Semantic-Aware Data Lake Architecture with Automated Schema Evolution and Intelligent Data Discovery

This paper presents a semantic-aware data lake architecture that incorporates automated schema evolution and intelligent data discovery capabilities to address the challenges of managing diverse, rapidly changing datasets in modern big data environments. The proposed Semantic Data Lake System (SDLS) combines knowledge graphs, machine learning, and advanced metadata management to create self-organizing data repositories that automatically understand, categorize, and optimize data storage and access patterns. Our methodology employs natural language processing and ontology learning techniques to automatically extract semantic meaning from ingested data, creating rich metadata descriptions that enable intelligent data discovery and lineage tracking. The system includes a novel automated schema evolution mechanism that can detect schema changes in streaming data and automatically update data catalogs and downstream processing pipelines without service interruption. We introduce an intelligent data discovery engine that uses semantic similarity and usage pattern analysis to help users find relevant datasets and understand their relationships. The architecture incorporates advanced data quality monitoring and automatic anomaly detection to maintain data integrity across the entire lake. Our implementation demonstrates superior performance in data discovery tasks with 78% improvement in relevant dataset identification and 65% reduction in data preparation time. Experimental validation using multi-domain enterprise datasets shows effective handling of schema evolution events with minimal performance impact and enhanced data governance capabilities.