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aem-GridColumn--default--12"},"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-d97e856760","text":"\u003Cp\u003E많은 사람에게 집 안 책장에 꽂힌 백과사전 전집은 거의 모든 질문의 답을 알려주는 권위 있는 지식의 원천이었습니다. 하지만 그 안에 담긴 정보는 빠르게 시대에 뒤처지곤 했습니다. 예를 들어 명왕성이 더 이상 행성이 아니게 된 뒤에도 오랫동안 아홉 번째 행성으로 등재되어 있거나 정작 찾고 싶은 정보가 빠져 있는 일이 많았습니다. 시간이 흐르면서 Wikipedia가 새로운 백과사전이 되었고, 작동 방식은 정반대였습니다. 처음에는 일부 교사들이 Wikipedia를 믿지 말라고 했지만, Wikipedia는 결국 거의 모든 주제를 포괄하고 최신성을 유지하는 지식의 기준이 되었습니다.\u003C/p\u003E\r\n\u003Cp\u003E엔터프라이즈 데이터는 이러한 전환을 이루지 못했습니다. 기업은 여전히 백과사전을 만들던 방식으로 데이터를 문서화하려고 합니다. 소수의 전문가가 모든 데이터의 의미를 기록하지만 작업은 끝내 마무리되지 않고, 비즈니스가 변화할수록 점점 뒤처집니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake의 제품 데이터 팀은 테이블을 에이전트가 읽고 이해할 수 있도록 하기 위해 시맨틱 뷰를 추가했습니다. 이를 통해 에이전트는 추측하는 대신 선별되고 거버넌스가 적용된 정의를 기반으로 데이터를 읽습니다. 제품 측면에서는 이를 더 빠르게 생성할 수 있도록 \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/autopilot\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESemantic View Autopilot\u003C/a\u003E을 출시했습니다. 이는 분명 도움이 되었지만, 그럼에도 Snowflake가 다룬 범위는 빙산의 일각에 불과했습니다. 9,685개 테이블 중 5% 미만이었습니다. 해당 5% 안에 포함된 데이터에 대해서는 좋은 답변을 얻을 수 있었지만, 사람들이 실제로 묻는 대부분의 질문은 그 범위 밖에 있었습니다. 문서화되지 않은 테이블이나 시맨틱 뷰가 존재하기에는 너무 새로운 데이터에 관한 질문이었습니다. \u003C/p\u003E\r\n\u003Cp\u003E회사가 2주 전에 사용량 기반 요금제를 출시했다고 가정해 보겠습니다. 이를 뒷받침하는 테이블은 생성된 지 며칠밖에 되지 않았습니다. PM이 에이전트에게 \u003Ci\u003E“새 요금제는 출시 이후 어떤 성과를 내고 있지?”\u003C/i\u003E라고 직접 질문하면 시맨틱 뷰에만 의존하는 Cortex Agent는 답변을 거절합니다. 반면 팀이 이미 사용하고 있을 수 있는 다른 에이전트, 예를 들어 Snowflake CoCo, Cursor, ChatGPT는 해당 데이터에 대한 시맨틱 뷰 존재 여부와 관계없이 새 데이터를 직접 쿼리할 수 있습니다. 이 경우 확신에 차 있지만 잘못된 답변으로 이어질 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Sense(프라이빗 프리뷰 예정)는 시맨틱 뷰를 대체하는 것이 아니라 함께 작동하도록 설계되었습니다. 거버넌스가 적용된 일관된 답변이 필요한 경우 시맨틱 뷰는 계속 가장 신뢰할 수 있는 기준으로 남으며, Cortex Sense는 이를 권위 있는 신호로 처리합니다. 나머지 데이터 자산 환경 전반에서는 Cortex Sense가 이러한 이해를 스스로 구축합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-5f483f9095","additionalClasses":"is-vertical","alt":"cortex","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_768250269":{"id":"blog-text-df96f8398a","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003ECortex Sense는 시맨틱 뷰가 제공하는 것과 같은 종류의 이해를 구성하고, 더 나아가 테이블 카탈로그에 그치지 않고 비즈니스 전체에 대한 작동 모델을 구축합니다. 이 작업은 비즈니스가 이미 생성하는 신호를 기반으로 자동 수행됩니다. 예를 들어 분석가들이 과거에 실행한 쿼리, 변환 도구에 정의된 모델, BI에 이미 존재하는 지표가 여기에 포함됩니다. 이러한 컨텍스트의 상당 부분은 \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-horizon\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Horizon Connectors\u003C/a\u003E를 통해 확보됩니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_477562269":{"id":"image-8eebb1785f","alt":"architecture","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_241586153":{"id":"blog-text-b2e78134da","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ECortex Sense는 사용자가 별도로 유지 관리해야 하는 또 하나의 시스템 오브 레코드가 아니라, 에이전트형 쿼리의 정확도 하한선을 높이는 관리형 솔루션입니다.