Autonomous Data Products: Enabling AI-Driven Data Interoperability in Cloud Architectures
TL;DR
This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems and demonstrates how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.
Abstract
As enterprises transition toward increasingly distributed and cloud-native architectures, the need for seamless data interoperability has become paramount. Traditional data integration and governance approaches often fall short in dynamic, multi-cloud environments. Autonomous Data Products (ADPs) emerge as a transformative paradigm—self-contained, self-describing, and AI-enabled units that encapsulate data, metadata, policies, and processing logic. This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems. We discuss how AI enables adaptive schema evolution, smart data discovery, and automated quality checks within ADPs, and how they align with principles of data mesh and data fabric. Through technical frameworks and real-world use cases, we demonstrate how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.