How we achieved 84% efficient data management with cloud-based modernization
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...
At a Glance
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
Challenge
Data quality concerns and migration complexities with the legacy POS system
Our client faced the significant challenge of modernizing their legacy Point of Sale (POS) system. It was built utilizing Java, Hibernate, and an Oracle database backend, this system-imposed challenges in data quality management. The client aimed to redesign the data model, focusing on normalization techniques to ensure enhanced data integrity and reliable decision-making.
Simultaneously, the client sought a seamless migration strategy for 8 TB of historical data spanning six years. The extensive dataset comprised over 900 tables, including 600+ transaction tables and 200+ lookup tables. This intricate migration demanded meticulous planning and execution within a designated timeframe.
In response to these challenges, the client showcased a commitment to embracing modern solutions and methodologies. This encompassed the adoption of microservices architecture and leveraging the AWS Aurora Database. Ultimately, their goal was to migrate to a cloud-based POS solution that is scalable and modular, improving data management to align with evolving business requirements.
Solution
Overcoming legacy POS system constraints with cloud-based modernization
Our team devised a comprehensive solution considering the client's unique business challenges and organizational data model. Here's what we implemented:
Tech Stack
Amazon Aurora - PostgreSQL
AWS Database Migration Service (AWS DMS)
React JS
Node JS
Application development and database restructuring Our team developed a robust POS application leveraging React JS for the frontend, Node JS for the backend, and AWS Aurora PostgreSQL as the transactional database.
Additionally, we redesigned the transactional data model by prioritizing normalization and efficient indexing. This significantly resulted in enhanced application performance.
Data model optimization We optimized the data model by reducing the number of tables from 900+ to under 500, fostering improved data handling and processing.
Further, we developed purpose-built databases, such as the Config Database to effectively manage critical business rules required for application access and configuration.
Seamless data migration strategy Our team implemented a seamless strategy for migrating historical data from the legacy transactional database to the restructured PostgreSQL database. This comprehensive approach encompassed Oracle-side transformations, data storage in stage tables, and AWS Data Migration Services (DMS).
Management of DMS Tasks To ensure data integrity and streamline the synchronization process, our team meticulously managed Data Migration Service (DMS) tasks at the module level. This approach enabled precise and controlled data transfer, mitigating potential issues during the migration process.
Migration optimization strategies We accelerated the migration process by segregating tables based on volume to expedite the migration process. This involved establishing multiple DMS tasks concurrently to meet the designated timeframe.
Furthermore, we implemented a partitioning strategy for tables with substantial volumes, breaking down the data into manageable chunks factoring in primary key ranges. This approach created multiple DMS tasks, each configured to manage specific primary key ranges as defined in the mapping rule.
Benefits
84% efficient data management & 72% cost savings
- Enhanced scalability and operational agility with a cloud-based modular POS system.
- Optimized resource allocation for efficient operations, fostering adaptability to evolving market demands.
- Improved data management for informed decisions, leading to increased revenue growth and enhanced customer satisfaction.
- Enhanced system reliability and cost savings by adopting a microservices architecture.
- Improved data integrity, resulting in accurate reporting and analytics through a redesigned data model.
- Successfully migrated extensive historical data and ensured accessibility, contributing to sustained business growth.
Ready to modernize your data platform?
Our partnership with the retail client has transformed their data strategies, showcasing the pivotal role of optimized data management in driving tangible and sustained business growth. Whether you are struggling with outdated data systems or handling the intricacies of data management, we are here to help you.
Partner with us to embark on a journey towards a data-powered business model.
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