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다운로드합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EODBC 커넥터\u003C/strong\u003E: 스프레드시트 사용자는 SQL로 데이터에 연결할 수 있도록 데이터 팀에 ODBC 드라이버 구성을 요청합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E서드 파티 커넥터\u003C/strong\u003E: 스프레드시트 사용자는 데이터 연결용 별도 시스템을 프로비저닝하고 유지 관리해 달라고 IT 팀에 요청합니다.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E수동으로 내보낸 데이터는 원본과 연결이 끊긴 사본이 되어 데이터 사일로를 만듭니다. 관리 팀 입장에서는 곳곳에 흩어진 데이터 전반에 거버넌스를 적용하기가 어려워집니다. 비즈니스 사용자 입장에서는 분석 결과가 제각각이 되고, 각자 고립된 채 작업하게 됩니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"blog-title-b20e9b68f9","propertiesId":"ai","type":"heading3","lines":["업무 생산성을 위해 이제는 당연해진 AI"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_6":{"id":"blog-text-ec0060d49b","text":"\u003Cp\u003E무엇보다 수동 데이터 커넥터의 한계 때문에 스프레드시트에 AI를 통합하기가 어렵습니다. 코딩용 통합 개발 환경(IDE) 등 다른 인터페이스와 마찬가지로, 스프레드시트에서도 에이전트가 기본 작업 방식이 될 것으로 예상합니다. AI는 데이터 가져오기와 정리, 복잡한 수식 작성, 모델 생성처럼 워크북 구축에서 반복적인 부분을 처리해 분석가가 분석 자체에 집중할 수 있도록 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_7":{"id":"image-194d6330c2","alt":"Diagram showing three methods to export data from Snowflake to spreadsheets with governance trade-offs","isLcpImage":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--181a553f-403d-4711-87a3-bc8ad6f0163d/three-methods-to-export-data-from-snowflake-to-spreadsheets.png?quality=85&preferwebp=true","height":"1028","lazyEnabled":true,"width":"1999","title":"자료 1: 기존의 내보내기 방식과 데이터 커넥터는 사용하기 까다롭고 AI와 통합하기도 어렵습니다.",":type":"snowflake-site/components/image"},"blog_component_8":{"id":"blog-title-9519ec964c","propertiesId":"snowflake-ai","type":"heading2","lines":["Snowflake: 이제 스프레드시트에서 만나는 AI 기반 "],":type":"snowflake-site/components/blog/blog-title"},"blog_component_ins":{"id":"wistia-video-cb614b3fb2","title":{"id":"title","type":"heading1","lines":["스프레드시트에 거버넌스가 적용된 AI 구현: Microsoft Excel 및 Google Sheets용 Snowflake CoCo 발표"],":type":"snowflake-site/components/title"},"wistiaVideoId":"9u5hsiejs8","showVideoLength":false,"showRewatchButton":false,":type":"snowflake-site/components/wistia-video"},"blog_component_9":{"id":"blog-text-a4cf852df8","text":"\u003Cp\u003E자료 2: CoCo가 Excel에서 Snowflake 데이터를 사용해 차트, 피벗 테이블 등을 포함한 차입매수 모델을 구축하며 재무 작업을 에이전트 방식으로 수행하는 모습(4배속).\u003C/p\u003E\n\u003Cp\u003EMicrosoft Excel 및 Google Sheets용 Snowflake CoCo를 사용하면 분석가가 완전히 새로운 도구를 배울 필요 없이, 매일 쓰는 스프레드시트에서 Snowflake를 활용할 수 있습니다. CoCo는 커넥터 없이, 사용자의 권한에 따라 Snowflake 데이터에 대한 쿼리를 생성하고 실행합니다. 그런 다음 셀을 컨텍스트로 활용해 네이티브 스프레드시트 오브젝트를 만들고 워크북을 구축합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"blog-title-662024c5bc","propertiesId":"snowflake","type":"heading2","lines":["거버넌스가 적용된 라이브 Snowflake 데이터와 지능형 새로 고침"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_11":{"id":"blog-text-36bbc885a8","text":"\u003Cp\u003EExcel 및 Sheets용 CoCo는 \u003Ca href=\"https://www.snowflake.com/ko/blog/governed-ai-enterprise-controls-snowflake-coco/\"\u003E엔터프라이즈 거버넌스를 유지\u003C/a\u003E하고 컨텍스트를 보존하면서 라이브 Snowflake 데이터를 스프레드시트에 직접 제공합니다. 