Data Engineering First Principles: A Constraint-Driven Framework for Designing Reliable, Scalable, and AI-Native Data Systems

Abstract / Research Summary

This paper proposes Data Engineering First Principles (DEFP), a constraint-driven framework for reasoning about data systems before selecting technologies. DEFP models engineering decisions as a progression from requirements to measurable constraints, explicit trade-offs, architecture patterns, and only then technology selection. It organizes design around six recurring constraint families: data shape, scale, latency, reliability, economics, and governance/security, and extends the method to batch, streaming, cloud-native, lakehouse, and AI-native/RAG workloads.