and configuration compliance. Use Infrastructure as Code as the single source of truth. Change management should be lightweight but auditable, with approvals aligned to risk levels and enforced through pipelines rather than email.

Cost Governance and FinOps: Proactive, Not Reactive

Cloud cost is a design parameter. Build accountability and transparency into the program from the outset. Tags and account hierarchies are the backbone of showback and chargeback. Enforce minimum tag sets—application, owner, environment, cost center—and automate tag compliance. Use AWS Cost Explorer for visibility, AWS Budgets for guardrails, and anomaly detection to catch deviations early.

Rightsize aggressively after cutover. Move from older instance families to Graviton-based instances where supported to harvest immediate savings and performance gains. Adopt gp3 volumes for EBS to decouple performance and cost, and employ S3 Intelligent-Tiering or lifecycle policies to reduce storage spend automatically. For steady-state compute, purchase Savings Plans or Reserved Instances with a pragmatic commitment horizon based on usage patterns; for flexible batch or development workloads, leverage Spot Instances. Autoscaling should be the norm, not the exception.

Tie cost to outcomes. Measure unit economics that matter—cost per order, cost per API request, cost per inference for AI workloads—and share them widely. Where you modernize databases, quantify the impact of managed services on license and ops costs. Where you adopt serverless, surface the running cost differences so teams understand usage patterns. FinOps is a cross-functional practice; treat it as part of operating the cloud, not a quarterly clean-up.

AI and Data Modernization During Migration

For AI innovators and data-driven companies, migration is the on-ramp to modern model development and production-scale inference. Centralize data into a well-governed lake on Amazon S3 with clear zones—raw, refined, and published—cataloged by AWS Glue. Create repeatable pipelines with AWS Step Functions, AWS Glue, and Amazon EMR or AWS Glue for Spark. Expose analytics with Amazon Redshift and Amazon Athena as appropriate. Build feature pipelines and registries so data scientists have reliable inputs.

Select the right compute for ML training and inference. Amazon SageMaker streamlines the full lifecycle—data prep, training, tuning, deployment, and monitoring—and supports managed spot training for cost reduction. For highly customized frameworks or multi-tenant SaaS models, run GPU-enabled training on EC2 with distributed training strategies and sharded datasets. For generative AI, evaluate Amazon Bedrock for access to foundation models with enterprise controls, and plan for prompt management, guardrails, and cost monitoring per use case. Productionize models with CI/CD for ML (MLOps) and build observability into inference endpoints: latency, error rates, drift, and per-request costs. Design data privacy and access controls carefully—fine-grained S3 IAM policies, KMS encryption, and private VPC endpoints—so experimental work never exposes sensitive data improperly.

Timeline Templates: From Pilot to Enterprise Rollout

While each organization’s pace varies, the following templates reflect practical timelines for the majority of migrations. The key is to time-box phases, keep exit criteria objective, and de-risk through pilots.

A 90-day pilot targets one or two representative applications and their data stores. Spend the first three weeks on readiness and landing zone hardening, including identity integration, network connectivity, and baseline guardrails. Weeks four through nine focus on data migration setup—DMS configuration and initial replication—application rehosting or replatforming, and pipeline build-out. The final three weeks perform user acceptance testing, performance checks, and cutover in a planned low-traffic window. Success criteria include zero major incidents, performance within 10% of baseline or improved, and post-cutover rightsizing that confirms projected savings.

A 6–9 month wave program tackles 20–60% of your portfolio. Early months emphasize discovery completion, application dependency mapping, and wave planning. The migration factory spins up, using standardized patterns and automation to execute two to four waves in parallel, each with their own cutover windows coordinated with business calendars. The program includes a center of excellence that codifies lessons learned into updated runbooks and IaC modules. Midway, revisit the business case with real data and adjust. The final months concentrate on the highest-value modernizations and consolidation of on-premises capacity to unlock larger savings.

A 12–18 month enterprise migration encompasses complex, deeply integrated systems—ERP, data warehouses, real-time processing, and global footprints. It typically includes hybrid modes for a time—Direct Connect, AWS Outposts for local processing if latency demands it, and VMware Cloud on AWS as a bridge for particularly sticky workloads. The program includes a formal governance board, compliance oversight, executive steering, and vendor management. Expect to run full DR tests and perhaps an active/active cutover for mission-critical systems. The payoff is not only cost reduction but an architectural position from which you can build faster than competitors.

Risks and Mitigations

Every migration comes with risks. The playbook addresses them by design. Data loss or corruption risk is minimized through CDC replication, validation scripts, and shadow reads during dual-run. Performance regressions are mitigated through performance testing that mirrors real-world loads and right-sizing based on CloudWatch and APM data rather than static guesses. Security drift is controlled via automation—SCPs, AWS Config rules, and pre-approved IaC modules—that prevent misconfigurations from landing in production. Scope creep is managed by a change control process that channels new requirements into future waves rather than derailing current ones. Human factors—knowledge gaps and resistance—are addressed through training, clear role definitions, and a culture that measures outcomes, not heroics.

