Chander Pal, Mohit Srivastava, Vibhore Kumar

ETLGenAIData

GenAI Powered Crew for Migrating Legacy ETL Pipelines

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genai-etl-crew-migrating-legacy-etl-pipelines

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Nov 1, 2024

GenAI Powered Crew for Migrating Legacy ETL Pipelines

Migrating ETL pipelines from legacy systems to modern, cloud-based frameworks is a complex but critical task for enterprises. Ensuring compatibility, transferring large amounts of data, and maintaining system reliability require a structured and efficient approach. To streamline this transition, we successfully deployed a team of Generative AI-powered agents—the ETL Crew—to automate and accelerate the migration process while incorporating human oversight at key stages.

Project Overview

The ETL Crew, built on the CrewAI framework, consists of three specialised AI agents:

Owl Lead (Tech Lead) – Oversees the migration and manages workflow coordination.

Owl Dev (Developer) – Translates and adapts the legacy ETL code into the modern framework.

Owl QA (Quality Assurance Engineer) – Conducts end-to-end (E2E) testing to ensure successful migration.

This AI-driven solution integrates with Jira for task management, GitHub for version control, Microsoft Teams for communication, and Langfuse for prompt management, debugging, and cost tracking.

Through this structured and automated approach, we successfully migrated legacy ETL pipelines to modern cloud-based frameworks while ensuring accuracy, efficiency, and cost-effectiveness.

ETL Crew Workflow and Execution

Step 1: Task Creation and Assignment

The migration process was initiated with Jira task creation, detailing the legacy ETL pipeline’s specifications.

The Owl Lead agent took ownership of the task, analyzing requirements, and setting up subtasks for Owl Dev and Owl QA.

Before execution, human reviewers validated the task setup.

Step 2: Code Migration and Development

Owl Dev retrieved the legacy ETL code from GitHub and, using CrewAI’s transformer-based models, translated it into the new environment (AWS Glue, Spark, or a modern ETL framework).

The system generated equivalent code and unit tests, which were automatically committed to GitHub for review.

Human oversight ensured compliance with organizational coding standards before final approvals.

Step 3: Rigorous E2E Testing for Quality Assurance

Owl QA executed end-to-end testing using validation datasets to confirm that the migrated pipeline replicated legacy performance.

Any discrepancies were flagged and sent back to Owl Dev for corrections.

Automated CI/CD pipelines (via GitHub Actions) facilitated iterative improvements and successful deployment.

Step 4: Final Review, Approval, and Deployment

Owl Lead conducted a final code review to verify compliance with industry best practices.

The human review phase provided additional assurance before merging the final pull request into production.

Upon completion, stakeholders received a summary of the migration via Microsoft Teams.

Video Demo: See the ETL Crew in Action

The accompanying video demonstrates the ETL Crew handling a complete migration, from task setup through E2E testing and final code merge. You’ll see each agent in action, along with the critical role of human validation at specific points in the workflow.

Results & Impact

The successful implementation of the ETL Crew delivered significant improvements in migration efficiency and cost reduction:

40% Faster Migration Time – AI-driven automation accelerated the end-to-end migration process.

25% Reduction in Costs – AI assistance minimized the need for extensive manual labor.

Seamless Transition to Cloud-Based ETL Pipelines – The structured migration approach ensured minimal disruption to business operations.

High Reliability and Accuracy – End-to-end testing and human validation at critical stages ensured error-free deployments.

Conclusion

The deployed ETL Crew successfully completed the migration of legacy ETL pipelines, ensuring a smooth transition to cloud-native architectures. By blending AI-driven automation with human oversight, we delivered a cost-effective, efficient, and reliable multi-agent migration solution. Organisations can now seamlessly and efficiently leverage this proven AI-powered framework to modernize their data infrastructure with confidence, unlocking new opportunities for scalability, productivity, and innovation. We have been able to leverage similar ETL crews at another customer to complete one of the biggest data migration projects in the ASEAN region.