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28, 2026","tag":{"tagText":"데이터 엔지니어링","tagColor":"#29B5E8"},"authors":[{"authorImage":{"id":"image-c47f906266","alt":"Jena Donlin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--74c1226b-7c18-414d-be92-3833932fa0ea/jena.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"512","width":"341",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-4e90f160ce","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jena-donlin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Jena Donlin"}}],"title":{"lines":["차세대 데이터 엔지니어링: Dynamic Table과 데이터 구축 방식을 변혁할 5가지 기능"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"image":{"id":"image-21392f4d88","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--871c66ef-aee3-4827-8c3b-9dd5041c45dc/sf-snowflake-snowpipe.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"686","width":"1200",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnClassNames":{"youtube":"aem-GridColumn aem-GridColumn--default--12","youtube_337368697":"aem-GridColumn aem-GridColumn--default--12","blog_text_copy_746541545":"aem-GridColumn aem-GridColumn--default--12","blog_text_copy_164860931":"aem-GridColumn aem-GridColumn--default--12","blog_text_copy_1103053933":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text_copy_164860931":{"id":"blog-text-fbf3a750b9","additionalClasses":"inline","text":"\u003Cp\u003E데이터 엔지니어의 역할은 큰 변화를 겪고 있습니다. 단순히 데이터를 A 지점에서 B 지점으로 이동시키는 스크립트를 작성하는 수준을 넘어, 오늘날의 데이터 엔지니어는 \u003Ca href=\"https://medium.com/binary-notes/so-data-engineering-is-just-writing-pipelines-fe29a5126be3%20\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E대규모 확장성\u003C/a\u003E과 복잡한 DevOps 워크플로우, 시맨틱 모델링을 균형 있게 다루는 ‘\u003Ca href=\"https://www.reddit.com/r/dataengineering/comments/1qoiat1/are_you_seeing_this_too/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E풀스택 빌더\u003C/a\u003E’로 진화하고 있습니다. 이러한 역량의 변화와 함께 선언형 프로그래밍으로의 전환도 뚜렷해졌습니다. 이제 엔지니어는 취약한 단계별 명령을 수시간 관리하는 대신 데이터의 목표 상태를 정의하고 이를 \u003Ci\u003E어떻게\u003C/i\u003E 구현할지는 플랫폼에 맡기고 있습니다.\u003C/p\u003E\n\u003Cp\u003EDynamic Table, 시맨틱 뷰, Cortex Code에 이르기까지 Snowflake는 기존 데이터 엔지니어링 워크플로우를 수일에서 수분 단위로 단축하고 있습니다. 더 적은 것으로 더 많은 것을 하려는 접근 방식보다 충분한 리소스로 더 많은 성과를 달성하려는 것입니다. 차세대 도구를 통해 데이터 엔지니어는 인프라 프로비저닝, 분산된 도구 관리, 수작업 코딩에 대한 부담 없이, 레이크하우스 데이터 위에서 AI 에이전트에 필요한 컨텍스트를 제공하는 지표와 비즈니스 요구 사항을 중앙에서 정의하여 AI 솔루션을 구현할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E다음은 데이터 엔지니어링 생산성을 다음 단계로 끌어올릴 6가지 기능입니다.\u003C/p\u003E\n\u003Ch2\u003ESnowflake에서 더 빠르게 빌드하기: Cortex Code\u003C/h2\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-8eb9239d18","youtubeVideoId":"lftWaAcG2nE","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text_copy_746541545":{"id":"blog-text-d21be3d565","additionalClasses":"inline","text":"\u003Cp\u003E데이터 엔지니어는 Cortex Code에서 간단한 프롬프트만으로 프로덕션 수준의 파이프라인을 구축할 수 있습니다. Cortex Code는 다양한 유형의 데이터 엔지니어와 분석가가 Snowflake에서 개발을 간소화할 수 있도록 지원합니다. 또한 작업의 복잡성과 구축 시간을 줄여 숙련된 데이터 엔지니어에게도 생산성을 높이는 도구로 활용됩니다. 