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Horizon Catalog 내에서 거버넌스가 적용되는 컨텍스트 계층으로, 메타데이터를 수집하고 비즈니스 의미를 더해 사용자, BI 도구, AI 에이전트가 활용할 수 있도록 합니다.\u003C/p\u003E\u003Cp\u003E이제 18개 소스를 지원하는 메타데이터 커넥터, 스튜어드에게 설명 관리에 필요한 권한만 부여하는 기능, 지원되는 다운스트림 컬럼으로 설명을 자동 전파하는 기능이 퍼블릭 프리뷰로 제공됩니다. 이러한 기능은 최근 새롭게 설계된 Horizon Catalog Explorer와 함께 기존 데이터 지식을 찾아 보강하고 AI와 분석에 재사용할 수 있도록 돕습니다.\u003C/p\u003E\u003Cp\u003EHorizon Context를 사용하면 다음과 같은 작업이 가능합니다.\u003C/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E사용 전 데이터 이해\u003C/strong\u003E: 정의, 태그, 담당자 정보를 쉽게 확인할 수 있는 일관된 카탈로그 경험을 통해 데이터를 이해합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E기존 데이터 지식 재사용\u003C/strong\u003E: 각 AI 사용 사례마다 다시 구성하지 않고 보고서, 데이터베이스, 파이프라인의 지식을 활용합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E문제 해결 및 변경 영향 평가\u003C/strong\u003E: 지원되는 소스, 변환 과정, BI 도구를 연결하는 데이터 계보를 활용합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E비즈니스 전문가의 컨텍스트 관리 참여\u003C/strong\u003E: 실제 데이터에 대한 더 광범위한 제어 권한을 부여하지 않고도 참여할 수 있습니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E문서 유지 관리 부담 감소\u003C/strong\u003E: 지원되는 다운스트림 컬럼으로 설명을 전파합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E공유 컨텍스트에 기반한 AI와 분석\u003C/strong\u003E: 각 애플리케이션이나 프롬프트에서 지식을 다시 구성할 필요를 줄입니다.\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"blog-title-c4e8b39c4f","propertiesId":"ai","type":"heading2","lines":["AI 도입 확대에 맞춰 비즈니스 이해도 향상"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_2":{"id":"blog-text-dfa34d648a","text":"\u003Cp\u003E고객과 대화해 보면 AI에 대한 열정과 함께 실질적인 기대도 드러납니다. 필요한 데이터를 찾고 그 의미를 명확히 이해하도록 도우며, 소수 전문가에 대한 의존도를 줄여 달라는 것입니다. 팀은 보고서의 데이터 출처를 확인하고, 문서를 반복해서 유지 관리하는 대신 재사용할 수 있다는 점을 중요하게 여깁니다. 데이터 작업을 에이전트에 맡길 때는 이러한 요구가 더욱 중요해집니다.\u003C/p\u003E\u003Cp\u003EAI 도입이 확대될수록 에이전트마다 별도의 지침과 정의를 관리하면 업무가 늘고 불일치가 발생할 가능성도 커집니다. 거버넌스가 적용된 공통 컨텍스트 계층은 팀이 여러 사용 사례에서 재사용할 수 있는 기반을 제공합니다. 비즈니스를 이해하기 위해 매번 새로운 통합 프로젝트를 진행할 필요가 없어집니다.\u003C/p\u003E\u003Cp\u003EHorizon Context는 이 기반의 세 요소인 데이터 시스템에 담긴 근거, 비즈니스 전문가의 지식, 그리고 이 둘을 활용하는 도구를 연결합니다. 목표는 컨텍스트를 재구성하는 시간을 줄이고, 이를 비즈니스 의사 결정에 활용하는 데 더 많은 시간을 쓰는 것입니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"blog-title-2ddf579864","propertiesId":"","type":"heading2","lines":["기존 데이터 투자를 통해 축적한 지식 활용"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_4":{"id":"blog-text-df2f7ce0b0","text":"\u003Cp\u003E조직은 비즈니스 운영을 뒷받침하는 보고서와 데이터 파이프라인에 이미 투자해 왔습니다. 그 메타데이터에는 데이터의 구조, 변환 과정, 활용 방식에 관한 중요한 근거가 담겨 있습니다. 이러한 근거를 활용할 수 있으면 AI 개발자는 개별 테이블 이름과 컬럼 목록에만 의존할 때보다 더 탄탄한 기반에서 시작할 수 있습니다.\u003C/p\u003E\u003Cp\u003EHorizon Catalog의 메타데이터 커넥터는 이제 데이터베이스, BI, 데이터 파이프라인 도구 전반의 18개 소스를 지원합니다. 여기에는 Power BI, Tableau, Looker, Sigma, Hex, dbt, SQL Server, Oracle, PostgreSQL, Databricks, BigQuery가 포함됩니다.\u003C/p\u003E\u003Cp\u003E커넥터와 구성에 따라 Horizon Context는 스키마, 쿼리 로그, 대시보드 정의, 사용 현황 신호를 수집합니다. 