Migrations Architecture
Our migration architecture follows a phased, validated approach to move legacy workloads to modern, cloud-native platforms with zero disruption.
Legacy Assessment
Discovery and dependency mapping identify risks and prioritize workloads into migration waves.
Validation & Wave Migration
Integrations are rewired and tested in parallel runs, ensuring performance matches legacy baselines before cutover.
Modern Target
Decommissioning legacy systems as workloads stabilize on cloud iPaaS, microservices, and event-driven infrastructure.
Our Migrations practice moves you from legacy platforms to modern, cloud-native architectures through a structured, phased approach that eliminates risk and maintains business continuity. We migrate from on-premise ESBs like webMethods and TIBCO, legacy middleware over MQ, JMS, and SOAP, legacy databases including DB2, Oracle, and MSSQL, legacy applications on mainframes and COBOL, point-to-point and batch integrations, and file and FTP-based flows — all carrying high TCO and technical debt. The migration follows five phases: Assess (discovery, inventory, dependency mapping, and risk and complexity scoring in weeks 1–4), Design (target architecture, migration strategy, and data mapping and cutover planning in weeks 5–8), Migrate (application and integration migration, data migration and validation, and parallel run and testing in phased waves across weeks 9–36), Validate (integration testing, performance benchmarks, and cutover rehearsals in weeks 37–44), and Go Live (production cutover, legacy decommission, and hypercare and support in weeks 45–52) — using webMethods, Kafka, AWS, Azure, and Kubernetes as migration tooling. The target modern platform includes cloud iPaaS for managed integration, API gateways for modern API management, Kafka and event mesh for event streaming, containerized microservices, managed cloud database services, and full observability through Grafana, ELK, and traces — all running on Kubernetes, Docker, Terraform, GitOps and CI/CD, cloud-native infrastructure, and enterprise security. The result is 40% lower TCO, cycle times reduced from days to hours, zero critical incidents during migration, and full decommission of legacy systems.
Our Approach
We plan and execute migrations from legacy platforms — on-premise ESBs like webMethods and TIBCO, middleware over MQ, JMS, and SOAP, legacy databases like DB2, Oracle, and MSSQL, mainframe applications, point-to-point integrations, and file and FTP-based flows — to modern, cloud-native architectures through a structured five-phase approach. We assess your current state with discovery, inventory, dependency mapping, and risk scoring, then design the target architecture with a clear migration strategy, data mapping, and cutover plan. Migration runs in phased waves with parallel runs and testing so business continuity is maintained at every stage, followed by rigorous validation through integration testing, performance benchmarks, and cutover rehearsals before go-live.
The target platform includes cloud iPaaS for managed integration, modern API gateways, Kafka and event mesh for event streaming, containerized microservices, managed cloud databases, and full observability through Grafana, ELK, and distributed traces — all running on Kubernetes, Docker, Terraform, and GitOps with CI/CD. After production cutover, we provide hypercare support and a clear path to decommissioning legacy systems — delivering 40% lower TCO, cycle times reduced from days to hours, and zero critical incidents through the transition.
Key Capabilities
Migration Strategy & Planning
Assess your legacy landscape — on-premise ESBs like webMethods and TIBCO, middleware over MQ, JMS, and SOAP, legacy databases like DB2, Oracle, and MSSQL, mainframe applications, point-to-point integrations, and file and FTP flows — with discovery, inventory, dependency mapping, and risk and complexity scoring to build a phased migration roadmap.
Target Platform Design
Design the modern target architecture — cloud iPaaS for managed integration, API gateways for modern API management, Kafka and event mesh for event streaming, containerized microservices, managed cloud database services, and full observability through Grafana, ELK, and distributed traces.
Application Migration
Migrate applications and middleware through lift-and-shift, refactoring, or replacement — moving from legacy ESBs, mainframes, and COBOL to cloud iPaaS, containerized microservices, modern API gateways, and Kafka event streaming on the target platform.
Data Migration
Extract, transform, and load data from legacy databases and file systems with full validation and reconciliation — mapping data across source and target schemas, running parallel comparisons, and ensuring zero data loss through every migration wave.
Integration Rewiring
Rewire integrations and APIs from legacy point-to-point and batch patterns to modern cloud iPaaS, API gateways, event streaming, and microservices — updating every connection so dependent systems continue to operate without disruption during and after migration.
Validation & Testing
Validate every migration wave with integration testing, performance benchmarks against production baselines, cutover rehearsals, and parallel run comparisons — ensuring the target platform meets or exceeds legacy system performance before any production traffic is switched.
Cutover & Rollback
Plan and execute production cutover with documented rollback procedures, followed by hypercare support and a clear path to decommissioning legacy systems — achieving zero critical incidents through the transition.
Infrastructure & Modernization
Deploy the target platform on modern, cloud-native infrastructure with Kubernetes, Docker, Terraform, GitOps and CI/CD, cloud-native security, and enterprise observability — replacing legacy infrastructure with a scalable, cost-efficient foundation that delivers 40% lower TCO.
