Architectural Challenges in Building Real-Time Data Processing Systems for Search and Recommendations

Abstract

This article provides an in-depth analysis of the architectural challenges and solutions involved in building real-time data processing systems for search and recommendation engines. It begins by exploring the core architectural challenges, including latency management, scalability, and data consistency, which are critical for maintaining high performance and reliability in distributed environments. The discussion then advances to technology stack implementation, highlighting essential tools and frameworks such as Apache Kafka for stream processing and modern distributed storage solutions that underpin these systems. Focusing on Industry-specific considerations, the article examines unique requirements and strategies employed by e-commerce platforms and content delivery networks, illustrating how different sectors address their specific architectural needs. The data pipeline architecture section delves into sophisticated pipeline designs that ensure data integrity and efficient processing from ingestion to validation and monitoring.