Intent-Driven Data Platforms: Replacing Pipeline Engineering with Declarative System Intent

Abstract

Traditional data platforms require explicit pipeline engineering, where developers must encode operational logic, performance assumptions, and governance rules directly into the procedural code, creating brittle systems that resist adaptation and scale poorly with organizational growth. Intent-driven data platforms introduce a declarative execution model that separates business intent from technical implementation, enabling users to specify desired outcomes through natural language constructs while the platform automatically determines optimal execution strategies. The proposed architecture comprises three core components: an intent parser that analyzes declarative specifications, an execution planner that generates optimal processing workflows, and a runtime optimizer that continuously adapts system behavior to maintain compliance with specified objectives. Evaluation across multiple enterprise environments demonstrates substantial improvements in development efficiency, operational complexity reduction, and system adaptability compared to traditional pipeline approaches. The intent-driven paradigm enables autonomous data platform operation while maintaining consistent governance enforcement and cost optimization through automated resource management strategies.