Our client is a leading rent-to-own service provider based out of the US that offers leasing services for commodities like furniture, appliances, and electronics. They provide services through an e-commerce website and around 2000+ offline storefront locations across the country.
The client had a legacy POS application, built over a decade ago using Java, Hibernate, and an Oracle database backend. So, they struggled to adapt evolving industry demands & resulted in challenges like
To address these challenges, the client sought a team of experts to migrate to a cloud-based, modular POS solution & enhance data management capabilities.
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Solution: Modernizing POS system & simplifying data management process
Our team analyzed the client’s existing application and devised a strategic solution that best fits their organizational needs.
Modern application development: We developed a modern POS application using React JS for the front end, Node JS for the backend, and AWS Aurora PostgreSQL as the database. This modern stack enhanced user experience and application performance.
Data model redesign: We redesigned the transactional data model for normalization and efficient indexing, improving data integrity and overall system efficiency.
Seamless data migration: To ensure a seamless migration of historical data from the legacy transactional database to the restructured PostgreSQL database, we devised a seamless migration strategy. This involved Oracle-side transformations, data storage in stage tables, and migration using AWS Data Migration Services (DMS).
Database optimization: We streamlined the data model by reducing the number of tables from 900+ to under 500. Additionally, we designed purpose-built databases, like the Config Database, to manage business rules for application access and configuration.
Benefits: Achieving scalable data management application
Improved scalability and agility: By migrating to a cloud-based modular POS system, the client achieved enhanced scalability and agility in their operations.
Enhanced application performance: The new microservices-based architecture and optimized data model significantly improved application performance, providing users with a smoother and faster experience.
Efficient data management: The streamlined data model and purpose-built databases enhanced data efficiency, ensuring that data was organized and accessible.
Cost savings: The cloud-native approach brought about cost savings, allowing overall resource utilization.
Tech Stack
- AWS Aurora PostgreSQL
- AWS DMS (Database Migration Service)
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