사용자가 로그인하면 CoCo는 선택한 Snowflake 역할 또는 기본 역할을 기준으로 액세스를 제한합니다. \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/rbac-simplified-inherited-grants-access-requests/\"\u003ESnowflake 액세스 제어\u003C/a\u003E와 마스킹 및 행 액세스 정책이 적용된 결과가 스프레드시트에 기록됩니다. 이러한 데이터 및 AI 쿼리에는 Semantic Views, Business Ontology, Snowflake Cortex Sense를 비롯한 모든 Snowflake 컨텍스트가 활용됩니다. 이러한 컨텍스트는 분석가가 일관되고 정확한 결과를 얻도록 설계되었으며, 이는 AI로 신뢰할 수 있는 분석을 수행할 때 특히 중요합니다.\u003C/p\u003E\u003Cp\u003E데이터를 스프레드시트로 가져온 뒤에는 지능형 새로 고침으로 데이터를 손쉽게 최신 상태로 유지할 수 있습니다. CoCo가 데이터를 가져오기 위해 쿼리를 실행하면 쿼리 텍스트가 워크북 설정에 저장됩니다. CoCo는 데이터를 새로 고칠 때 결과 크기가 달라지면 이를 반영해 워크북을 지능적으로 업데이트할 수 있습니다. 사용자는 두 가지 방식으로 새로 고침을 실행할 수 있습니다.\u003C/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E수동\u003C/strong\u003E: 버튼 또는 \u003Ccode\u003E/refresh\u003C/code\u003E 스킬을 통해 실행합니다. 분석가가 최신 데이터로 모델을 업데이트할 때 사용하는 방식입니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E자동\u003C/strong\u003E: 워크북을 열 때 실행됩니다. 임원이 별도의 클릭이나 구성 없이 워크북을 열기만 해도 항상 최신 데이터를 확인할 수 있도록 하기 위한 방식입니다. 자동 새로 고침은 CoCo for Excel에서만 지원됩니다.\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003E결과 크기가 달라지거나 SELECT * 문에서 반환되는 컬럼 집합이 달라지는 등 쿼리가 변경되면 CoCo는 기존 데이터, 수식, 자산이 유지되도록 워크북을 업데이트합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_12":{"id":"image-b278197688","alt":"Diagram showing before and after states of a data refresh workflow with regional heatmaps and three steps: first pull, workbook saved query, and rerun query","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--dddf803f-0211-44d9-b205-91af3be64850/before-and-after-states-of-a-data-refresh-workflow-with.png?quality=85&preferwebp=true","height":"800","lazyEnabled":true,"width":"1999","title":"자료 3: CoCo가 저장된 쿼리로 최신 데이터를 가져와 워크북을 새로 고침합니다.",":type":"snowflake-site/components/image"},"blog_component_13":{"id":"blog-text-d4c3869b0c","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E모든 쿼리가 워크북에 저장되므로 사용자는 분석에 어떤 데이터 자산이 사용되었는지 명확히 확인할 수 있습니다. 따라서 워크북마다 다른 결과를 쉽게 대조하고, 마침내 어떤 숫자를 믿어야 하는지에 대한 답을 얻을 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_14":{"id":"blog-title-12971b7d7f","propertiesId":"coco-ai","type":"heading2","lines":["워크북 안의 CoCo로 가능해진 자연어 분석, 에이전틱 조작, AI 함수"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-3f98d04f69","text":"\u003Cp\u003ECoCo는 챗봇 통합과 데이터 커넥터를 넘어, 스프레드시트를 직접 조작하는 에이전틱 워크플로우를 제공합니다. CoCo는 사이드 패널에서 질문에 답하는 대신, 수식과 오브젝트를 사용해 스프레드시트 안에서 네이티브 방식으로 결과를 생성합니다. Excel에서는 \u003Ccode\u003ECORTEX.CLASSIFY\u003C/code\u003E, \u003Ccode\u003ECORTEX.SUMMARIZE\u003C/code\u003E 등 지원되는 Snowflake Cortex AI 함수를 스프레드시트 셀에서 직접 호출할 수 있습니다.\u003C/p\u003E\u003Cp\u003E예를 들어 CoCo에 매출 데이터를 기반으로 지역별 분기 매출과 성장 전망을 보여 달라고 요청할 수 있습니다. 