Generative AI_ETL Modernization with AWS DevOps & Cloud Operations
About the Customer
The customer is one of the world’s leading healthcare organizations, delivering innovative solutions across pharmaceuticals, medical technologies, and healthcare services. Operating across multiple business units and global regions, the organization manages a vast ecosystem of enterprise applications, data platforms, and business-critical workflows that support research, manufacturing, supply chain, regulatory operations, and commercial functions.
As part of its enterprise modernization strategy, the customer initiated a program to transform legacy Extract, Transform, and Load (ETL) workflows into a scalable, cloud-native, AI-assisted modernization platform — accelerating migration from legacy ETL technologies to modern PySpark-based data processing while improving engineering productivity, operational governance, security, and long-term maintainability.
The Challenge
The customer’s enterprise data landscape consisted of numerous legacy ETL workflows supporting business-critical reporting, analytics, and operational processes across multiple functions. Many were built on legacy ETL platforms, making modernization a complex and resource-intensive effort.
Manual analysis of workflow logic, metadata extraction, dependency mapping, and code conversion demanded significant engineering effort while increasing the risk of inconsistencies and migration delays.
The organization required a modern solution capable of accelerating ETL modernization without compromising governance, security, or operational stability — one that could automatically extract metadata, identify technical lineage, generate PySpark-based transformation logic, and create supporting engineering artifacts while maintaining end-to-end traceability.
Beyond development automation, the platform needed enterprise-grade operational capabilities including centralized monitoring, secure secrets management, resilient Multi-AZ deployment, workload scalability, and governed execution of AI-assisted workflows — while integrating with existing enterprise systems and enforcing secure access controls.
The Solution
- AI-Assisted ETL Modernization — specialized generative AI agents analyze legacy ETL workflows, extract metadata and lineage, and automatically generate PySpark code and supporting engineering artifacts.
- Responsible AI Governance — Amazon Bedrock Guardrails and validation checkpoints enforce responsible AI usage before generated artifacts are released.
- Cloud-Native Architecture — a highly available, Multi-AZ platform on Amazon EKS with Amazon RDS for PostgreSQL, Redis with Celery orchestration, and a vector knowledge base for AI-assisted processing.
- DevOps Automation — standardized CI/CD using AWS CodePipeline, CodeBuild, CodeDeploy, Amazon ECR, and CloudFormation, with Infrastructure as Code for repeatable, consistent deployments.
- Cloud Operations Excellence — centralized monitoring with Amazon CloudWatch, secure credentials via AWS Secrets Manager, Kubernetes-based autoscaling, and Multi-AZ resilience for secure day-2 operations.
- Measurable Outcomes — accelerated modernization, a significant reduction in manual engineering effort, and estimated modernization cost savings of 40–60%.
Download the Case Study Document
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