Harry’s – Enterprise Data Platform Modernization on AWS Serverless

Staying on provisioned Redshift → Rejected due to scaling limitations·

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 suppor

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

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.

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

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

40–55% cost reduction in Redshift compute
60–70% reduction in EMR compute cost
80% reduction in Airflow operational overhead
50% faster pipeline recovery times
Improved SLA compliance from 92% → 99.4%
Zero maintenance windows for Airflow
Seamless onboarding of multiple retail ingestion pipelines

Technical Info

IndustryGeneral
EngagementStaying on provisioned Redshift → Rejected due to scaling limitations

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