데이터 엔지니어는 파이프라인을 처음부터 생성하거나 기존 코드를 Snowflake로 마이그레이션할 수 있으며, 옵저버빌리티 개선, 문제 해결 및 디버깅을 수행하고, 전반적으로 AI를 생산성 향상 도구로 활용해 엔드투엔드 파이프라인을 구현할 수 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E자율형 파이프라인: Dynamic Table\u003C/h2\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube_337368697":{"id":"embed-cd41b3ae49","youtubeVideoId":"bk1uzVM8aN8","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text_copy_1103053933":{"id":"blog-text-cd3a13df7e","additionalClasses":"inline","text":"\u003Cp\u003E수년 동안 증분 처리는 복잡한 로직과 스케줄링이 필요한 수작업 중심의 어려운 작업이었습니다. 데이터 엔지니어, 플랫폼 팀, 분석가는 Dynamic Table을 통해 SQL 쿼리만 정의하면 Snowflake가 증분 업데이트와 오케스트레이션을 자동으로 수행할 수 있습니다.\u003C/p\u003E\r\n\u003Cp style=\"margin-left: 40px;\"\u003E\u003Cb\u003ESnowflake Dynamic Table을 통한 비즈니스 단위의 데이터 제공 효율 향상\u003C/b\u003E\u003Cbr\u003E\r\n\u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/travelpass/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETravelpass\u003C/a\u003E는 Dynamic Tables를 활용해 복잡한 수작업 코딩에서 벗어났습니다. 선언형 접근 방식을 도입해 데이터 파이프라인을 단순화하고 실시간 데이터 흐름 유지에 필요한 엔지니어링 시간을 크게 줄여 효율을 350% 향상시켰습니다.\u003C/p\u003E\r\n\u003Ch2\u003E확장된 개발: dbt Projects on Snowflake\u003C/h2\u003E\r\n\u003Cp\u003Edbt는 오랜 기간 데이터 변환의 업계 표준이었으며 이제 조직은 OSS를 Snowflake에서 네이티브로 실행할 수 있습니다. Snowflake 인프라에서 dbt 프로젝트를 직접 실행함으로써 dbt 및 오케스트레이션을 위한 인프라 관리 부담을 줄일 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003Edbt Projects on Snowflake는 버전 관리, 테스트, 문서화가 데이터와 함께 이루어지는 통합 환경을 제공합니다. 이를 통해 팀은 데이터 변환을 소프트웨어 코드처럼 다루고 개발에서 프로덕션까지 원활하고 안전하며 확장 가능한 전환을 구현할 수 있습니다.\u003C/p\u003E\r\n\u003Cp style=\"margin-left: 40px;\"\u003E\u003Cb\u003E데이터 엔지니어링의 판도를 바꾸는 dbt Projects on Snowflake\u003C/b\u003E\u003Cbr\u003E\r\n데이터 슈퍼히어로 Keith Belanger와 Jan Láznička는 \u003Ca href=\"https://www.youtube.com/watch?v=3R6PrWBf7CM\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBehind the Cape\u003C/a\u003E 에피소드에서 dbt 프로젝트가 데이터 엔지니어링 방식을 어떻게 변화시키는지 설명합니다.\u003C/p\u003E\r\n\u003Ch2\u003E간소화된 오케스트레이션: Snowflake Tasks\u003C/h2\u003E\r\n\u003Cp\u003ETasks는 특정 간격 또는 특정 이벤트에 따라 SQL 문이나 저장 프로시저를 실행하도록 스케줄링할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E방향성 비순환 그래프(DAG) 구조를 활용하면 Tasks를 통해 엔지니어는 복잡한 다단계 워크플로우를 Snowflake 내에서 직접 구성할 수 있습니다. 이로 인해 많은 사용 사례에서 비용이 높은 서드 파티 오케스트레이터가 필요하지 않으며, 데이터와 로직을 가깝게 유지하고 아키텍처 복잡성을 크게 줄일 수 있습니다.\u003C/p\u003E\r\n\u003Cp style=\"margin-left: 40px;\"\u003E\u003Cb\u003ESnowflake에서 파이프라인을 네이티브로 오케스트레이션\u003C/b\u003E\u003Cbr\u003E\r\nSnowflake Field CTO인 Jeremiah Hansen은 Snowflake Tasks가 dbt Projects의 변환과 결합되어 전체 파이프라인을 네이티브로 처리하는 방법을 \u003Ca href=\"https://www.youtube.com/watch?v=qC4e0nX4Hyw\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E설명합니다.\u003C/a\u003E 외부 오케스트레이터를 관리할 필요가 없습니다.\u003C/p\u003E\r\n\u003Ch2\u003E데이터 품질 향상: DMFs\u003C/h2\u003E\r\n\u003Cp\u003E자동화는 신뢰 없이는 무의미하며, 이를 지원하는 것이 DMFs(data metric functions)입니다. 과거에는 데이터 품질이 사후 고려 사항이었으며, 급하게 작성된 일련의 '정합성 검증' 스크립트에 불과했습니다. DMFs는 데이터 상태(예: 최신성, 고유성, NULL 개수)를 자동으로 측정하는 선언형 방식을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E각 테이블마다 별도의 검증 스크립트를 작성하는 대신, 이제 테이블 메타데이터에 품질 지표를 정의할 수 있습니다. 