Change Management and Skills Uplift

Migration to AWS is a people program as much as a technical one. Define an operating model that clarifies who builds, who operates, and who governs. Establish a Cloud Center of Excellence to curate patterns, train teams, and drive consistency. Invest in certifications and hands-on labs that map to your service choices—networking, security, databases, and containers. Align incentives so teams see cloud adoption as the path to autonomy and impact, not just another set of rules. Communicate the business case and celebrate measured wins—cutover successes, cost optimizations, performance gains—to build momentum.

KPIs and Success Metrics

Track a blend of technical and business KPIs. Uptime and SLA adherence remain table stakes. Mean time to detect and resolve incidents should trend down. Deployment frequency and lead time should trend up as pipelines replace manual steps. From a financial perspective, watch infrastructure cost as a share of revenue, cloud spend variance against budget, and savings captured by rightsizing and commitments. Monitor unit economics relevant to your domain. For compliance, track control coverage and audit issues closed. For AI workloads, monitor per-inference costs and model performance drift. Publish these metrics monthly to your steering committee and quarterly to your executive leadership.

Putting It All Together: A Pragmatic Path to Value

A migration to AWS that adheres to this playbook delivers predictable outcomes because it balances rigor with pragmatism. It starts with a business case, not a tool. It prioritizes a secure, automated landing zone. It moves quickly where risk is low, and carefully where risk is high. It bakes cost and security into the DNA of the program. It upgrades integration patterns so future changes are easier. It readies your organization for a world where data and AI are competitive levers, not side projects. The result is an environment where your teams can build, ship, and scale with confidence.

If you are preparing for your own move, use this as the spine of your plan. Tailor the details to your constraints, but resist skipping the fundamentals. With the right foundation, you will find the first wave validates the model, the second wave accelerates, and by the third, migration is simply how you run change. That is when the cloud stops being a project and becomes a capability.

Frequently Used AWS Services in This Playbook

While your exact bill of materials will vary, certain services recur because they reduce risk and accelerate outcomes. Identity and access revolves around AWS IAM Identity Center, IAM, and Secrets Manager. Governance and account management rely on AWS Organizations, Control Tower, and Service Control Policies. Networking and traffic management anchor on Amazon VPC, Transit Gateway, Direct Connect, VPN, and Route 53. Migration tooling includes AWS Application Migration Service, AWS DMS, and AWS DataSync. Storage and databases commonly include Amazon S3, Amazon EBS, Amazon EFS, Amazon RDS and Aurora, DynamoDB, and Amazon Redshift. Compute and orchestration span Amazon EC2, AWS Autoscaling, AWS Lambda, Amazon ECS/EKS, and Application Load Balancer. Observability and security feature CloudWatch, CloudTrail, X-Ray, AWS Config, Security Hub, Detective, Macie, AWS WAF, and AWS Shield. DevOps and automation utilize CodePipeline, CodeBuild, CodeDeploy, CloudFormation, and Systems Manager. Disaster recovery and backup center on AWS Backup and AWS Elastic Disaster Recovery.

Sample Cutover Plan: From Dry Run to Go-Live

A sound cutover plan is rehearsed, reversible within a defined window, and business-aligned. In the week prior, freeze non-essential changes, confirm monitoring and alerting, and complete a dry run that includes data replication catch-up, application deployment, DNS change simulation, and rollback testing. On the day, quiesce the source where necessary, allow replication to converge, switch application endpoints behind a load balancer, and update DNS with TTLs pre-tuned for rapid propagation. Keep the old environment warm for a planned rollback window. Post-cutover, execute validation scripts, run synthetic and user smoke tests, and monitor error rates and latency closely. Only after a stable period do you decommission the old resources, following a documented checklist to avoid stranding dependencies.

Sustainability Considerations

Cloud migration is an opportunity to reduce your environmental footprint. AWS’s shared data centers achieve higher utilization and renewable energy usage than typical private facilities. At the architecture level, right-sizing, autoscaling, and serverless models avoid idle resources. Graviton processors deliver better performance per watt. S3 lifecycle policies gently move data to lower-energy storage classes. Surface these choices in your ESG reporting to reflect the broader benefits of your migration program.

Common Anti-Patterns to Avoid

Three anti-patterns frequently derail timelines and benefits. First, treating the landing zone as a “nice to have” leads to a fragile, insecure foundation that will need rework. Second, attempting to refactor every application during migration exhausts teams and delays value; instead, limit modernization to clear wins and reserve deeper changes for post-migration sprints. Third, neglecting cost governance and tagging results in budget surprises and reactive cuts; successful programs make cost visible and controllable from day one. Avoiding these pitfalls protects your credibility and your timeline.

