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Cortex AISQL을 사용하면, 익숙한 SQL 명령을 사용하여 멀티 모달 엔터프라이즈 데이터 전반에 걸쳐 확장 가능한 AI 파이프라인을 구축할 수 있습니다. 텍스트(PuPr), 이미지(PuPr), 오디오(PuPr 예정)를 더 빠르고 비용 효율적으로 처리하여, 정형 데이터와 비정형 데이터 모두에서 보다 심층적인 인사이트를 동시에 얻을 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E데이터 분석은 AI를 기반으로 근본적인 변화를 겪고 있습니다. 오늘날의 엔터프라이즈는 가치 있는 인사이트가 기존의 표 형태 데이터에 국한되지 않는다는 점을 인식하고 있습니다. 그리고 이러한 인사이트는 문서, 이미지, 오디오 파일과 같은 다양한 비정형 소스 속에 포함되어 있는 경우가 많습니다. \u003C/p\u003E\r\n\u003Cp\u003E데이터 다양성이 증가함에 따라, 여러 소스 전반에서 인사이트를 추출하고 종합하여 결합하려면 분석에는 점점 더 정교한 다단계 프로세스가 필요합니다. 하지만 엔터프라이즈 규모로 복합적인 데이터를 분석하는 데에는 상당한 기술상, 그리고 운영상의 과제가 잇따릅니다. \u003C/p\u003E\r\n\u003Cp\u003E조직에서는 이러한 정보를 처리하고 분석하기 위해 다양한 전문 도구와 스킬 세트를 함께 사용하는 데 어려움을 겪고 있으며, 이는 효율성과 확장성을 저해하고 인사이트 도출의 지연과 품질 저하를 야기하곤 합니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 문제를 해결하기 위한 방법으로, Snowflake는 강력한 생성형 AI 기능을 핵심 SQL 엔진에 통합함으로써, 분석가들이 다음을 통해 단기간에 더 높은 성과를 달성하도록 지원합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E표현력이 풍부하고 구성 가능한 AI 연산자\u003C/b\u003E: FILTER 및 AGGREGATE와 같은 기존 SQL 프리미티브에 원활하게 매핑되는 새로운 AI 기반 연산자 세트입니다. 이를 통해, 분석가는 익숙한 SQL 명령을 사용하여 운영 데이터의 미묘한 이상 감지, 고객 통화 녹취록에서 실행 가능한 인사이트 집계, 광범위한 이미지 라이브러리 분류 등을 수행할 수 있는 확장 가능한 AI 파이프라인을 손쉽게 구축할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E멀티 모달 데이터를 위한 네이티브 지원\u003C/b\u003E: 새로운 FILE 데이터 유형 도입을 통해 Snowflake 테이블 내에서 텍스트, 이미지, 오디오를 포함한 멀티 모달 데이터를 직접 참조할 수 있습니다. 모든 AI 연산자는 이러한 다양한 데이터 모달리티 전반에서 원활하게 작동하도록 설계되었으며, 이로 인한 별도의 처리 시스템은 필요하지 않습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E획기적인 성능 및 비용 개선:\u003C/b\u003E Snowflake SQL 엔진에 내장된 네이티브 기능과 성능 최적화를 통해 Cortex AISQL은 처리 시간과 계산 비용을 크게 낮출 수 있습니다. Cortex AISQL 성능 최적화(PrPr)에 대한 내부 벤치마크 결과, FILTER 및 JOIN과 같은 연산의 쿼리 실행 시간을 최대 70%까지 단축했습니다. 이는 수동 구현 방식 대비 비용 절감으로 이어져, AI 확장성 및 엔터프라이즈 도입을 위한 경제적 실효성에 대한 우려를 직접적으로 해소할 수 있었습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E"Snowflake Cortex AISQL로 서비스 기술자 애플리케이션 개발을 가속화하여 기술자들이 수천 여 개의 다국어 사용자 매뉴얼을 활용하여 정보를 얻고 분석할 수 있어 예상했던 것보다 더 빠르게 고객 문제를 해결할 수 있었습니다. Cortex AISQL로 텍스트에서 이미지에 이르는 다양한 데이터 유형을 원활히 통합하는 방식이 저희에겐 근본적인 전환점이 되었습니다. 이를 통해 데이터에서 인사이트를 도출하는 여정을 앞당겨 저희 팀의 작업 방식을 혁신할 수 있었습니다."라고 Toyota Material Handling Europe의 리드 데이터 사이언티스트인 Ahmad Al-Mashahedi가 말했습니다.\u003C/p\u003E\r\n\u003Ch1\u003ECortex AISQL의 핵심 기능\u003C/h1\u003E\r\n\u003Cp\u003ECortex AI SQL을 통해 AI 기능을 SQL 환경에 직접 통합하여 다양한 데이터 유형에 이르는 정교한 다단계 분석이 가능합니다. 다음 섹션에서는 이러한 핵심 역량이 어떻게 성능을 최적화하고 비용을 절감하며 복잡한 분석 워크플로우를 원활히 수행할 수 있는지 살펴보겠습니다.\u003C/p\u003E\r\n\u003Ch3\u003ECortex AISQL을 활용한 다양한 데이터용 통합 AI 작업\u003C/h3\u003E\r\n\u003Cp\u003ECortex AISQL의 핵심 강점은 사용자가 익숙한 SQL 환경 내에서 여러 모달리티에 걸쳐 정교한 AI 파이프라인을 직접 구축할 수 있는 데 있습니다. 