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데이터 관리
데이터 관리 개요
논리적 데이터 관리
데이터 가상화
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디노도 플랫폼
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기능별
Denodo AI SDK
레이크하우스 액셀러레이터
데이터 마켓플레이스 / 카탈로그
디노도 어시스턴트
쿼리 가속
시맨틱 레이어
기술별
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생성형 AI
Snowflake
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AI Sovereignty Self-Assessment
AI 도입 준비가 끝났습니다. 그런데, 데이터도 준비됐나요?
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
You know where AI data resides, where it is copied, where it is processed, and which jurisdictions and policies apply.
Data Control
You know where AI data resides, where it is copied, where it is processed, and which jurisdictions and policies apply.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
AI can access current operational data from authoritative sources without waiting for batch pipelines or creating unnecessary copies.
Live Context
AI can access current operational data from authoritative sources without waiting for batch pipelines or creating unnecessary copies.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
AI uses consistent business definitions, source-of-truth rules, lineage, and context across domains and systems.
Semantic Trust
AI uses consistent business definitions, source-of-truth rules, lineage, and context across domains and systems.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
Access controls, masking, entitlements, residency rules, and usage policies are enforced at the point of AI access.
Runtime Governance
Access controls, masking, entitlements, residency rules, and usage policies are enforced at the point of AI access.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
You can deliberately decide where AI-related data processing occurs: source, cache, cloud, on-premises, edge, or sovereign environment.
Workload Placement
You can deliberately decide where AI-related data processing occurs: source, cache, cloud, on-premises, edge, or sovereign environment.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
You can change models, cloud systems, lakehouses, vector stores, AI tools, and even repatriate selected cloud data/workloads on-premises without rebuilding the governed data foundation or disrupting consumers.
Portability and Architecture Choice
You can change models, cloud systems, lakehouses, vector stores, AI tools, and even repatriate selected cloud data/workloads on-premises without rebuilding the governed data foundation or disrupting consumers.
Not true today
Rarely true
True for some AI use cases
Mostly true
Consistently true and auditable across all AI use cases
무료 다운로드
디노도 플랫폼 평가판을 다운로드하시고 거버넌스되는 데이터 액세스에 대해 배우고 이를 구현해 보시고 바랍니다.
지금 다운로드