What Success Feels Like After 90 Days, 6 Months, and 12 Months

By day 90, you should have a hardened landing zone, a completed pilot with at least one production workload migrated, and preliminary cost data that aligns with your business case. Operationally, your teams can deploy to AWS with pipelines and have clear runbooks for on-call. By month six, the migration factory is running multiple waves, savings are materializing through rightsizing and early commitments, and at least one modernization has proven the performance and agility upside. By month twelve, on-premises dependencies are reduced materially, a majority of tier-two applications are migrated, and your DR posture is measurably improved. Your teams spend more time building features than maintaining infrastructure, and the migration to AWS has become the standard path for change.

FAQ

How long does a typical migration to AWS take, and what drives the timeline?

Timelines vary by portfolio size, complexity, and change appetite. A focused pilot can complete in 90 days. A mid-sized program often runs 6–9 months to move the bulk of tier-two applications. Enterprise migrations with ERP, data warehouses, and global footprints may take 12–18 months. The drivers are discovery completeness, the number of integration dependencies, the extent of modernization you choose to do during the move, and organizational readiness. Investing in a strong landing zone, wave planning, and automation compresses timelines without increasing risk.

What is the best first step if we are just starting our migration to AWS?

Begin with a business outcome workshop and a readiness assessment. Clarify why you are moving—cost, agility, resilience, data and AI—and how success will be measured. Inventory your applications and data, map dependencies, and define non-functional requirements. In parallel, design and build your landing zone for identity, networking, security, and cost governance. Then select one or two representative workloads for a 90-day pilot to validate patterns, tooling, and the business case before scaling.

How do we ensure security and compliance during and after the migration?

Bake security into the foundation and the factory. Use AWS Organizations and Service Control Policies for guardrails, integrate SSO and enforce least privilege with IAM, encrypt data with KMS, and centralize logging with CloudTrail and AWS Config. Deploy AWS Security Hub, Macie, and Detective for continuous posture management and investigation support. Map your regulatory requirements to AWS services and document shared responsibilities with auditors. Automate controls through IaC and pipelines so every workload inherits security by default.

Which migration strategy should we use for our applications?

The right strategy depends on business value, technical complexity, and constraints. Rehost when speed is paramount and refactoring has low ROI. Replatform when managed services can deliver immediate operational and cost benefits—moving databases to RDS or Aurora is a common pattern. Refactor for applications where elasticity, performance, or feature velocity justifies code changes—often a subset of services, not entire systems. Consider repurchasing to SaaS where it offloads non-differentiating workloads. Retire what you do not need and retain a few systems temporarily if hard constraints exist. Treat strategies as a portfolio decision, not a one-size-fits-all choice.

How do we control and predict costs during migration to AWS?

Implement tagging and account structure from day one for visibility. Use AWS Budgets and anomaly detection to set guardrails. Rightsize aggressively after cutover using CloudWatch data, adopt Graviton instances where compatible, and move storage to cost-effective classes. For steady usage, purchase Savings Plans or Reserved Instances with prudent terms; use Spot Instances for flexible workloads. Track unit economics like cost per transaction. Revisit the business case quarterly with live data and adjust commitments as patterns stabilize.

What connectivity should we choose between on-premises and AWS?

It depends on latency, bandwidth, and reliability needs. For quick starts and moderate bandwidth, establish site-to-site VPNs. For predictable, high-throughput workloads or consistent hybrid operations, use AWS Direct Connect, potentially with redundant links for resilience. Architect your VPCs with AWS Transit Gateway to simplify routing and scale connectivity as you add accounts and regions. Align DNS with Route 53 Resolver endpoints for hybrid name resolution. Consider AWS Outposts for ultra-low-latency, on-premises compute that integrates with AWS if your use case demands it.

How do we migrate large databases with minimal downtime?

Plan for continuous replication and rehearsals. Use AWS DMS to set up full load plus change data capture, validate schema conversions with AWS Schema Conversion Tool for heterogeneous migrations, and run parallel environments to test application behavior against the target database. Schedule a brief cutover window where writes are paused or dual-written, allow replication to catch up, flip application endpoints, and keep rollback options warm. Validate data integrity post-cutover with automated and business-led checks before decommissioning the source.

When should we modernize to containers or serverless versus lifting and shifting?

Modernize during migration when the investment is small and the payoff immediate—externalizing session state, moving to managed databases, adopting load balancers, or containerizing services that already build cleanly. Reserve deeper refactors or domain re-architecture for post-migration sprints once your team is stable on AWS and you have baseline performance and cost data. Containers (ECS/EKS) are a good fit when you have many services with similar operational needs. Serverless (Lambda, Step Functions) shines for event-driven and sporadic workloads where you want to minimize operational overhead.

By following this playbook, you will conduct a migration to AWS that is defensible to executives, actionable for engineers, and valuable to customers. The path is proven, the risks are known and manageable, and the opportunity—faster innovation at lower cost with stronger security—is within reach.

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