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path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ko/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":items":{"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":items":{"title_v2":{"id":"title-v2-c2337f0a42","additionalClasses":"headline-decoration","type":"heading2","lines":["개요"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c56670fcde","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E데이터 레이크를 사용해 본 사람이라면 누구나 공감할 만한 답답한 순간이 있습니다. 어제까지 대시보드에 200,000건으로 표시되던 트랜잭션 수가 다음 날 갑자기 195,000건으로 줄어들었는데 그 이유를 아무도 설명하지 못하는 상황이 바로 그런 경우입니다. 기존 데이터 레이크는 유연하지만, 누락된 레코드, 중복 파일, 느린 쿼리 같은 오류에 취약합니다. \u003C/p\u003E\r\n\u003Cp\u003EDelta Lake는 이러한 문제를 해결합니다. 기존 데이터 레이크 위에 계층으로 추가되는 오픈소스 스토리지 프레임워크로, 안정성과 성능을 강화합니다. Delta Lake를 사용하면 수치의 정확성을 일일이 확인하는 대신, 데이터를 일관되고 정확하게 유지한 상태로 즉시 분석에 활용할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_115652453":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_1858830768":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":items":{"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":items":{"title_v2":{"id":"title-v2-70c0d610de","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake란?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-5c3f696f16","text":"\u003Cp\u003EDelta Lake는 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/data-lakes-and-data-lake-architecture/\"\u003E데이터 레이크\u003C/a\u003E에 구조와 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/data-governance/\"\u003E거버넌스\u003C/a\u003E를 더한 오픈소스 테이블 형식입니다. Delta Lake는 파일을 단순히 저장하는 공간에 그치지 않고, 데이터를 안정적으로 쿼리하고 업데이트할 수 있는 테이블 구조로 데이터를 체계화합니다. Delta Lake는 가공 전 데이터 레이크가 제공하지 못하는 핵심 기능을 통해 데이터 신뢰성을 강화합니다. ACID(원자성, 일관성, 격리성, 지속성) 트랜잭션으로 읽기 및 쓰기의 일관성을 유지하고 스키마 규정 준수 관리로 데이터 유형 불일치를 방지하며 ‘타임 트래블’ 쿼리를 통해 과거 시점의 데이터로 손쉽게 롤백할 수 있습니다. 또한 기존 데이터 레이크 위에서 동작하기 때문에 새로운 시스템을 도입할 필요가 없습니다. 이미 사용 중인 데이터 레이크를 더 스마트하게 관리할 수 있는 방법을 제공할 뿐입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":items":{"title_v2":{"id":"title-v2-b9b05c4a70","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake 주요 기능"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8a6447b5be","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake는 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/avro-vs-parquet-choosing-a-data-format-for-modern-workflows/\"\u003EParquet\u003C/a\u003E 또는 ORC 파일을 기반으로 구축된 일반적인 데이터 레이크와 달리, 데이터의 신뢰성을 높이고 활용을 더 쉽게 만드는 테이블 수준의 피처 세트를 제공합니다. Delta Lake를 차별화하는 핵심 기능은 다음과 같습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EACID 트랜잭션\u003C/h3\u003E\n\u003Cp\u003E대부분의 데이터 레이크는 여러 사용자나 작업이 동시에 데이터를 읽고 쓸 때 일관성을 보장하지 못합니다. Delta Lake는 ACID 트랜잭션을 지원해 모든 변경 사항이 안정적으로 추적되고 적용되도록 합니다. 예를 들어 \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/\"\u003E금융 기관\u003C/a\u003E이 밤사이 수백만 건의 결제 기록을 업데이트하는 경우 Delta Lake는 모든 업데이트가 일괄 적용되거나 전혀 적용되지 않도록 보장합니다. 일부 레코드만 업데이트되고 다른 레코드는 업데이트되지 않는 중간 상태는 존재하지 않습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E타임 트래블\u003C/h3\u003E\n\u003Cp\u003E실수는 언제든 발생할 수 있습니다. Delta Lake를 사용하면 이전 시점의 데이터로 롤백할 수 있습니다. 타임 트래블 기능을 통해 특정 시점을 기준으로 데이터를 쿼리해 변경 사항을 감사하거나 오류를 복구하고 과거 보고서를 재현할 수 있습니다. 예를 들어 소매업체는 이번 분기 실적을 이전 연말 시즌의 스냅샷과 비교할 수 있고, 분석가는 수정 전 입력 데이터를 확인해 모델을 디버깅할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E확장 가능한 메타데이터 관리\u003C/h3\u003E\n\u003Cp\u003E대규모 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/data-lakes-and-data-lake-architecture/\"\u003E데이터 레이크\u003C/a\u003E 환경에서는 메타데이터(파일에 대한 정보)가 병목 지점이 될 수 있습니다. Delta Lake는 메타데이터를 방대한 파일 목록 대신 트랜잭션 로그에 저장해 이 문제를 해결합니다. 