이러한 내장형 사용자 활성화 옵저버빌리티 기능을 통해 데이터가 비즈니스 기준을 충족하지 못할 경우, 시스템이 즉시 이를 감지하여 다운스트림 애플리케이션과 사용자에게 전달되기 전에 데이터 문제를 확인할 수 있습니다.\u003C/p\u003E\r\n\u003Cp style=\"margin-left: 40px;\"\u003E\u003Cb\u003EDMFs로 데이터 품질 평가하기\u003C/b\u003E\u003Cbr\u003E\r\nAugusto Rosa는 DMF가 데이터 상태와 무결성을 어떻게 모니터링하는지 \u003Ca href=\"https://medium.com/snowflake/snowflake-built-in-quality-metrics-3417d7d9f040\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E설명합니다.\u003C/a\u003E 최신성, 개수, 중복 또는 NULL과 같은 특정 값을 식별하는 주요 지표를 측정할 수 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E비즈니스 로직: 시맨틱 뷰\u003C/h2\u003E\r\n\u003Cp\u003E마지막으로, 시맨틱 뷰의 등장은 엔지니어링과 C 레벨 간의 오랜 과제였던 ‘정의 격차’를 해결합니다. 기존에는 비즈니스 로직이 여러 BI 도구에 분산되어 동일한 질문(예: ‘이탈률은 얼마인가?’)에 대해 서로 다른 답이 도출되었습니다.\u003C/p\u003E\r\n\u003Cp\u003E이 로직을 시맨틱 계층, 즉 시맨틱 뷰로 이동함으로써 데이터 엔지니어는 비즈니스 정의를 한 번만 정의하면 됩니다. 사용자가 대시보드, 스프레드시트, AI 기반 채팅 인터페이스를 사용하더라도 단일 진실 공급원을 기반으로 데이터를 활용하게 됩니다. 이를 통해 데이터 웨어하우스를 단순한 테이블 집합에서 비즈니스 활용이 가능한 지식 기반으로 전환하게 됩니다.\u003C/p\u003E\r\n\u003Cp style=\"margin-left: 40px;\"\u003E\u003Cb\u003EAI 기반 시맨틱 모델링을 몇 분 만에 구현\u003C/b\u003E\u003Cbr\u003E\r\neSentire, HiBob, Simon AI, VTS를 포함한 조직이 Semantic View Autopilot을 활용해 AI 에이전트가 동일하고 신뢰 가능한 비즈니스 지표를 기반으로 작동하도록 하고, 시맨틱 모델 생성 시간을 수일에서 수분으로 단축하는 방법을 \u003Ca href=\"https://www.snowflake.com/en/blog/semantic-view-autopilot/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E확인하세요.\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E이러한 기능의 영향은 단순히 점진적인 업데이트를 넘어 자동화되고 신뢰할 수 있으며 비즈니스에 정렬된 데이터 전략으로의 근본적인 전환을 의미합니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake의 데이터 엔지니어링에 대해 더 알아보려면 \u003Ca href=\"https://www.snowflake.com/en/resources/ebook/the-new-essential-guide-to-data-engineering/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EThe New Essential Guide to Data Engineering\u003C/a\u003E을 다운로드하고 4월 22일에 열리는 가상 이벤트 \u003Ca href=\"https://www.snowflake.com/events/snowflake-connect-building-transformation-pipelines-for-ai-ready-data/?utm_source=blog&utm_campaign=workload-bof-dataeng&utm_cta=link\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake Connect: AI 준비 데이터용 변환 파이프라인 구축\u003C/a\u003E에 등록하세요. 또한 온디맨드로 제공되는 최근 진행된 가상 핸즈온 랩 \u003Ca href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/autonomous-sql-pipelines-for-ai-agents-2026-03-17/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAI 에이전트를 위한 자율형 SQL 파이프라인\u003C/a\u003E도 확인할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text_copy_164860931","youtube","blog_text_copy_746541545","youtube_337368697","blog_text_copy_1103053933"],":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-e065fbbeb1","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":type":"snowflake-site/components/container",":items":{"author_chip":{"id":"author-chip-8d84c8b479","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-c47f906266","alt":"Jena Donlin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--74c1226b-7c18-414d-be92-3833932fa0ea/jena.