팀은 카탈로그에서 Snowflake 오브젝트와 외부 자산을 함께 탐색하고, 해당 메타데이터를 검색하며, 플랫폼 간 데이터 계보를 살펴볼 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"blog-title-cca0be9c75","propertiesId":"","type":"heading2","lines":["데이터 이해에 필요한 정보를 한눈에"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_6":{"id":"blog-text-bcc20b1480","text":"\u003Cp\u003E새롭게 설계된 \u003Ca href=\"https://www.snowflake.com/ko/product/features/horizon/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHorizon Catalog\u003C/a\u003E는 데이터 정의, 태그, 담당자 정보를 쉽게 확인할 수 있는 일관된 경험을 제공합니다. 비즈니스 분석가는 데이터 자산을 쉽게 찾고, 그 의미와 활용 방식, 추가 설명을 요청할 대상을 파악할 수 있습니다. 이러한 기능과 메타데이터 커넥터를 갖춘 Horizon Catalog는 Snowflake와 연결된 시스템 전반의 데이터를 한곳에서 파악할 수 있는 통합 창구가 됩니다. 팀과 에이전트는 필요한 데이터를 찾아 공통의 비즈니스 컨텍스트를 바탕으로 활용할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_7":{"id":"image-44f9fc9cda","isLcpImage":true,"alt":"Screenshot of a data catalog interface showing the CUSTOMERS dataset overview with column details and table metadata","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b85d7cee-a82e-438f-b7ab-6ce1130f0dd6/data-catalog-interface-showing-the-customers-dataset.png?preferwebp=true&quality=85","height":"768","width":"1280",":type":"snowflake-site/components/image"},"blog_component_8":{"id":"image-1bfb6ffec7","isLcpImage":false,"alt":"Data lineage diagram showing relationships between CUSTOMERS dataset, source databases, tables, and downstream analytics reports","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d21427b3-4116-415d-8957-0dfe64f7150a/data-lineage-diagram-showing-relationships-between.png?preferwebp=true&quality=85","height":"768","width":"1281",":type":"snowflake-site/components/image"},"blog_component_9":{"id":"blog-text-f2311de732","text":"\u003Cp\u003E고객 유지율 보고서에서 예상치 못한 하락이 나타나면, 팀은 실제 비즈니스 변화를 반영한 것인지 데이터 문제 때문인지 조사해야 합니다. 데이터 계보를 활용하면 지원되는 연결 시스템 전반에서 보고서의 데이터 변환 과정을 거슬러 원본 데이터까지 추적하고, 조사할 지점을 파악할 수 있습니다. 업스트림 테이블이나 컬럼을 변경하기 전에 동일한 종속 관계를 다운스트림으로 따라가 어떤 보고서가 영향을 받을 수 있는지 평가할 수 있습니다.\u003C/p\u003E\u003Cp\u003ESnowflake CoCo는 이러한 컨텍스트를 AI 지원 워크플로우에 활용합니다. 팀은 ‘이 고객 유지율 데이터 세트의 출처는 어디인가?’ 또는 ‘이 컬럼에 의존하는 다운스트림 보고서는 무엇인가?’와 같은 질문을 할 수 있습니다. CoCo는 확인 가능한 데이터 계보를 활용해 문제 조사와 변경 영향 평가를 도울 수 있으므로, 종속 관계를 수작업으로 다시 구성해야 하는 부담이 줄어듭니다.\u003C/p\u003E\u003Cp\u003E이러한 커넥터는 메타데이터를 수집하므로 실제 데이터 세트를 Snowflake로 복제할 필요가 없습니다. 커넥터는 기존 데이터 지식을 더 쉽게 찾고 재사용할 수 있게 해 줍니다. 여기에 거버넌스가 적용된 비즈니스 정의가 해석에 필요한 의미를 더합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"blog-title-e4fc09fe63","propertiesId":"ai","type":"heading2","lines":["AI 기반에 비즈니스 전문성 반영"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_11":{"id":"blog-text-2a9375359b","text":"\u003Cp\u003E고객 유지에 대해 잘 아는 사람과 데이터베이스를 관리하는 사람은 다를 수 있습니다. 그러나 고객 상태, 제외 기준, 보고 관례에 대한 이들의 지식은 데이터를 해석하는 데 필수적입니다. 이러한 지식을 기록하기 위해 엔지니어가 모든 설명을 일일이 문서로 옮겨야 해서는 안 됩니다.\u003C/p\u003E\u003Cp\u003E메타데이터 조회 기능은 카탈로그 탐색과 실제 데이터에 대한 액세스를 분리합니다. 적절한 메타데이터 권한이 있으면 사용자는 데이터 자체를 쿼리할 권한 없이도 카탈로그 정보와 데이터 계보를 탐색할 수 있습니다.\u003C/p\u003E\u003Cp\u003E또한 관리자는 새로운 APPLY COMMENT 권한으로 스튜어드에게 오브젝트와 컬럼 설명을 편집하는 데 필요한 권한만 부여할 수 있습니다. MODIFY나 OWNERSHIP 같은 더 광범위한 권한을 부여할 필요가 없습니다.\u003C/p\u003E\u003Cp\u003E비즈니스 전문가는 사용자와 AI 워크플로우가 활용할 컨텍스트를 직접 개선하고, 관리자는 데이터 액세스에 대한 통제권을 유지할 수 있습니다. 