Data has a meaning problem. Semantic data layers solve it. Semantics, applied to data, is the discipline of making meaning explicit, consistent, and durable. Semantic data layers are the architectural expression of that discipline. This book shows you how to build them.
Data management has always been messy. Inconsistent metrics, brittle integrations, data silos, and confusing dashboards persist even after heavy investment in data lakes, warehouses, pipelines, BI tools, metadata catalogs, and modern platforms. At the core, these are not technology problems; they're problems of meaning. More infrastructure doesn't fix a meaning problem. It expands it. AI workloads add new urgency: systems that act autonomously on data they don't fully understand compound the cost of every inconsistency.
Semantic data layers turn scattered data definitions into shared, stable, reusable business meaning that is independent of the physical structures that hold the data. The result is strong data interoperability, reliable analytics, improved self-service access, reduced technical debt, and a practical foundation for trustworthy AI.
Building semantic layers well requires three things in the right order. First, architecture: understanding the five types of semantic layers (enterprise, domain, integration, enrichment, and consumption) and where each belongs in your data management world. Next comes design: ontology, taxonomy, semantic models, knowledge graphs, and property graphs. This is the conceptual work that captures how your business actually understands its data. Semantic models that business people can read and validate are more durable than technically precise models that only engineers can interpret. Then comes implementation: building semantic layers as software through APIs, data products, data contracts, data virtualization, and schema registries.
Architecture first, next design, and then implementation. This book covers all three. Written for data architects, data engineers, data leaders, data governance professionals, analytics teams, and the emerging Semantic Layer Engineer, it is a practical guide to reducing data friction, improving data quality, increasing reuse, and enabling self-service analytics across a complex enterprise.
When data has shared meaning, systems connect more easily, people trust the answers, and AI has the context it needs to reason responsibly.
外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。
