Data Pipelines as Reliability-Critical Systems: A Systems-Theoretic View of Data Engineering for AI

Modern artificial intelligence systems increasingly depend on complex data pipelines that transform raw data into actionable insights for mission-critical applications. This paper presents a systems-theoretic framework for understanding data pipelines as reliability-critical systems, drawing parallels with established safety-critical system engineering practices. We formalize data pipelines as hybrid dynamical systems with both discrete state transitions and continuous data flows, subject to reliability constraints analogous to those in aerospace, automotive, and medical domains. Through systematic analysis of contemporary literature, we identify key failure modes, propose formal verification approaches adapted from control theory, and establish quality assurance frameworks grounded in systems theory. Our framework introduces the concept of "data safety barriers" analogous to control barrier functions, formalizes pipeline reliability metrics, and presents a taxonomy of failure modes specific to AI data engineering. We demonstrate that data pipelines for AI exhibit characteristics of safety-critical systems—including real-time constraints, fault propagation risks, and cascading failures—necessitating rigorous engineering approaches beyond traditional software quality assurance. This work bridges the gap between data engineering practice and formal systems theory, providing a foundation for developing provably reliable data infrastructure for AI applications in high-stakes domains.