Datalex partnered with AWS in late 2025 to accelerate modernization of its critical airline retailing platform. By migrating from legacy Java and EJB architectures to Spring Boot and containerized microservices on AWS, they achieved rapid proof of concept alongside establishing a secure, observable, and incremental deployment pattern enhanced with agentic AI capabilities.
- Incremental modernization from EJB/Java 8 to Spring Boot/Java 21 accelerated in 3-day AWS workshop
- Adopted Amazon ECS and Kong API Gateway for seamless service routing and backward compatibility
- Integrated generative AI agents and enhanced observability to support future airline retail innovations
Infrastructure signal
Datalex’s modernization leverages AWS infrastructure components such as Amazon ECS for container orchestration and Kong API Gateway to facilitate protocol translation, enabling coexistence of legacy SOAP and modern REST services. This hybrid architecture allows for phased migration with minimal disruption to ongoing airline operations, optimizing resource allocation and cloud costs by containerizing critical business logic incrementally.
The architecture incorporates a business service proxy that dynamically routes requests between legacy and modern systems based on migration status. This design ensures backward compatibility and supports operational resilience. Additionally, integrating real-time observability tools improves system monitoring and fault detection, further enhancing reliability as the platform evolves.
Developer impact
The migration from a mature EJB and Java 8 environment to Spring Boot running on Java 21 modernizes the developer workflow by enabling use of contemporary frameworks, language features, and tooling. This shift reduces technical debt and accelerates feature delivery cycles, demonstrated by the rapid transformation achieved in just three days during the AWS Experience-Based Acceleration workshop.
The introduction of a secure CI/CD pipeline standardizes deployment and testing, promoting safer, more frequent releases without impacting production stability. Engineers now benefit from integrated generative AI tools, such as Kiro and AWS Transform Custom, which automate migration tasks and boost productivity by supporting code adaptation, testing, and maintenance activities.
What teams should watch
Teams responsible for platform evolution and operations should focus on the incremental migration pattern enabled by the business service proxy and container orchestration, ensuring that API-level compatibility is rigorously maintained. Monitoring the rollout of generative AI assistance will be critical to understand its impact on code quality and developer efficiency in complex system contexts.
Observability enhancements now provide deeper insight into both legacy and modern service components, enabling proactive incident management. It is important for product, engineering, and operations teams to coordinate closely during migration phases to prevent customer experience degradation, continuously validate service integrations, and refine cloud cost management strategies.