Verwandte Artikel zu BUILDING AI-READY SEMANTIC LAYERS: GOVERNING METRICS...
BUILDING AI-READY SEMANTIC LAYERS: GOVERNING METRICS FOR LLMS AND DATA AGENTS: Implement dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views for Consistent AI and BI Analytics - Softcover
Inhaltsangabe
Build a semantic layer that gives BI tools, LLMs, and data agents the same governed business meaning.
Giving an AI model access to a warehouse does not guarantee analytically correct answers. Valid SQL can still use the wrong metric, join at the wrong grain, duplicate revenue, misinterpret time, expose restricted data, or produce a different answer from the company dashboard.
This practical guide shows how to move stable business logic out of prompts and ad hoc queries and into governed semantic models. You will learn how to design metrics that remain consistent across traditional analytics and AI systems, then implement and operate them with dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views.
- Model grain, entities, primary keys, joins, facts, dimensions, measures, and semantic contracts correctly
- Prevent fanout, chasm traps, duplicate aggregation, unsafe distinct counts, and ratio errors
- Build simple, ratio, derived, cumulative, and conversion metrics with governed time semantics
- Implement semantic models with MetricFlow, Cube Cloud, and Snowflake Semantic Views
- Prepare metrics for LLMs with descriptions, synonyms, AI context, verified queries, literal resolution, and domain routing
- Connect semantic services to applications and agents through SQL, REST, GraphQL, SDKs, and MCP
- Design governed MCP tools for metric discovery and semantic querying instead of unrestricted raw SQL
- Combine structured analytics with document retrieval, search, and multi-step agent reasoning
- Protect AI analytics with RBAC, row security, masking, tenant isolation, identity propagation, and least privilege
- Defend against prompt injection, tool misuse, semantic model poisoning, data exfiltration, and unsafe write actions
- Test metric behavior with semantic unit tests, paraphrase evaluations, regression suites, and cross-engine contract tests
- Control caching, pre-aggregations, materializations, query fanout, latency, concurrency, and agent cost
- Govern metric ownership, certification, versioning, deprecation, drift detection, and multi-platform synchronization
The guide includes extensive SQL, YAML, Python, Shell, JSON, and GraphQL examples that connect semantic concepts to realistic implementation, testing, security, agent integration, and production operations.
You will also learn how to build a production semantic trust chain so that transformations, metric definitions, execution engines, access policies, BI tools, and AI agents stay aligned around the same approved business definitions.
Grab your copy today and build analytics systems where AI flexibility works on top of governed, testable, and consistent business semantics.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Suchergebnisse für BUILDING AI-READY SEMANTIC LAYERS: GOVERNING METRICS...
Beispielbild für diese ISBN
BUILDING AI-READY SEMANTIC LAYERS: GOVERNING METRICS FOR LLMS AND DATA AGENTS: Implement dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views for Consistent AI and BI Analytics
Print-on-DemandAnbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Print on Demand. Bestandsnummer des Verkäufers I-9798170548637
Neu kaufen
EUR 34,84
EUR 2,67 Versand
Versand innerhalb von USA
Versand innerhalb von USA
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Building Ai-Ready Semantic Layers
Print-on-DemandAnbieter: PBShop.store US, Wood Dale, IL, USA
Verkäuferbewertung 5 von 5 Sternen
PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9798170548637
Neu kaufen
EUR 37,82
Versand gratis
Versand innerhalb von USA
Versand innerhalb von USA
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Building Ai-Ready Semantic Layers (Paperback)
Print-on-DemandAnbieter: Grand Eagle Retail, Bensenville, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. Build a semantic layer that gives BI tools, LLMs, and data agents the same governed business meaning.Giving an AI model access to a warehouse does not guarantee analytically correct answers. Valid SQL can still use the wrong metric, join at the wrong grain, duplicate revenue, misinterpret time, expose restricted data, or produce a different answer from the company dashboard.This practical guide shows how to move stable business logic out of prompts and ad hoc queries and into governed semantic models. You will learn how to design metrics that remain consistent across traditional analytics and AI systems, then implement and operate them with dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views.Model grain, entities, primary keys, joins, facts, dimensions, measures, and semantic contracts correctlyPrevent fanout, chasm traps, duplicate aggregation, unsafe distinct counts, and ratio errorsBuild simple, ratio, derived, cumulative, and conversion metrics with governed time semanticsImplement semantic models with MetricFlow, Cube Cloud, and Snowflake Semantic ViewsPrepare metrics for LLMs with descriptions, synonyms, AI context, verified queries, literal resolution, and domain routingConnect semantic services to applications and agents through SQL, REST, GraphQL, SDKs, and MCPDesign governed MCP tools for metric discovery and semantic querying instead of unrestricted raw SQLCombine structured analytics with document retrieval, search, and multi-step agent reasoningProtect AI analytics with RBAC, row security, masking, tenant isolation, identity propagation, and least privilegeDefend against prompt injection, tool misuse, semantic model poisoning, data exfiltration, and unsafe write actionsTest metric behavior with semantic unit tests, paraphrase evaluations, regression suites, and cross-engine contract testsControl caching, pre-aggregations, materializations, query fanout, latency, concurrency, and agent costGovern metric ownership, certification, versioning, deprecation, drift detection, and multi-platform synchronizationThe guide includes extensive SQL, YAML, Python, Shell, JSON, and GraphQL examples that connect semantic concepts to realistic implementation, testing, security, agent integration, and production operations.You will also learn how to build a production semantic trust chain so that transformations, metric definitions, execution engines, access policies, BI tools, and AI agents stay aligned around the same approved business definitions.Grab your copy today and build analytics systems where AI flexibility works on top of governed, testable, and consistent business semantics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798170548637
