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

Data Engineering First Principles (DEFP) presents a constraint-driven framework for designing reliable, scalable, cost-aware, and AI-native data systems. Rather than beginning architecture decisions with specific technologies, the framework proposes a reasoning sequence of Requirements → Constraints → Trade-offs → Architecture → Technology Selection. It organizes data engineering decisions around six recurring constraint families: data shape, scale, latency, reliability, economics, and governance/security. The paper applies this approach to batch and streaming architectures, cloud-native systems, warehouses, data lakes and lakehouses, relational and non-relational storage, DataOps and observability, and AI-native pipelines involving embeddings, vector retrieval and retrieval-augmented generation (RAG). It also introduces an Architecture Trade-off Matrix and an illustrative composite enterprise case study demonstrating how first-principles reasoning can guide architecture decisions without relying on tool-first design. The central argument is that while technologies continually change, durable data engineering expertise comes from understanding system constraints, architectural trade-offs, and the principles underlying reliable data systems.