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"512","width":"341",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-4e90f160ce","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jena-donlin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Jena Donlin"},"authorTitle":"Product Marketing Lead"}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"]}},":itemsOrder":["container_hero","responsivegrid_content","responsivegrid_premium_content_banner","container_author_chip"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-fdc4190fbc",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"blog_table_of_content":{"id":"blog-table-of-content-e3042dca93","tableOfContents":[],":type":"snowflake-site/components/blog/blog-table-of-content"}},":itemsOrder":["blog_table_of_content"]},"isBlogPage":true,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"related_content":{"id":"related-content-ae762df707","relatedContent":[{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"제품 및 기술","tagColor":"#3F808A"}],"button":{"id":"button-d985deb659","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/ko/blog/ai-smart-pipelines-whats-new/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"AI 시대의 데이터 엔지니어링: 스마트 파이프라인을 위한 새로운 Snowflake 도구"},"image":{"id":"image-8f254ed5ca","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--871c66ef-aee3-4827-8c3b-9dd5041c45dc/sf-snowflake-snowpipe.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"686","width":"1200",":type":"snowflake-site/components/image"},"title":{"lines":["AI 시대의 데이터 엔지니어링: 스마트 파이프라인을 위한 새로운 Snowflake 도구"],"type":"heading4",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/card","publicationDate":"JUN 11, 2026","authors":[{"authorImage":{"id":"image-d12d0a3b5d","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1fedfb68-b4f6-491d-9c76-af4b725d2855/abhishek-kashyap.png?quality=85&preferwebp=true","lazyEnabled":true,"height":"512","width":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e655645755","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/abhishek-kashyap/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Abhishek Kashyap"}},{"authorImage":{"id":"image-c47f906266","alt":"Jena Donlin","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--74c1226b-7c18-414d-be92-3833932fa0ea/jena.jpg?quality=85&preferwebp=true","lazyEnabled":true,"height":"512","width":"341",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-4e90f160ce","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jena-donlin/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Jena Donlin"}}]},{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"제품 및 기술","tagColor":"#3F808A"}],"button":{"id":"button-324100c944","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/ko/blog/claude-fable-5-snowflake-cortex-ai/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Cortex AI에서 Anthropic Claude Fable 5 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