# How Migrating a Legacy Analytics Platform to AWS Improved Data Processing Performance by 85%

Our client, a technology-driven organization, set out to transition from a proprietary analytics platform to a more flexible and scalable cloud-based data...

_2026-04-20 · Technology & Digital Services · Data & Analytics_

## At a glance

Our client, a technology-driven organization, set out to transition from a proprietary analytics platform to a more flexible and scalable cloud-based data environment. The existing setup limited performance, introduced higher operational costs, and created dependency on a single vendor. To support growing data volumes, evolving reporting requirements, and future AI/ML initiatives, the organization sought a modern data architecture built on AWS that could deliver efficiency, governance, and long-term adaptability.

- **85%** — Improved Data Processing Performance
- **80%** — Continuous Data Consistency Validation
- **50%** — Lowered operational and licensing costs

## Challenge: Legacy analytics platform restricting scalability and flexibility

The client's existing analytics environment presented multiple operational and architectural constraints on the Domo platform. High platform costs, performance limitations, and vendor lock-in made it difficult to scale reporting, optimize data processing, or adapt the platform to future analytical needs.

They required a modern data foundation capable of supporting structured ingestion, standardized transformations, and analytics-ready datasets. The platform also needed to accommodate increasing data volumes, improve reporting reliability, and establish a foundation for advanced analytics and AI/ML, while maintaining operational continuity and governance.

## Solution: A cloud-native data platform built for scalable analytics

Our team delivered a structured migration and platform enablement initiative centered on AWS services, establishing a modern, governed data architecture designed for long-term growth.

- **AWS Data Platform Architecture:** Designed the target-state architecture using Amazon Redshift for data warehousing and Amazon QuickSight for analytics, creating a scalable foundation for reporting and future AI/ML use cases.
- **Layered Data Modeling and ETL Enablement:** Implemented a Bronze–Silver–Gold data architecture and developed ETL pipelines to migrate, transform, and prepare data for analytics while maintaining data consistency and quality.
- **DevOps, Testing, and Knowledge Transfer:** Configured environments, CI/CD pipelines, and infrastructure components, executed testing and validation, and delivered documentation and knowledge-sharing sessions to support operational continuity.

## Benefits: Achieved 85% faster data processing and continuous data validation

The initiative delivered meaningful improvements across the client's data and analytics landscape:

- **Lower Cost and Operational Efficiency:** Transitioning from a proprietary analytics platform to AWS established a more cost-effective operating model for data storage, processing, and reporting, resulting in 50% lower operational and licensing costs.
- **Improved Performance and Reporting Reliability:** Centralized data processing and a modern warehouse architecture enabled faster data access and stable reporting while supporting 85% improved data processing performance.

## Ready to modernize your data platform?

zeb, an AWS Premier Tier partner, helps organizations to migrate legacy analytics environments, design governed cloud architectures, and build scalable data platforms for long-term analytical growth. Our approach emphasizes structured data models, reliable engineering practices, and future-ready analytics foundations.

Let's create a unified data environment built for scalability and sustained insight.