그러면 CoCo가 몇 초 만에 이 요청을 수식과 피벗 테이블까지 갖춘 분석 구축 계획으로 바꿉니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"image-35fe21aef7","alt":"Excel spreadsheet showing ARR Bridge analysis with formulas, clustered column chart, pivot table, and AI assistant panel","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--534b9e3e-7ca2-4837-8cd3-34d7ba998dd9/excel-spreadsheet-showing-arr-bridge-analysis-with-formulas.png?quality=85&preferwebp=true","height":"1231","lazyEnabled":true,"width":"1999","title":"자료 4: CoCo가 차트, 피벗 테이블, 수식을 만들어 시맨틱 뷰의 샘플 매출 데이터를 분석합니다.",":type":"snowflake-site/components/image"},"blog_component_17":{"id":"blog-text-16f382ff2a","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E이러한 에이전틱 기능은 스프레드시트 사용자가 만드는 핵심 분석 오브젝트와 함께, 분석가가 일상적으로 작업하는 환경으로 확장됩니다. CoCo는 사용자의 스킬, 워크북, 데이터를 컨텍스트로 활용할 수 있어 분석가가 직접 하듯 워크북을 구축할 수 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E시각화\u003C/b\u003E: CoCo는 데이터의 형태를 분석해 가장 효과적인 차트 유형을 선택합니다. 차트는 실시간으로 연동되고 인터랙티브하게 작동하며, 스프레드시트와 연결되어 있어 손쉽게 편집할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E피벗 테이블:\u003C/b\u003E CoCo는 데이터 모델링과 집계처럼 부하가 큰 작업을 Snowflake 컴퓨팅으로 처리해 피벗을 구축합니다. 소스 데이터가 Excel의 100만 행 제한이나 Sheets의 2,000만 셀 제한을 넘는 경우에도 마찬가지입니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E서식 지정 및 유효성 검사:\u003C/b\u003E CoCo는 조건부 서식, 정확한 숫자 서식, 데이터 유효성 검사 규칙을 자동으로 적용합니다. 이러한 설정은 사용자의 워크플로우와 업무 방식에 특화된 스킬로 사용자 지정할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EThe Chefs’ Warehouse는 CoCo for Excel의 에이전틱 기능 덕분에 복잡한 표 기반 데이터를 쉽고 직관적으로 처리할 수 있었다고 평가했습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_ins_b":{"id":"quote-item-8a5a4cebb5","alignment":"left","layout":"card","quoteSourceName":"Matt Wyman","quoteSourceTitle":"Vice President of Information Technology, The Chefs’ Warehouse","showQuoteIcon":true,"quote":"CoCo for Excel의 성능과 기능은 인상적입니다. 저희 데이터에는 복잡한 테이블이 많은데, CoCo는 테이블 간 관계와 구조를 이해해 올바른 조인을 실행하고 워크북을 구축합니다. 무엇보다 직관적이어서 분석가들이 별도의 교육 없이도 사용할 수 있습니다.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_18":{"id":"blog-title-3e38fc9604","propertiesId":"snowflake","type":"heading2","lines":["컨텍스트를 포함한 Snowflake 데이터 플랫폼과의 네이티브 통합"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_19":{"id":"blog-text-6da7b88600","text":"\u003Cp\u003ECoCo는 Semantic Views, Business Ontology, Cortex Sense를 포함해 Snowflake의 컨텍스트와 긴밀하게 통합됩니다. CoCo는 Snowflake 컨텍스트를 바탕으로 기업의 Business Ontology에 근거해 분석하므로 엔터티 간 관계와 정의를 이해하고, Semantic Views로 개념과 데이터를 연결합니다. 또한 CoCo는 Cortex Sense와 통합되어 데이터 자산의 롱테일을 자동으로 포괄하며, 이는 특히 애드혹 분석에 유용합니다. 이미 많은 팀이 AI 및 BI 애플리케이션을 위한 컨텍스트를 큐레이션하고 있으며, 이제 이 컨텍스트가 Excel과 Sheets로 직접 확장됩니다.\u003C/p\u003E\u003Cp\u003E또한 CoCo는 부하가 크고 복잡한 계산을 Snowflake의 확장 가능한 가상 웨어하우스로 오프로드할 수 있습니다. 기존에는 대규모 데이터 소스를 다루려면 사용자가 데이터 팀에 맞춤형 데이터 제품을 큐레이션할 파이프라인 구축을 요청해야 했습니다. CoCo for Excel 및 Sheets를 사용하면 복잡한 쿼리가 클라이언트의 스프레드시트 인터페이스가 아닌 Snowflake 웨어하우스에서 실행됩니다. 