Designing implementing semantic data von wells dave (7 Ergebnisse)
- Softcover
Anbieter: California Books, Miami, FL, USACalifornia Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 4 SternenZustand: Neu
EUR 31,72
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
Zustand: New.
- Softcover
Anbieter: PBShop.store US, Wood Dale, IL, USAPBShop.store US
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 32,88
Versand gratisVersand innerhalb von USAAnzahl: Mehr als 20 verfügbar
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 32,09
EUR 3,84 VersandVersand von Vereinigtes Königreich nach USAAnzahl: Mehr als 20 verfügbar
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 43,00
EUR 60,94 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Taschenbuch. Zustand: Neu. Neuware.
- Softcover
- Print-on-Demand
Anbieter: Grand Eagle Retail, Bensenville, IL, USAGrand Eagle Retail
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 35,58
Versand gratisVersand innerhalb von USAAnzahl: 1 verfügbar
Paperback. Zustand: new. Paperback. 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 alwa…ys 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print-on-Demand
Anbieter: AussieBookSeller, Truganina, VIC, AustralienAussieBookSeller
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 47,55
EUR 31,66 VersandVersand von Australien nach USAAnzahl: 1 verfügbar
Paperback. Zustand: new. Paperback. 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 alwa…ys 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
- Print-on-Demand
Anbieter: CitiRetail, Stevenage, Vereinigtes KönigreichCitiRetail
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 37,86
EUR 43,19 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 1 verfügbar
Paperback. Zustand: new. Paperback. 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 alwa…ys 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