Beispielbild für diese ISBN
Building Ai-Ready Semantic Layers
Print-on-DemandAnbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9798170548637
Neu kaufen
EUR 34,03
EUR 5,99 Versand
Versand von Vereinigtes Königreich nach USA
Versand von Vereinigtes Königreich nach USA
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Building Ai-Ready Semantic Layers (Paperback)
Print-on-DemandAnbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. Build a semantic layer that gives BI tools, LLMs, and data agents the same governed business meaning.Giving an AI model access to a warehouse does not guarantee analytically correct answers. Valid SQL can still use the wrong metric, join at the wrong grain, duplicate revenue, misinterpret time, expose restricted data, or produce a different answer from the company dashboard.This practical guide shows how to move stable business logic out of prompts and ad hoc queries and into governed semantic models. You will learn how to design metrics that remain consistent across traditional analytics and AI systems, then implement and operate them with dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views.Model grain, entities, primary keys, joins, facts, dimensions, measures, and semantic contracts correctlyPrevent fanout, chasm traps, duplicate aggregation, unsafe distinct counts, and ratio errorsBuild simple, ratio, derived, cumulative, and conversion metrics with governed time semanticsImplement semantic models with MetricFlow, Cube Cloud, and Snowflake Semantic ViewsPrepare metrics for LLMs with descriptions, synonyms, AI context, verified queries, literal resolution, and domain routingConnect semantic services to applications and agents through SQL, REST, GraphQL, SDKs, and MCPDesign governed MCP tools for metric discovery and semantic querying instead of unrestricted raw SQLCombine structured analytics with document retrieval, search, and multi-step agent reasoningProtect AI analytics with RBAC, row security, masking, tenant isolation, identity propagation, and least privilegeDefend against prompt injection, tool misuse, semantic model poisoning, data exfiltration, and unsafe write actionsTest metric behavior with semantic unit tests, paraphrase evaluations, regression suites, and cross-engine contract testsControl caching, pre-aggregations, materializations, query fanout, latency, concurrency, and agent costGovern metric ownership, certification, versioning, deprecation, drift detection, and multi-platform synchronizationThe guide includes extensive SQL, YAML, Python, Shell, JSON, and GraphQL examples that connect semantic concepts to realistic implementation, testing, security, agent integration, and production operations.You will also learn how to build a production semantic trust chain so that transformations, metric definitions, execution engines, access policies, BI tools, and AI agents stay aligned around the same approved business definitions.Grab your copy today and build analytics systems where AI flexibility works on top of governed, testable, and consistent business semantics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798170548637
Neu kaufen
EUR 38,67
EUR 44,12 Versand
Versand von Vereinigtes Königreich nach USA
Versand von Vereinigtes Königreich nach USA
Anzahl: 1 verfügbar
Foto des Verkäufers
Building Ai-Ready Semantic Layers : GOVERNING METRICS FOR LLMS AND DATA AGENTS: Implement dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views for Consistent AI and BI Analytics
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware - Build a semantic layer that gives BI tools, LLMs, and data agents the same governed business meaning.Giving an AI model access to a warehouse does not guarantee analytically correct answers. Valid SQL can still use the wrong metric, join at the wrong grain, duplicate revenue, misinterpret time, expose restricted data, or produce a different answer from the company dashboard.This practical guide shows how to move stable business logic out of prompts and ad hoc queries and into governed semantic models. You will learn how to design metrics that remain consistent across traditional analytics and AI systems, then implement and operate them with dbt MetricFlow, Cube Cloud, and Snowflake Semantic Views.- Model grain, entities, primary keys, joins, facts, dimensions, measures, and semantic contracts correctly- Prevent fanout, chasm traps, duplicate aggregation, unsafe distinct counts, and ratio errors- Build simple, ratio, derived, cumulative, and conversion metrics with governed time semantics- Implement semantic models with MetricFlow, Cube Cloud, and Snowflake Semantic Views- Prepare metrics for LLMs with descriptions, synonyms, AI context, verified queries, literal resolution, and domain routing- Connect semantic services to applications and agents through SQL, REST, GraphQL, SDKs, and MCP- Design governed MCP tools for metric discovery and semantic querying instead of unrestricted raw SQL- Combine structured analytics with document retrieval, search, and multi-step agent reasoning- Protect AI analytics with RBAC, row security, masking, tenant isolation, identity propagation, and least privilege- Defend against prompt injection, tool misuse, semantic model poisoning, data exfiltration, and unsafe write actions- Test metric behavior with semantic unit tests, paraphrase evaluations, regression suites, and cross-engine contract tests- Control caching, pre-aggregations, materializations, query fanout, latency, concurrency, and agent cost- Govern metric ownership, certification, versioning, deprecation, drift detection, and multi-platform synchronizationThe guide includes extensive SQL, YAML, Python, Shell, JSON, and GraphQL examples that connect semantic concepts to realistic implementation, testing, security, agent integration, and production operations.You will also learn how to build a production semantic trust chain so that transformations, metric definitions, execution engines, access policies, BI tools, and AI agents stay aligned around the same approved business definitions.Grab your copy today and build analytics systems where AI flexibility works on top of governed, testable, and consistent business semantics. Bestandsnummer des Verkäufers 9798170548637
Neu kaufen
EUR 53,15
EUR 35,00 Versand
Versand von Deutschland nach USA
Versand von Deutschland nach USA
Anzahl: 2 verfügbar