Scalable Data Feed Management System for eCommerce Retailers

The client faces significant difficulties in aggregating, normalizing, and managing complex product data from numerous retailers and affiliates across diverse feed formats. Ensuring accurate data retrieval, consistency, and seamless integration with multiple formats has become increasingly complex as the retailer database grows beyond 100 sources, impacting operational efficiency and data accuracy.

A mid-sized retail technology company specializing in aggregating and standardizing product data from multiple online retailers to facilitate integrations and analytics.

• Develop a robust, scalable web platform to standardize and normalize product data from multiple retail sources.
• Improve efficiency in data aggregation by implementing automated scraping and ingestion mechanisms.
• Expand the retailer database while maintaining high data quality and consistency.
• Enable advanced data analysis capabilities for complex data provided by retailers.
• Ensure the system's architecture supports scalability and easy maintenance as data sources increase.

• Data aggregation and scraping engine capable of retrieving data from diverse retail trade points and online sources.
• Data normalization and conversion modules to provide standardized product data across all retailers.
• Support for integration with hundreds of different feed formats to ensure compatibility and seamless data ingestion.
• Advanced search and filtering functionality for efficient data querying and management.
• A scalable architecture that supports ongoing growth in data volume and source diversity.
• Robust data analysis engine to enable complex data insights and reporting.

• Data scraping tools and APIs for data aggregation
• Third-party feed format parsers for diverse retailer formats
• Database systems optimized for handling large-scale data normalization and querying

• System must support data aggregation from over 100 retail sources with room for growth.
• Migration and data processing should be performed with minimal latency, ensuring real-time or near-real-time data updates.
• The platform should be secure, safeguarding sensitive data and access controls.
• Architecture must be easily maintainable and support horizontal scaling to handle increased data volume.

The new data feed management system will streamline data aggregation and normalization, reducing manual data processing efforts. It will support the expansion of retailer integrations beyond 100 sources, improve data consistency, and enhance analytical capabilities. This will enable the client to deliver accurate, standardized product data more efficiently, leading to improved operational workflows and better decision-making support.