AI-Governed Multi-Modal Data Sourcing Pipelines Using Apache Flink on Kubernetes: A Self-Evolving Architecture for Semantic Contracts, Schema Intelligence, and Cross-Format Normalization in Cloud Lakehouse Systems

Abstract

Contemporary​‍​‌‍​‍‌​‍​‌‍​‍‌ cloud lakehouse settings are grappling with the challenges of handling diverse data streams originating from transactional databases, messaging queues, REST APIs, and unstructured sources. Conventional ETL systems often struggle to handle schema drift, semantic inconsistencies, and changing data formats on a large scale.  The proposed AI-governed multi-modal data sourcing framework addresses fundamental limitations through three core innovations. Semantic contracts replace rigid schemas with machine-learned expectations encoding attribute relationships, value distributions, and contextual meanings derived from historical patterns. Self-evolving schema intelligence leverages large language models and embedding-based similarity scoring to detect structural drift, infer field transformations, and generate adaptation logic without manual intervention. Cross-format normalization unifies diverse modalities through AI-based extraction engines that process unstructured text, recursive parsing handles semi-structured hierarchies, and temporal alignment mechanisms maintain event ordering across sources with varying latency characteristics. Apache Flink deployed on Kubernetes provides distributed stream processing foundations enabling elastic scaling, stateful computation, and exactly-once processing semantics. Asynchronous barrier snapshots enable lightweight checkpointing without halting stream execution. Container orchestration automates resource allocation, failure recovery, and operator lifecycle management during transformation updates. The architecture delivers autonomous adaptation capabilities, semantic coherence across heterogeneous sources, and operational resilience for rapidly evolving lakehouse deployments.