How it Works
1. Discover Legacy
We begin by inventorying your entire legacy landscape — on-premise ESBs like webMethods and TIBCO, middleware over MQ, JMS, and SOAP, legacy databases including DB2, Oracle, and MSSQL, mainframe and COBOL applications, point-to-point and batch integrations, and file and FTP-based flows. Every component is cataloged with its dependencies mapped and a risk and complexity score assigned to prioritize what moves first.
2. Design Target
With the legacy inventory complete, we design the modern target architecture — cloud iPaaS for managed integration, API gateways for modern API management, Kafka and event mesh for event streaming, containerized microservices, managed cloud databases, and full observability through Grafana, ELK, and distributed traces. The migration strategy, data mapping, and cutover plan are defined so every wave has a clear scope and sequence.
3. Migrate in Waves
Migration runs in phased waves rather than a big bang. Applications, integrations, data, and APIs are moved to the target platform incrementally — with parallel runs keeping both legacy and modern systems active simultaneously so business operations continue uninterrupted. Each wave is self-contained: migrate, validate, stabilize, then proceed to the next wave.
4. Validate & Test
Before any production traffic switches, we run rigorous validation — integration testing across all migrated flows, performance benchmarks compared against legacy baselines, and full cutover rehearsals that simulate the production switch. Rollback procedures are documented and tested so the team can revert quickly if any wave encounters issues.
5. Go Live & Decommission
Production cutover is executed with the validated plan. Legacy systems are decommissioned once all dependent flows are confirmed stable on the target platform. Hypercare support runs through the transition period to catch and resolve any issues immediately — delivering zero critical incidents through the entire migration.
6. Operate Modern
With legacy systems retired, you operate on a modern, cloud-native platform — Kubernetes, Docker, Terraform, GitOps with CI/CD, and enterprise security as the foundation. Grafana and ELK provide full observability across all integration and application flows. The result is 40% lower TCO, cycle times reduced from days to hours, and a scalable platform ready for future growth.
Technology stack
Use Case
Scenario: A healthcare provider migrates legacy patient data and integration interfaces from on-premise EAI to a modern cloud-native architecture.
Outcome: Successfully migrated 100+ integrations with zero data loss, improved system reliability, and met stringent compliance requirements.
Frequently Asked Questions
We always recommend phased migration over big bang. Our approach runs in waves — each wave migrates a defined set of applications, integrations, data, and APIs to the target platform with parallel runs keeping both legacy and modern systems active simultaneously. Each wave is self-contained: migrate, validate, stabilize, then proceed. This means business operations continue uninterrupted and risk is contained to each wave rather than the entire platform.
We migrate from on-premise ESBs like webMethods and TIBCO, legacy middleware over MQ, JMS, and SOAP, legacy databases including DB2, Oracle, and MSSQL, mainframe and COBOL applications, point-to-point and batch integrations, and file and FTP-based flows. If your legacy system has high TCO, technical debt, or is blocking modernization, it's a migration candidate.
The target architecture includes cloud iPaaS for managed integration, modern API gateways, Kafka and event mesh for event streaming, containerized microservices, managed cloud database services, and full observability through Grafana, ELK, and distributed traces — all running on Kubernetes, Docker, Terraform, GitOps with CI/CD, cloud-native security, and enterprise infrastructure. The specific components are selected based on your existing investment, cloud strategy, and integration patterns.
Every migration wave goes through rigorous validation — integration testing across all migrated flows, performance benchmarks compared against legacy baselines, and full cutover rehearsals that simulate the production switch. Rollback procedures are documented and tested for every wave so the team can revert quickly if issues arise. Parallel runs keep legacy systems active until the target is confirmed stable, and hypercare support runs through the transition period.
A complete migration from legacy to modern platform typically runs over 12 months in five phases: Assess (weeks 1–4), Design (weeks 5–8), Migrate in phased waves (weeks 9–36), Validate (weeks 37–44), and Go Live with hypercare (weeks 45–52). Smaller migrations with fewer systems and dependencies can move faster. The phased approach means you start seeing value from early waves while later waves are still in progress.
Once all dependent flows are confirmed stable on the target platform and parallel runs are complete, legacy systems are formally decommissioned. We provide a clear decommission plan that covers shutting down legacy runtimes, archiving data and configurations, updating documentation, and confirming all dependent teams have transitioned. The goal is full legacy retirement — not an indefinite coexistence that doubles your operational overhead.
Our reference deployments achieve 40% lower TCO after migration — driven by eliminating legacy licensing and maintenance costs, reducing infrastructure footprint, and gaining operational efficiency from modern cloud-native platforms with auto-scaling, CI/CD, and centralized observability. We build a TCO comparison during the assessment phase so you have clear visibility into expected savings before committing to the full migration.
Yes — that's why parallel runs and integration rewiring are central to the approach. As each wave migrates applications and data to the target platform, we rewire the dependent integrations and APIs to point to the new systems. Legacy and modern systems run side by side during the transition, and every connection is tested and validated before the legacy endpoint is retired. Dependent teams and downstream consumers experience no disruption.