이를 통해 스프레드시트 인터페이스가 담거나 계산할 수 있는 범위를 넘어서는 작업도 처리할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_20":{"id":"blog-title-e88da8eb40","propertiesId":"snowflake-coco","type":"heading2","lines":["Snowflake 데이터 기반 스프레드시트 작업을 위해 설계된 CoCo"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_21":{"id":"blog-text-5791c5455f","text":"\u003Cp\u003ECoCo는 \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/data-eng-bench-data-engineering-agent-benchmark/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E효율적인 데이터 작업을 위해 특별히 설계\u003C/a\u003E되었습니다. 이 기반을 Excel과 Sheets로 확장해 워크북 검사, 수식 작성, 셀 서식 지정, 차트 및 피벗 테이블 생성을 위한 도구를 제공했습니다.\u003C/p\u003E\u003Cp\u003E하지만 모델링 작업은 워크플로우의 일부에 불과합니다. 비즈니스 질문에 답하려면 기반 데이터에 액세스하고 이를 이해하는 과정도 필요합니다. CoCo는 Snowflake 오브젝트 검색 및 시맨틱 정보 액세스 기능을 워크북 전용 도구와 결합해, 사용자가 질문에서 출발해 관련 데이터와 분석에 이르도록 돕습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_ins_d":{"id":"quote-item-74d33a556a","alignment":"left","layout":"card","quoteSourceName":"Kate Savina","quoteSourceTitle":"Head of Data Technology, GTCR LLC","showQuoteIcon":true,"quote":"스프레드시트에서 쓸 수 있는 AI 도구는 많지만, CoCo for Excel은 저희 업무에 맞게 만들어졌고 저희 데이터에서 바로 시작합니다. 데이터를 내보내거나 도구를 오갈 필요가 없습니다. 정확한 답을 더 빨리 얻을 수 있고, 팀이 보고서를 다시 만드는 데 쓰는 시간도 줄어듭니다.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_ins_c":{"id":"blog-text-dd7e937470","text":"\u003Cp\u003E엔드투엔드 스프레드시트 워크플로우를 평가하기 위해 추론, 모델링, Snowflake 데이터 가져오기가 포함된 Excel의 일반적인 스프레드시트 태스크를 내부적으로 테스트했습니다.\u003Csup id=\"footnote-ref-1\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003E CoCo for Excel과, Snowflake MCP를 사용하는 Excel용 AI 모델 하네스를 대상으로 세 차례 시험을 진행해 정확도, 지연 시간, 태스크당 출력 토큰의 세 가지 차원에서 성능을 측정했습니다. 이러한 태스크에서 두 애드인은 유사한 정확도를 보였지만, CoCo for Excel은 지연 시간이 더 짧고 출력 토큰 사용량도 더 적었습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_22":{"id":"image-d1c0954737","alt":"Scatter plot comparing CoCo for Excel vs AI model harness performance across three models, showing speed and token efficiency","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--b2e41e18-ceb9-4704-abb5-23b4c0ab3d81/scatter-plot-comparing-coco-for-excel-vs-ai-model-harness.png?quality=85&preferwebp=true","height":"1000","lazyEnabled":true,"width":"1999","title":"자료 5: 자체 테스트 결과, 동일한 모델을 사용했을 때 CoCo for Excel은 Snowflake MCP를 사용하는 Excel용 AI 모델 하네스와 비슷한 정확도를 보이면서도 더 빠르고 토큰을 더 적게 사용했습니다.",":type":"snowflake-site/components/image"},"blog_component_23":{"id":"blog-text-b769958a07","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ECoCo for Excel은 오브젝트 검색, 이해, 확장성을 포함해 데이터 작업에 최적화되어 있습니다. CoCo는 direct-to-sheet 데이터 전송을 오케스트레이션해 모델의 컨텍스트 창을 사용하지 않고 쿼리 결과를 워크북으로 직접 스트리밍합니다. 이 아키텍처는 Snowflake의 컴퓨팅 기능과 자산 통합을 활용해 대규모 데이터 조작과 분석을 수행하도록 설계되었습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_24":{"id":"image-47ffdd6f3e","alt":"Line chart comparing task completion time versus rows written for CoCo Excel and AI model harness across three models","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--2e68dd07-62d8-4da4-97d3-159b7d72719d/line-chart-comparing-task-completion-time-versus-rows.png?quality=85&preferwebp=true","height":"1000","lazyEnabled":true,"width":"1999","title":"자료 6: 자체 테스트 결과, Snowflake MCP를 사용하는 Excel용 AI 모델 하네스와 달리 CoCo for Excel은 데이터 크기가 커져도 성능이 거의 일정하게 유지되었습니다.",":type":"snowflake-site/components/image"},"blog_component_25":{"id":"blog-text-afd1647911","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E이러한 내부 결과는 데이터 팀을 위한 코딩 에이전트라는 CoCo의 설계 목표를 보여 줍니다. 