ADA helped a leading online pharmacy optimize data storage costs and performance by migrating from AWS Redshift to Apache Hive
A leading online pharmacy and medical platform serving 1,000+ cities and 22,000+ pin codes was experiencing rapid data growth from streaming transactions, customer activity, and operational reporting. Their existing AWS Redshift data warehouse began facing scalability limits, with storage costs rising sharply and query performance slowing down.
Long-running queries—often taking nearly an hour—delayed reporting cycles and impacted decision-making across operations, supply chain, and customer analytics teams. At the same time, the business needed to maintain uninterrupted reporting while modernising its data infrastructure, making migration and optimisation complex and high-risk.
The client required a scalable, cost-efficient data warehouse solution that could reduce storage expenses, improve query performance, and support continued growth without disrupting critical analytics workflows.
The customer is the region’s top online pharmacy and medical care platforms with doorstep delivery service available in 1000+ cities and towns across 22000+ pin codes. They provide a wide range of over-the-counter products and medical equipment across a broad budget spectrum.
The customer was facing growing storage costs and scalability bottlenecks on their existing AWS Redshift data warehouse as their volume of streaming and transactional data was increasing. They needed a more cost-effective data and customizable data warehouse for their data growth.
ADA helped to evaluate different technologies and developed a phase-by-phase plan to migrate the existing data to a Hadoop based Apache Hive data warehouse. We ensured that existing data reporting operations remain unaffected during the migration process.
ADA established best practices, performed necessary data validation, and deployed optimized codes that would power multiple business-wide dashboards and reports.