\u003Cbr\u003E\n\u003Cbr\u003E\n기준선을 설정하기 위해 Snowflake는 내부 데이터를 기반으로 테이블 간 조인, 지표 공식 조회, 필터 규칙 지식이 필요한 제품 분석 질문에서 에이전트를 평가했습니다. Snowflake 데이터 팀은 별도의 컨텍스트 계층이 없을 때 AI의 정확도가 약 25%라는 사실을 확인했습니다. 이는 \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude\"\u003EAnthropic\u003C/a\u003E이 독립적으로 측정한 21%라는 결과와 놀라울 만큼 유사했습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_254880255":{"id":"image-22c69e3e37","alt":"sense","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_904305490":{"id":"blog-text-a26e0bc3c7","text":"\u003Cp\u003E\u003Ci\u003E\u003Csub\u003ECortex Sense가 없으면 CoCo는 모든 테이블과 스키마를 각각 조사하며 그 역할을 파악하려고 합니다. Cortex Sense가 있으면 CoCo는 어디를 살펴보고 무엇을 쿼리해야 하는지 정확히 압니다.\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E수작업으로 작성하지 않고 자동으로 마이닝한다는 점에서는 충분히 우려가 제기될 수 있습니다. 아무도 알아차리지 못한 채 확신에 찬 오답을 내놓을 수 있기 때문입니다. Cortex Sense는 자체 지식의 공백과 충돌을 지속적으로 식별하는 자가 수정 루프를 실행해 이러한 문제를 완화합니다. CoCo를 통해 Cortex Sense를 활성화하면 Cortex Sense는 연결된 소스에서 초기 기준선을 구축합니다. 이후에는 목표 지향 평가를 통해 개선됩니다. 검증된 정답이 있는 질문을 입력하면 Cortex Sense는 각 불일치를 활용해 자체 이해를 수정합니다. 이러한 평가 입력은 세 가지 소스에서 제공됩니다. 자체 정답 기준 벤치마크, 사용자 피드백, 커버리지가 부족한 영역에 대한 시스템 자체 제안입니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_750716749":{"id":"image-6e6245c66e","additionalClasses":"is-vertical","alt":"evals","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_855786552":{"id":"blog-text-03c1c16c9f","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003ESnowflake CoCo가 Cortex Sense와 함께 평가 결과를 보여줍니다. (이 그림에 표시된 정보는 가상의 정보이며 실제 고객 또는 Snowflake 데이터를 기반으로 하지 않습니다.)\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E평가에서 공백이 드러나면 Cortex Sense는 추측하는 대신 스스로 근거를 확보합니다. 출시 후 새 요금제의 성과에 대한 질문으로 돌아가 보겠습니다. Cortex Sense가 ‘업그레이드’의 의미에 대해 충돌하는 두 가지 정의를 감지했다면 그 모호성을 자체 지식 안에 그대로 두지 않습니다. Cortex Sense 빌더에게 충돌을 표시하고 사람이 이를 정리하도록 요청합니다. Snowflake는 컨텍스트에 대해 이 정도 수준의 정직성을 강제한다는 점이, 발견된 정보를 단순히 검색해 가져오는 검색 증강 생성(RAG) 같은 시스템과 Cortex Sense를 구분한다고 봅니다. 어떤 이유로든 평가가 실패하거나 충돌하는 정보를 감지하면 Cortex Sense는 그 이유를 되짚고 자체 이해를 업데이트하려고 합니다. 스스로 해결할 수 없으면 도움을 요청합니다. 예를 들어 Snowflake가 Cortex Sense를 내부적으로 테스트하기 시작했을 때 Cortex Sense는 일일 활성 사용자에 대한 수십 가지 정의를 발견했습니다. 각 팀은 이 지표를 계산하는 자체 방식을 가지고 있었기 때문에 Cortex Sense가 물어왔을 때 Snowflake는 어떤 지표가 어떤 팀에 해당하는지만 자연어로 설명하면 됐습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2032658441":{"id":"image-ce8eb33dd1","alt":"conflict","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_430573664":{"id":"blog-text-e8835fe502","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E신호가 서로 충돌하면 Cortex Sense는 웹 검색이 페이지 순위를 매기는 방식처럼 관련성, 권위성, 인기도, 최신성을 기준으로 신호의 우선순위를 정합니다. 