Enterprise modernization is at a critical juncture. While transforming a single repository is manageable, scaling this process to 50, 100, or even 200 repositories presents a unique challenge. Beyond mere code transformation, organizations must also coordinate teams, capture knowledge, and ensure quality across their entire portfolio.
AWS Transform custom addresses these challenges with its bulk automation capabilities, enabling organizations to modernize efficiently. One notable success story involves a customer who reduced their modernization timeline from 12 weeks to just 2.5 weeks, achieving a 3-5x decrease in delivery time and a 10-20x reduction in total effort hours.
The Coordination Challenge
Enterprise architects often recount the difficulties of coordinating major modernization initiatives. For instance, a software company faced the daunting task of migrating a large legacy codebase. The actual code transformation took only days, but weeks were spent managing team coordination, ensuring consistency across various codebases, and handling dependencies.
This highlights the coordination problem inherent in enterprise modernization. As the number of repositories increases, so does the complexity and overhead associated with managing them. In fact, code transformation typically accounts for only about 30% of the total modernization effort, with the remaining 70% involving activities like testing, validation, documentation, and organizational coordination.
A New Approach with AWS Transform Custom
AWS Transform custom revolutionizes the modernization process by learning from each transformation execution. Instead of treating each repository as a separate project, it captures insights and applies them to future transformations.
Workflow Overview
The modernization process follows a structured workflow:
- Learn: Start with a few representative repositories, executing transformations interactively while providing feedback to the AI agent. This phase captures valuable context and enhances the transformation definition.
- Scale: Transition to non-interactive bulk execution, allowing the system to process multiple repositories overnight. It utilizes patterns learned during the pilot phase and validates transformations in real-time.
- Improve: After each bulk execution, review the insights captured, approve valuable learnings, and refine the transformation definition for future iterations.
Capturing Knowledge as an Asset
This approach not only accelerates the modernization process but also transforms how organizations capture and share knowledge. Transformation definitions become reusable organizational assets, preserving expertise that would otherwise be lost when developers move on.
Proven Results
Real-world applications of AWS Transform custom demonstrate significant productivity gains. For instance, a company migrating Control-M workflows to Apache Airflow completed the project in just 2.5 weeks, achieving a 100% success rate and improving edge case handling by 60% compared to previous methods.
Getting Started
AWS Transform custom supports various programming language upgrades and migrations, including Java, Python, and Node.js. It integrates seamlessly into existing development processes, allowing teams to leverage their standard CI/CD pipelines.
To initiate the transformation journey, organizations can utilize an open-source sample repository provided by AWS, which facilitates bulk execution across multiple repositories.
Conclusion
Modernizing enterprise code at scale requires more than just tools for code transformation. AWS Transform custom addresses the essential challenges of coordination, learning, and knowledge capture, allowing organizations to respond swiftly to modernization needs. By following the learn-scale-improve workflow, enterprises can achieve remarkable reductions in delivery timelines and effort hours, turning aspirations into tangible results.