이러한 통합은 고객의 데이터 스택과 AI 스택 간의 분리를 제거하고, 광범위한 데이터에 정교한 분석을 위한 통합 플랫폼을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex AISQL은 AI 연산자를 Snowflake 생태계 내에 완전히 내장된 네이티브 SQL 프리미티브로 도입합니다. 그 예는 다음과 같습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003EAI_FILTER는 WHERE 절 내에서 AI 기반 필터링 로직을 직접 적용합니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E이제 JOIN 작업은 단순히 사전 정의된 키가 아닌 AI 관계를 기반으로 수행될 수 있습니다. 이를 통해, 시맨틱 이해를 기반으로 다양한 데이터 형식 간의 연결이 가능합니다(예: AI의 적합도 평가를 기반으로 이력서와 직무 설명을 조인). \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E선도적인 AI 기반 집계 연산자 AI_AGG는 다양한 데이터 행에서 복합적인 인사이트를 도출하기 위해 GROUP BY와 함께 작동하도록 설계되었습니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E향상된 AI_CLASSIFY는 텍스트와 이미지 모두에서 멀티 레이블 분류를 지원할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-6984e628b0","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b5888b22-4a2c-4508-b18d-b5a2fe33508a/screenshot-2025-05-30-at-8.32.29%25E2%2580%25AFam.png?quality=85&preferwebp=true","alt":"Figure 1: AI capability meets SQL primitives.","width":"2052","title":"그림 1: AI 역량과 SQL 프리미티브의 결합.","height":"990",":type":"snowflake-site/components/image"},"blog_text_1663165861":{"id":"blog-text-fea2ce8109","text":"\u003Cp\u003ECortex AISQL은 주소 지정을 통한 처리 가능 데이터 영역을 대폭 확장합니다. 모든 새로운 연산자는 통합 연산자를 통해 텍스트, 이미지, 그리고 곧 출시될 오디오 데이터에 걸쳐 효율적으로 작동하도록 설계되었습니다. 추가적인 서비스가 전혀 필요하지 않으므로, 모든 유형의 데이터를 다룰 수 있는 AI 슈퍼히어로와도 같이 데이터 분석가들의 역량이 향상됩니다.\u003C/p\u003E\n\u003Ch3\u003E심층 인사이트 확보: AI 파이프라인을 활용한 고급 다단계 분석\u003C/h3\u003E\n\u003Cp\u003EAI 역량은 분석의 새로운 차원을 열어주기에, 단순한 조회를 넘어 점점 더 복잡해지는 다단계 질문 처리 기능을 제공합니다. 검색 증강 생성(RAG)은 포인트 룩업 작업에 탁월한 반면, 보다 심층적인 분석 문제에서는 인사이트를 종합하려면 여러 AI 기반 단계를 오케스트레이션해야 합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-c66fbc9da6","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"다음 질문에서 요구하는 분석적 깊이를 고려해 보세요. \"최근 CEO가 교체된 재생 에너지 분야 운영 기업들의 연간 수익 성장률과 시장 전망은 어떠한가?\"","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_102841155":{"id":"blog-text-998aa9d2e3","text":"\u003Cp\u003E다음은 이를 해결하기 위한 방법입니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E대규모 문서 컬렉션을 \u003Ci\u003E검색\u003C/i\u003E하여 관련 정보를 식별합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E관련 문서 섹션에서 CEO 변경 사항 및 해당 조직의 산업 분야(재생 에너지)와 같은 주요 기준을 \u003Ci\u003E필터링\u003C/i\u003E합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E비정형 데이터에서 수익 수치 및 전망 진술과 같은 주요 정보를 \u003Ci\u003E추출\u003C/i\u003E합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E비정형 인사이트를 정형 데이터와 \u003Ci\u003E조인\u003C/i\u003E하여 전년 대비 수익 성장률을 계산합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E결과를 명확하고 일관된 요약으로 \u003Ci\u003E종합\u003C/i\u003E하여 추세와 미래 전망에 대한 심층적인 이해를 제공합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ECortex AISQL은 조합 가능한 AI 연산자와 기본 SQL 통합을 통해 이러한 복잡한 분석 워크플로우를 구현합니다. 