정확도는 유지하면서 지연 시간을 개선하고 태스크당 출력 토큰을 줄이는 것입니다. 데이터 컨텍스트가 분석의 근거가 되고, 최적화된 데이터 전송은 분석 결과를 내는 데 필요한 모델 작업을 줄입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_26":{"id":"blog-title-e529d767f6","propertiesId":"excel-sheets-snowflake-coco","type":"heading2","lines":["Excel 및 Sheets용 Snowflake CoCo를 지금 바로 사용해 보기"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_27":{"id":"blog-text-84ba0038cd","text":"\u003Cp\u003E이제 스프레드시트 사용자는 Microsoft Excel 및 Google Sheets용 Snowflake CoCo를 통해 더 많은 인사이트를 발견하고 더 심층적인 분석을 수행할 수 있습니다. Snowflake CoCo는 스프레드시트를 위한 쉽고 신뢰할 수 있으며 효율적인 AI입니다. 두 제품 모두 현재 퍼블릭 프리뷰로 제공됩니다. 시작하려면 다음 단계를 따르세요.\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E설명서를 참고해 \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-excel-integration\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EExcel\u003C/a\u003E용 CoCo 또는 \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-sheets-integration\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESheets\u003C/a\u003E용 CoCo를 설치합니다.\u003C/li\u003E\n\u003Cli\u003ESnowflake 계정에 로그인해 분석을 시작합니다.\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003EHorizon Catalog에 스프레드시트 계보 추가, 시각화 내보내기 등 앞으로 선보일 기능 업데이트도 기대해 주세요.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E본 게시물에는 당사의 향후 제품 제공에 대한 미래 전망 진술이 포함되어 있으며, 이는 제품 제공에 대한 보장이 아닙니다. 실제 결과 및 제공 내용은 달라질 수 있으며, 알려진 위험과 알려지지 않은 위험 및 불확실성의 영향을 받을 수 있습니다. 보다 자세한 내용은 최신 10-Q를 참조하세요.\u003C/i\u003E\u003C/p\u003E\n\u003Chr\u003E\n\n\u003Col data-footnotes=\"true\"\u003E\n\u003Cli id=\"footnote-1\"\u003E 모델 및 하네스 전반의 내부 테스트는 Excel for Web을 사용해 2026년 10월 26일부터 2026년 10월 28일까지 실행되었습니다. 이 결과에는 두 애드인에서 Opus 5.5, Sonnet 5.5, Haiku 5.5로 25개 스프레드시트 태스크를 세 차례씩 실행한 내용이 반영되어 있습니다. 자료 5는 시험 전반에 걸친 태스크당 소요 시간(초)의 중앙값을 보여 줍니다. 자료 6에서는 실패했거나 시간이 초과된 태스크가 900초 상한값에 표시됩니다. \u003Ca href=\"#footnote-ref-1\" aria-label=\"각주 [1]로 돌아가기\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003C/ol\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_ins","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_ins_b","blog_component_18","blog_component_19","blog_component_20","blog_component_21","blog_component_ins_d","blog_component_ins_c","blog_component_22","blog_component_23","blog_component_24","blog_component_25","blog_component_26","blog_component_27"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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약관\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca 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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