추가 설명은 채팅 스레드나 특정 에이전트의 프롬프트에만 머무는 답변이 아니라, 데이터와 함께 재사용할 수 있는 지식이 됩니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_12":{"id":"blog-title-f083f1b6b7","propertiesId":"","type":"heading2","lines":["유용한 컨텍스트 유지 비용 절감"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_13":{"id":"blog-text-9e8a989b26","text":"\u003Cp\u003EAI 프로젝트에는 첫 데모용 문서뿐 아니라 팀이 지속적으로 관리할 수 있는 컨텍스트가 필요합니다. 동일한 고객 식별자가 여러 다운스트림 뷰에 사용될 때, 설명을 반복해서 복사해도 의미가 더해지지는 않고 작업만 늘어납니다. 설명이 누락되거나 일관되지 않으면 분석가와 AI 워크플로우 모두 활용할 수 있는 컨텍스트가 줄어듭니다.\u003C/p\u003E\u003Cp\u003E컬럼 설명 전파 기능은 컬럼 계보를 활용해, 값이 변경 없이 전달되는 경우 지원되는 다운스트림 컬럼으로 설명을 전파합니다. 예를 들어 고객 식별자 컬럼의 설명은 해당 컬럼을 사용하는 다운스트림 뷰로 전달될 수 있으며, 컬럼 이름이 바뀌어도 마찬가지입니다.\u003C/p\u003E\u003Cp\u003E값이 변환, 결합 또는 집계되면 전파가 중단되며, 다운스트림 컬럼에 직접 작성된 설명을 덮어쓰지 않습니다. 팀은 전파된 설명과 사용자가 직접 작성한 설명을 구분할 수 있습니다. 이 기능을 활성화하면 소유자가 의미에 대한 통제권을 유지하면서 반복적인 유지 관리 작업을 줄일 수 있습니다. 따라서 전문가는 판단이 필요한 정의에 집중할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_14":{"id":"blog-title-696adf21f2","propertiesId":"ai","type":"heading2","lines":["비즈니스에 대한 공통의 이해를 AI 워크플로우의 기반으로"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-2547f9edfc","text":"\u003Cp\u003EAI를 활용한 고객 유지율 분석에서 컨텍스트는 여러 역할을 합니다. 설명은 고객 필드가 무엇을 나타내는지 알려줍니다. 데이터 계보는 팀이 데이터의 출처를 확인하는 데 도움을 줍니다. 거버넌스가 적용된 지표 정의는 고객 유지율을 계산하는 방법을 명시합니다. 각 요소는 사람이 평가할 수 있는 답변을 만드는 데 서로 다른 역할을 합니다.\u003C/p\u003E\u003Cp\u003E사용자는 \u003Ca href=\"https://www.snowflake.com/ko/product/features/horizon-context/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHorizon Context\u003C/a\u003E에서 컨텍스트를 탐색할 수 있습니다. Snowflake CoCo는 데이터 탐색과 영향 분석 등의 작업에 카탈로그 컨텍스트를 활용할 수 있습니다. 시맨틱 뷰는 AI와 분석을 위해 거버넌스가 적용된 지표, 차원, 관계의 정의를 제공하여 이 기반을 보완합니다.\u003C/p\u003E\u003Cp\u003E비즈니스 가치는 재사용에 있습니다. 팀은 대시보드, 애플리케이션, 프롬프트마다 지식을 다시 구성하는 대신 공통 지식을 바탕으로 작업할 수 있습니다. 이러한 기반은 더 일관된 해석을 가능하게 하고, AI가 생성한 답변을 검토할 때 확인할 수 있는 근거를 제공합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"blog-title-2e8fcec3b7","propertiesId":"","type":"heading2","lines":["비즈니스 지식을 업무에 활용하기"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_17":{"id":"blog-text-c126fb2ce4","text":"\u003Cp\u003E지원되는 데이터, BI, 파이프라인 시스템을 Horizon Catalog에 연결하세요. 비즈니스 전문가가 설명 관리에 참여하도록 하고, Snowflake CoCo로 데이터 계보를 탐색하고 데이터 문제를 조사하며 변경의 영향을 평가하세요.\u003C/p\u003E\u003Cp\u003E팀이 오늘 축적하는 지식은 앞으로 만들 보고서, 애플리케이션, AI 워크플로우에 재사용할 수 있는 기반이 될 수 있습니다.\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-horizon/connectors\" target=\"_blank\" 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href=\"https://info.snowflake.com/Preference-center.html\"\u003E이메일 수신 설정\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cbutton id=\"ot-sdk-btn\" class=\"ot-sdk-show-settings\"\u003E쿠키 설정\u003C/button\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/ko/legal/privacy/privacy-policy/#12\"\u003E개인 정보를 공유하지 않습니다.\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/ko/legal/\"\u003E법률\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. 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