General
Harry's – Enterprise Data Platform Modernization on AWS Serverless
Industry: General
Executive Summary
Harry's shifted legacy data systems (Redshift, EMR, Airflow) to AWS serverless services—Redshift Serverless, EMR Serverless, and MWAA cutting infrastructure overhead, improving pipeline performance and resiliency, reducing costs, and supporting rapid onboarding across brands and retail channels. Harry's modernization represents a textbook example of serverless adoption for data platforms. The transformation significantly improved scalability, reliability, developer productivity, and cost efficiency—while laying a strong foundation for future AI-driven analytics and ingestion automation. This project meets AWS Modernization Competency requirements in the areas of infrastructure modernization, serverless migration, operational excellence, and cost optimization.
Client: Harry's shifted legacy data systems (Redshift, EMR, Airflow) to AWS serverless services—Redshift Serverless, EMR Serverless, and MWAA cutting infrastructure overhead, improving pip
Background
Harry's shifted legacy data systems (Redshift, EMR, Airflow) to AWS serverless services—Redshift Serverless, EMR Serverless, and MWAA cutting infrastructure overhead, improving pipeline performance and resiliency, reducing costs, and supporting rapid onboarding across brands and retail channels. Harry's modernization represents a textbook example of serverless adoption for data platforms. The transformation significantly improved scalability, reliability, developer productivity, and cost efficiency—while laying a strong foundation for future AI-driven analytics and ingestion automation. This project meets AWS Modernization Competency requirements in the areas of infrastructure modernization, serverless migration, operational excellence, and cost optimization.
Challenge
Harry's shifted legacy data systems (Redshift, EMR, Airflow) to AWS serverless services—Redshift Serverless, EMR Serverless, and MWAA cutting infrastructure overhead, improving pipeline performance and resiliency, reducing costs, and supporting rapid onboarding across brands and retail channels. Harry's modernization represents a textbook example of serverless adoption for data platforms. The transformation significantly improved scalability, reliability, developer productivity, and cost efficiency—while laying a strong foundation for future AI-driven analytics and ingestion automation. This project meets AWS Modernization Competency requirements in the areas of infrastructure modernization, serverless migration, operational excellence, and cost optimization.
Strategy
Harry's defined clear modernization outcomes: Move to fully serverless architecture (Redshift, EMR, Airflow) Reduce platform operational overhead by >70% Improve pipeline stability and SLA consistency Enable autoscaling to support retail data expansion Improve developer agility and deployment velocity Optimize spending with consumption-based pricing Strengthen governance, observability, and compliance
Solution Overview
Staying on provisioned Redshift → Rejected due to scaling limitations Autoscaling EMR on EC2 → Still required cluster management Kubernetes-based Airflow → Too operationally complex Lowest operational overhead Highest elasticity Best cost/performance ratio Deepest AWS integration
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