Abstract
Organizations these days increasingly rely on fast growing data for their critical business decision making. To leverage full potential of data, petabytes of data are being ingested into central data lakes mostly powered by cloud. They also realize that it is not enough to just collect huge amounts of data. To derive value from this data, it must be cleansed, interconnected and translated from its complex technicalities into an easily interpretable and more familiar business terminology. Building a semantic view of the data enriched with business metrics enable users to query, analyze and visualize information as quickly as the business demands. While the semantic layer is perceived as the cornerstone in modern data architecture, there are different perspective towards where or how this should be implemented. Additionally, understanding the evolution of semantic layer over the years can help choose the right architecture when attempting to build one in an organization. With the advent of Artificial Intelligence (AI), it is imperative that we discuss the impact of AI on this topic. This research delves into the evolution of the need and significance of semantic layer, exploring their architecture, benefits. It also analyzes the challenges faced during semantic layer adoption and the outlook.
Published Version
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