Project Overview
Cubet collaborated with a multinational fashion retailer to transform its e-commerce ecosystem into a unified, Python-powered retail platform. The client’s previous setup relied heavily on third-party platforms, leading to high vendor costs, performance inefficiencies, and delayed feature rollouts. By building a microservices-driven architecture, Cubet delivered a cloud-native, scalable, and high-performance platform that consolidated operations, reduced external dependencies, and provided the agility needed for global expansion.
Industry
Retail / E-Commerce
Client
A multinational fashion retailer managing multiple brands and operating across diverse global markets.
Challenges Addressed
- Fragmented Systems: E-commerce operations distributed across multiple third-party platforms created silos and inefficiencies.
- Rising Costs: Dependence on external vendors increased licensing fees and operational expenses.
- Performance Bottlenecks: Slow rollouts and latency issues limited the ability to scale and adapt quickly.
- Data Management Issues: Customer and order data spread across platforms created complexity in access, migration, and synchronization.
- Operational Inflexibility: Lack of ownership over the commerce environment slowed expansion into new markets and regions.
Collaboration in Action
Cubet engineered a Python-based, microservices-driven solution that replaced fragmented systems with a cloud-native in-house platform. This approach gave the client complete ownership of their digital ecosystem while ensuring modular growth and seamless integration.
- Microservices Architecture: Built on Python frameworks, containerized, and orchestrated with Kubernetes.
- Core Services Delivered:
- User Account Service: Centralized authentication and account management for global customers.
- Product Catalog Service: APIs delivering real-time product details, pricing, and stock availability.
- Order Management Service (OMS): Integrated with warehouse and inventory systems for efficient global order handling.
- Data Migration Strategy:
- Customer Data: Migrated legacy records into relational PostgreSQL structures with full validation.
- Order Data:
- Hot Storage: Recent orders stored in PostgreSQL for fast access.
- Cold Storage: Historical orders maintained in legacy DBs and ingested progressively into PostgreSQL via an asynchronous Kafka pipeline.
- This hybrid strategy ensured zero data loss and instant access to critical data.
Technologies Deployed
- Backend: Python (FastAPI, Django, Flask)
- Databases: PostgreSQL, Redis, DynamoDB
- Search & Streaming: Elasticsearch, Apache Kafka
- Cloud & Storage: AWS (S3, EC2, EKS), Docker, Kubernetes
- Frontend: React.js with Server-Side Rendering (SSR)
- DevOps: Jenkins, GitHub Actions, CI/CD pipelines
Innovative Features
- Seamless OMS & System Integration: REST APIs and ETL pipelines connected OMS with IMS and WMS, with asynchronous job handling ensuring high throughput and real-time synchronization.
- Performance-Optimized Catalog APIs:
- Redis-based in-memory caching
- Optimized concurrent query execution
- Denormalized data structures for faster retrieval
- Order Processing at Scale:
- Elasticsearch-powered real-time order search
- Sharded and partitioned PostgreSQL for scalability
- Async logging and monitoring pipelines
- Frontend Enhancements:
- Server-Side Rendering (SSR) with React.js
- Lazy loading and asset compression for faster page loads
Value Delivered
The shift from external platforms such as Magento and Demandware to a fully Python-powered architecture delivered measurable business and operational value:
- Cost Reduction: Significantly lower vendor and licensing costs.
- Agility: Faster rollouts enabled expansion into new markets with minimal friction.
- Performance Gains: Sub-second response times across catalog and OMS operations.
- Scalability: Ability to support multiple brands and regions seamlessly from a unified platform.
User Feedback
The client reported enhanced agility in launching products across multiple regions, with technical teams appreciating the modular design that simplified integration and scaling. Business users highlighted faster reporting and order management capabilities, enabling smoother day-to-day operations.
Conclusion
By consolidating fragmented third-party systems into a Python-based, cloud-native retail ecosystem, Cubet empowered the client with ownership, agility, and cost efficiency. The solution not only improved performance but also laid the groundwork for future innovation and global scalability.
Impact Made
The client now operates a high-performance retail platform that supports multiple brands across global markets with speed and reliability. The modernization reduced costs, accelerated rollouts, and created a scalable foundation for long-term growth.