Designing Real-Time Distributed Data Pipelines for AI-Ready AdTech and CRM Decision Systems

Abstract

Aim: While streaming behavioral signals are essential for real-time bidding optimization and next-best-action CRM decisioning, most enterprise data stacks are still batch-oriented, causing delays in decision-making and reducing model freshness. This study aims to develop and comparatively evaluate a reference real-time distributed pipeline architecture for AI-enabled AdTech and CRM decision systems, focusing on latency, throughput, fault tolerance, AI integration, and regulatory-readiness requirements. Methods: A reference real-time distributed pipeline architecture is designed and evaluated that incorporates event-time semantics, watermarking, idempotent deduplication, a unified identity and entity graph to reconcile customer journeys across channels and enable data joins that respect privacy consent, while complying with relevant privacy laws and regulations. Structurally reduces training-serving skew by materializing features into an online feature store and deterministically replaying them. The architecture is evaluated by comparing it to two baselines: batch and micro-batch, on five operational dimensions: latency, throughput, fault tolerance, capability to integrate AI, and readiness for regulatory compliance. Results: The comparative analysis indicates that the proposed streaming architecture is capable of supporting the stated RTB latency requirement, whereas batch and conventional micro-batch approaches are less suitable for stringent real-time decisioning. Conclusion: Event-time-first, privacy-aware distributed pipeline architectures are a significant and feasible step toward AI-ready infrastructure for AdTech and CRM decision systems, and a reference design that is based on production-tested frameworks. Recommendation: Organizations adopting real-time AI decisioning should prioritize evaluation of streaming infrastructure, feature consistency, governance controls, and latency requirements, while future research should validate the proposed architecture using controlled production-scale experiments.