Decentralized Schema Evolution Management via Collective Learning in Enterprise Data Ingestion Pipelines

Abstract

Enterprise data ingestion pipelines are increasingly required to integrate heterogeneous data sources whose schemas evolve continuously and often independently. Conventional schema management practices rely on centralized registries and tightly controlled governance processes, which become difficult to sustain when organizations adopt decentralized ownership models and rapidly changing application ecosystems. At the same time, large organizations seek to maintain global consistency constraints, auditability, and reliability of downstream analytics while allowing local teams to introduce changes at their own pace. This tension between local autonomy and global coherence motivates approaches that distribute responsibility for schema evolution while still coordinating decisions across multiple ingestion components. This paper examines a decentralized perspective on schema evolution management for enterprise ingestion pipelines, with particular emphasis on collective learning among ingestion agents and services. The proposed perspective models each ingestion component as a learning agent that maintains a local representation of schemas and their transformations, and that exchanges summaries of its beliefs with neighboring agents in the ingestion topology. Through this view, schema evolution becomes a continuous adaptation problem where agents collaboratively update shared representations of compatible schemas and transformation operators. The paper develops a linear modeling framework that formalizes this process and discusses how it can be instantiated in practical settings such as log aggregation, event streaming, and batch ingestion architectures. The discussion remains focused on structural properties of the models and algorithms rather than specific technologies, with the goal of supporting a range of system realizations.