분석가가 익숙한 SQL 구문 내에서 문서 필터링부터 시맨틱 추출, 지능형 조인에 이르기까지 AI 기반 연산을 서로 연결하여 여러 전문 도구 및 사용자 지정 코드의 필요성을 낮춥니다. 이와 같은 통합 접근 방식으로, 기존이라면 데이터 사이언스 전문 지식이나 개발 시간이 몇 주 가량 필요했을 작업을 비즈니스 분석가가 단 몇 분 만에 구축하고 수정할 수 있는 간단한 SQL 쿼리로 변환할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1865652061":{"id":"image-a386658be7","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--2788b5b5-c0e8-4e29-b576-ac0d9e7303f9/screenshot-2025-05-29-at-3.34.03%25E2%2580%25AFpm.png?quality=85&preferwebp=true","alt":"Figure 2: Building an AI pipeline using Cortex AISQL.","width":"1976","title":"그림 2: Cortex AISQL을 사용한 AI 파이프라인 구축.","height":"1164",":type":"snowflake-site/components/image"},"blog_text_246277908":{"id":"blog-text-610c85ad75","text":"\u003Cp\u003E앞으로는 이러한 유형의 복잡한 분석 질문이 비즈니스 환경에서 점점 더 늘어날 것입니다. 조직은 의미 있는 인사이트를 추출하기 위해 정형 데이터 및 비정형 데이터에 걸쳐 있는 여러 분석 패턴을 결합해야 합니다. Cortex AISQL를 통해 분석가 입장에서는 정교한 AI 파이프라인을 생성하여 이러한 증대되는 필요성을 해소하며, 팀은 SQL 네이티브 기능으로 복합적인 질문에 답변할 수 있도록 역량을 강화합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_1684898516":{"id":"quote-item-ac6ddd3ee0","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"\"수십 년 동안 BI 도구들은 데이터가 정제되고 구조화되어 있다는 전제로 개발되어 왔습니다. 하지만 실제 의사 결정은 PDF, 이미지, 영수증 같은 정리되지 않은 비정형 콘텐츠에 의존합니다. Cortex AISQL은 이러한 병목 현상을 제거하고, Sigma 및 Snowflake와 결합하여 인간의 전문 지식을 확장 가능하고 지능적인 분석으로 전환하고 있습니다.”라고 Sigma CEO인 Mike Palmer는 말합니다.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1459449202":{"id":"blog-text-17e647773b","text":"\u003Ch3\u003E정확성을 유지하며 쿼리 시간 및 비용 최적화\u003C/h3\u003E\r\n\u003Cp\u003E엔터프라이즈 AI의 경우, 처리 시간 및 비용 절감은 매우 중요한 사안입니다. AI 함수를 Snowflake의 핵심 쿼리 엔진에서 실행함으로써, AI 및 정형 데이터 처리를 한 곳에서 결합합니다. 즉, Snowflake 웨어하우스의 병렬성 및 배치를 활용하여 이 둘을 동시에 최적화합니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 확장성은 실제 엔터프라이즈 사용 사례에 필수적입니다. "샌프란시스코에서 10마일 이내, 예상하는 가격 범위 내에서 현대적이고 매력적인 주택을 찾아줘"와 같은 작업이 있다고 가정해 봅니다. Snowflake 시스템에서는 여러 WHERE 절에 걸쳐 실행을 지능적으로 계획합니다. 먼저 가격 및 거리에 대한 표준 필터를 적용하여 데이터 세트를 좁힌 후, 시각적 언어 모델을 사용하여 "현대적이고 매력적인" 특성을 평가하여 최대 효율성을 보장하게 됩니다. \u003C/p\u003E\r\n\u003Cp\u003ECortex AISQL의 핵심은 인사이트의 정확도를 유지하면서도 탁월한 성능 및 비용 효율성을 갖춘 강력한 AI 역량을 제공하는 데 있습니다. 적응형 LLM 최적화(PrPr)를 전략적으로 구현하여 더 간단한 작업의 경우에는 소형 모델을 오케스트레이션하는 동시에, 더 복잡한 분석의 경우에는 대형 LLM을 할당합니다. 이러한 계층화된 접근 방식을 통해 고품질의 결과를 유지하면서도 최적의 리소스 활용도를 보장합니다. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_188602535":{"id":"image-eef0ac3057","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--795e0e88-d318-4fb5-9c20-0383aab6be05/screenshot-2025-05-29-at-3.34.38%25E2%2580%25AFpm.png?quality=85&preferwebp=true","alt":"Figure 3: Cortex AISQL offers world-class performance on AI queries.","width":"1748","title":"그림 3: AI 쿼리에서 세계 최고 수준의 성능을 보여주는 Cortex AISQL.","height":"1084",":type":"snowflake-site/components/image"},"title_v2":{"id":"blog-title-8e3b5ba42d","propertiesId":"cortex-aisql","type":"heading4","lines":["Cortex AISQL, 산업 워크플로우에 통합 데이터 분석 기능 제공"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1964063270":{"id":"blog-text-5d02a3a030","text":"\u003Cp\u003ECortex AISQL은 금융 서비스에서 리테일, 그리고 헬스케어 산업에 이르기까지 새로운 인사이트를 확보하고 정형 데이터 및 비정형 데이터 모두에서 복잡한 프로세스를 자동화할 수 있도록 지원합니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E금융 서비스:\u003C/b\u003E 자본 시장 기관들은 이제 전례 없는 효율성으로 복잡한 기업 활동 처리를 자동화할 수 있습니다. 분석가는 방대한 뉴스 및 규제 문서에 AI_FILTER를 적용하여 관련 이벤트 공지사항을 즉시 식별할 수 있습니다. 이러한 결과는 AI 기반의 조직 이름 및 식별자 지능형 매칭을 통해 내부 보유 현황과 원활하게 연결됩니다. 이로써 정확한 영향 평가를 지원하고 이전에는 대규모로 불가능했던 다음 단계 조치에 대한 정보를 제공할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E리테일 및 전자상거래: \u003C/b\u003E고객 경험 팀에서는 AI_AGG를 사용하여 SKU별로 수천 건의 고객 리뷰를 그룹화하고 분석하여, 실시간으로 발생하는 신제품 품질 문제를 감지할 수 있습니다. 시스템에서 AI_FILTER를 적용하여 부정적인 감정의 급증 또는 특정 결함 보고와 같이 우려되는 패턴을 보이는 제품에 플래그를 지정함으로써, 브랜드 평판 훼손이나 비용이 많이 드는 리콜이 발생하기 전에 품질 보증 팀에서 잠재적인 문제에 신속하게 대응하도록 할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E헬스케어:\u003C/b\u003E 의료 연구자는 임상 기록의 비정형 데이터와 환자 기록의 정형 데이터를 연결하는 AI 기반 분석을 통해 발견을 가속화합니다. 연구자들은 의사 기록에 AI_FILTER를 적용하여 표준 진단 코드로 포착되지 않고 특정 증상 패턴을 보이는 환자를 식별할 수 있습니다. 해당 인사이트는 이후 JOIN을 사용하여 검사 결과 및 투약 기록과 조인하여, 새로운 치료 경로 또는 이전에 인지되지 않은 위험 요소를 보여주는 미묘한 상관 관계를 밝혀낼 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E이와 같은 사용 사례를 통해 더디고 수동적인 프로세스를 고도로 효율적이고 지능적인 워크플로우로 변환하는 Cortex AISQL의 작동 방식을 살펴볼 수 있습니다. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_776461705":{"id":"quote-item-95193e64ea","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"“LLM을 SQL 쿼리에 직접 통합하는 일은 분석을 근본적으로 바꾸는 혁신이었습니다. 복잡한 자연어 처리(NLP) 또는 비전 모델에 필요했던 작업을 이제는 단 한 줄의 SQL로 처리할 수 있습니다. 고객들이 더 심도 있는 질문을 던지고, 이전에는 미처 파악하지 못한 인사이트를 발견하는 모습을 확인할 수 있어 기쁩니다.”\r\n\r\n—Armin Efendic, Hex 리드 파트너 엔지니어","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1166111163":{"id":"blog-text-9bbf7e9233","text":"\u003Ch3\u003ECortex AISQL 가용성\u003C/h3\u003E\r\n\u003Ctable\u003E\r\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E연산자\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E텍스트\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E멀티 모달\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_COMPLETE\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_FILTER\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_CLASSIFY\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_EMBED\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr 예정\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr 예정\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_SIMILARITY\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_FILTER Adaptive Optimization\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPrPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E-\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_AGG\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E-\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_SUMMARIZE_AGG\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E-\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EAI_TRANSCRIBE\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E-\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPuPr 예정\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Ch3\u003E오늘 시작하는 데이터 분석의 혁신\u003C/h3\u003E\r\n\u003Cp\u003ECortex AISQL은 비즈니스가 데이터 인사이트로 전환되는 방식을 재정의하고 있습니다. Snowflake는 \u003Ca href=\"https://www.snowflake.com/ko/blog/openflow-revolutionizes-data-movement-ai/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EOpenflow를 통해\u003C/a\u003E 모달리티 전반의 모든 고객 데이터 소스에 쉽게 접근하고, AI 기반 SQL 연산자를 활용하여 정형 및 비정형 데이터를 함께 분석하는 작업을 그 어느 때보다 원활히 구현하고 있습니다. Cortex AISQL을 통해 수동으로 오케스트레이션되는 AI 파이프라인보다 저렴한 비용으로 고성능 처리가 가능하여 Snowflake의 핵심 강점인 보안 및 거버넌스 기능을 유지하면서도 엔터프라이즈 전반에서 신뢰할 수 있는 인사이트를 확보할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex AISQL의 PuPr은 이제 모든 Snowflake 고객에게 제공됩니다. 지금 바로 시작하여 이러한 강력한 기능이 데이터 분석을 어떻게 혁신할 수 있는지 경험해 보세요. \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1671605061":{"id":"blog-text-e87ef82025","text":"\u003Cp\u003E\u003Ci\u003E미래 전망 진술\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E본 페이지는 당사의 향후 제품 제공 관련 내용을 포함한 미래 전망 진술을 제공하지만, 제품 제공에 대한 보장을 의미하는 것은 아닙니다. 실제 결과 및 제공 내용은 다를 수 있으며, 해당 내용은 알려져 있거나 혹은 알려지지 않은 위험 및 불확실성의 영향을 받을 수 있습니다. 자세한 내용은 최신 10-Q를 참조하세요.\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1663165861","quote_item","blog_text_102841155","image_1865652061","blog_text_246277908","quote_item_1684898516","blog_text_1459449202","image_188602535","title_v2","blog_text_1964063270","quote_item_776461705","blog_text_1166111163","blog_text_1671605061"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-46b0c5573c",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"author_chip":{"id":"author-chip-7b9c07cd65","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-d0f55878b0","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","alt":"Arun Agarwal","width":"720","height":"653",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e1a50739d9","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Arun Agarwal"},"authorTitle":"AI 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