High-Throughput Bid Enrichment Architecture: Data Flow in a Million-QPS Ad-Tech Platform

Abstract

This article presents a high-throughput bid enrichment architecture designed for million-QPS advertising technology platforms. The architecture addresses the challenges of processing massive data volumes while maintaining strict latency requirements in real-time bidding environments. The framework integrates Apache Spark on EMR for data ingestion, Apache Airflow for workflow orchestration, Snowflake for storage, and implements sophisticated machine learning techniques including feature engineering, clustering with Spark MLlib, and uplift modeling with LightGBM. The article details engineering solutions for data skew mitigation, partition tuning, cost-efficient scaling, and latency optimization. The implementation demonstrates significant performance improvements across multiple campaign categories, resulting in substantial ROI lift and incremental revenue while reducing operational costs. The architecture's success highlights the importance of microservice design, hybrid batch/streaming approaches, comprehensive testing methodologies, and systematic technical debt management in building scalable, high-performance advertising platforms.