거버넌스가 적용된 시맨틱 뷰가 뒷받침하는 지표 정의는 몇 개의 쿼리에서 추론한 정의보다 더 높은 권위를 갖습니다. 이를 테면 500개의 프로덕션 SQL 문에 나타나는 조인 패턴은 세 개의 SQL 문에만 나타나는 패턴보다 더 큰 가중치를 갖습니다. 지난달 업데이트된 정의는 2년 전 정의보다 우선합니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Sense와 관련해 자주 받는 질문 중 하나는 컨텍스트에 대한 액세스를 어떻게 관리할 것인가입니다. Cortex Sense는 실제 데이터 행이 아니라 메타데이터와 사용 패턴만 수집합니다. 하지만 다른 모든 Snowflake 오브젝트와 마찬가지로, 액세스는 Snowflake의 기존 거버넌스를 통해 역할별로 범위가 지정됩니다. 곧 제공될 프라이빗 프리뷰에서는 먼저 사용자가 Cortex Sense 전체에 액세스할 수 있는 단일 역할을 지정하도록 허용할 예정입니다. 이후에는 역할별 컨텍스트로 확장해 마케팅 팀과 재무 팀이 서로 다른 컨텍스트에 액세스할 수 있도록 할 계획입니다.\u003C/p\u003E\r\n\u003Cp\u003E이 시스템을 내부 데이터 사이언티스트에게 처음 공개했을 때, 사람들이 이미 시맨틱 뷰에 투입해 둔 작업을 기반으로 구축한 결과 초기 내부 평가 세트에서 직접 큐레이션한 시맨틱 뷰와 동등한 수준에 도달했습니다. 이후 Cortex Sense는 두 영역에서 앞서 나갔습니다. 정의가 오래되어 최신 상태를 반영하지 못한 영역, 그리고 기존 시맨틱 뷰가 포괄하지 못한 도메인입니다. 해당 세트에서는 최근 쿼리 기록과 같은 신호를 활용해 인간 성능을 10%포인트 앞섰습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Sense가 가치를 더할 수 있는지 검증하기 위해 까다로운 질문 세트에서 벤치마크를 수행했습니다. MCP(Model Context Protocol)를 통해 SQL 실행에 직접 액세스할 수 있는 프런티어 코딩 에이전트, 필요한 경우 관련 시맨틱 뷰를 검색할 수 있는 기본 CoCo, 그리고 Cortex Sense로 그라운딩된 CoCo를 비교했습니다. 벤치마크에서 Cortex Sense는 정확도를 24.1%에서 86.3%로 높였습니다. 또한 프런티어 에이전트와 비교했을 때 Cortex Sense는 에이전트가 모든 테이블을 수동으로 검사하지 않도록 하여 쿼리당 비용을 1.76달러에서 0.59달러로 줄였습니다. 프런티어 에이전트는 수십 개 오브젝트에서 DESCRIBE TABLE을 실행하는 경향이 있어 토큰을 낭비하고 지연 시간을 늘렸습니다. Cortex Sense의 비용 이점은 초기 1회성 인덱싱 비용으로 일부 상쇄됩니다. 이 비용은 인덱싱되는 데이터 규모에 따라 달라집니다. 하지만 일반적으로 사전 인덱싱 비용은 낮아진 쿼리당 비용을 통해 시간이 지나면서 회수할 수 있음을 확인했습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_416045696":{"id":"image-e775c89895","alt":"accuracy","lazyEnabled":true,":type":"snowflake-site/components/image"},"blog_text_131379276":{"id":"blog-text-41055164f6","text":"\u003Cp\u003E\u003Csub\u003E\u003Ci\u003E2026년 6월 내부 테스트 결과 및 비용 계산 기준입니다.\u003C/i\u003E\u003C/sub\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ECortex Sense를 구축하는 데는 일반적으로 이러한 프로젝트에 소요되는 몇 개월이 아니라 단 하루가 걸렸습니다. 기다려야 할 컨설팅 계약이나 수동 구축 작업이 없기 때문입니다. 새 시맨틱 뷰를 작성할 필요가 없으므로 며칠 동안 큐레이션에 시간을 들이지 않고 소스에 연결하기만 하면 바로 시작할 수 있습니다. 지속적인 유지 관리 모델도 다릅니다. 데이터 자산이 발전함에 따라 Cortex Sense는 지속적인 새로 고침 주기에 맞춰 신호를 자동으로 포착합니다. 그리고 지식을 실제로 업데이트하고 싶다면 수정 워크플로우는 대화형입니다. 올바른 정의를 자연어로 설명하면 Cortex Sense가 모델을 업데이트합니다. \u003C/p\u003E\n\u003Cp\u003E백과사전이 Wikipedia로 대체되었을 때 혁신은 사람들이 더 많은 것을 알게 되었다는 데 있지 않았습니다. 지식이 전문가들이 정기적으로 발행하는 대상에서 벗어나 스스로 진화하기 시작했다는 데 있었습니다. 엔터프라이즈 컨텍스트도 같은 전환점에 있습니다. 거버넌스가 적용된 일관된 답변이 필요한 영역에서 시맨틱 뷰는 여전히 최고의 기준이지만, AI가 비즈니스를 학습하는 유일한 방식이어서는 안 됩니다. Cortex Sense는 이미 생성되는 신호에서 학습하고, 비즈니스가 변화함에 따라 계속 개선됩니다.\u003C/p\u003E\n\u003Cp\u003E이 제품을 전 세계에 선보이게 되어 매우 기쁩니다. 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