Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Exploring the pivotal role of semantic layers in modern analytics architectures, this article delves into how these specialized abstraction layers bridge complex data structures and business users. Such intermediaries enable intuitive data exploration without technical expertise while offering standardization and democratized analytics access across organizations. Various implementation approaches receive thorough examination - from traditional metadata-driven models to code-first methodologies and integrated transformation frameworks. The evolution toward AI-enhanced semantic layers reveals how machine learning facilitates dynamic schema inference, conversational interfaces, and knowledge graph integration. Forward-looking trends emerge through decentralized semantic modeling, embedded AI assistance throughout modeling processes, and expansion beyond structured data to incorporate diverse analytics. Throughout, the article highlights the delicate balance between governance requirements and accessibility needs, positioning semantic layers as foundational elements within successful business intelligence strategies.