그 결과 수십억 개의 파일이나 파티션을 가진 페타바이트 규모 테이블도 성능 저하 없이 쿼리할 수 있습니다. 예를 들어 통신 사업자가 수억 명의 고객에 대한 통화 기록을 분석하는 상황을 생각해 볼 수 있습니다. Delta Lake의 트랜잭션 로그가 없다면, 쿼리를 실행하기도 전에 파일 목록화 작업만으로 시스템이 과부하에 걸릴 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E스키마 규정 준수 관리 및 진화\u003C/h3\u003E\n\u003Cp\u003E기존 데이터 레이크는 스키마가 데이터 세트의 나머지 부분과 일치하지 않더라도 모든 파일을 그대로 수용합니다. 이로 인해 잘못된 데이터가 아무 경고 없이 유입될 수 있습니다. 한 팀이 ‘date’ 컬럼에 텍스트 문자열이 들어 있는 파일을 업로드하거나 다른 팀이 데이터 세트의 나머지 부분에는 존재하지 않는 새 컬럼을 추가하면 전체 테이블의 신뢰성이 저하될 수 있습니다. 분석가는 쿼리를 실행할 때 오류가 발생할 수 있으며 더 심각한 경우에는 겉보기에는 정상처럼 보이지만 실제로는 잘못된 결과를 얻을 수 있습니다.\u003C/p\u003E\n\u003Cp\u003EDelta Lake는 스키마를 적용해 이러한 문제를 해결하며 스키마가 일치하지 않는 파일이 데이터 세트를 오염시키기 전에 차단합니다. 동시에 스키마 진화를 지원해 비즈니스 요구 사항이 변화하더라도 기존 워크플로우를 중단하지 않고 새 컬럼을 추가하거나 데이터 유형을 조정할 수 있습니다. 즉 마케팅 팀은 고객 데이터에 ‘campaign_id’ 컬럼을 추가할 수 있고 재무 팀은 전환 준비가 될 때까지 기존 스키마를 계속 사용할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":items":{"title_v2":{"id":"title-v2-cf81fe4786","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake의 이점"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-22985791d4","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E최신 데이터 스택에 Delta Lake를 추가하면 기술적 기능을 넘어 실질적인 가치를 제공합니다. 조직이 체감할 수 있는 주요 이점은 다음과 같습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E데이터 신뢰성 향상\u003C/h3\u003E\n\u003Cp\u003EACID 트랜잭션과 스키마 규정 준수 관리가 기본으로 제공되어 Delta Lake는 손상된 파일, 불완전한 업데이트, 데이터 유형 불일치의 위험을 줄입니다. 이러한 안정성 덕분에 팀은 재무 보고서를 작성하든 머신러닝 모델을 학습하든 데이터 수치를 안심하고 신뢰할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E데이터 아키텍처 단순화\u003C/h3\u003E\n\u003Cp\u003EDelta Lake가 없으면 많은 조직은 트랜잭션 처리, 감사, 버전 관리를 위해 데이터 레이크 위에 추가 시스템을 덧붙여야 합니다. Delta Lake는 이러한 기능을 하나의 계층으로 통합합니다. 그 결과 관리가 쉬워지고 유지 비용도 줄어드는 단순한 아키텍처를 구축할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E데이터 품질 강화\u003C/h3\u003E\n\u003Cp\u003EDelta Lake는 유입되는 데이터를 검증하고 스키마를 적용하기 때문에 잘못된 레코드가 데이터 레이크에 유입되는 것을 방지합니다. 이전 버전으로 되돌릴 수 있는 기능과 결합되어 일관되고 정제된 데이터 세트를 유지할 수 있으며, 이에 분석 팀은 데이터 정제에 쓰는 시간을 줄이고 데이터 활용에 더 집중할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E거버넌스와 감사 강화\u003C/h3\u003E\n\u003Cp\u003EDelta Lake에서는 모든 변경 사항이 트랜잭션 로그에 기록됩니다. 이 로그는 기본 제공되는 감사 추적 기록으로 작동해 어떤 사용자가 어떤 업데이트를 언제 수행했는지 정확히 확인할 수 있습니다. 규제가 적용되는 산업이나 엄격한 규정 준수 요구 사항을 가진 기업의 경우, 기본으로 제공되는 투명성을 통해 추가적인 툴링 없이도 거버넌스 요구 사항을 충족할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E분석 성능 향상\u003C/h3\u003E\n\u003Cp\u003EDelta Lake는 데이터 레이아웃을 최적화하고 대규모 메타데이터를 관리할 수 있어 쿼리 결과를 더 빠르게 반환합니다. 분석가는 이제 테라바이트 규모의 원시 파일을 스캔하는 쿼리 때문에 몇 시간씩 기다릴 필요가 없습니다. 시스템에서 필요한 파티션만 정확히 찾아내 몇 분 또는 몇 초 안에 인사이트를 얻을 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E운영 비용 절감\u003C/h3\u003E\n\u003Cp\u003EDelta Lake는 스키마 적용, 버전 추적, 데이터 손상 복구를 위한 추가 도구의 필요성을 줄여 총소유비용을 낮출 수 있습니다. 팀은 오류 수정과 중복 시스템에 드는 비용을 줄이고, 더 가치 있는 프로젝트에 리소스를 투입할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":items":{"title_v2":{"id":"title-v2-8d08e1521c","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake 고려 사항 "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3fd3b177f0","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake는 다양한 이점을 제공하지만 특히 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/apache-iceberg-tables/\"\u003EApache Iceberg\u003C/a\u003E와 같은 다른 오픈 테이블 포맷과 비교할 경우 몇 가지 \u003Ca href=\"https://hevodata.com/learn/apache-iceberg-vs-delta-lake-key-differences/\"\u003E과제\u003C/a\u003E가 존재하기도 합니다. Apache Iceberg 역시 데이터 레이크에 구조와 안정성을 제공합니다. 두 기술 모두 원시 파일 스토리지의 한계를 해결하는 것을 목표로 하지만 접근 방식은 서로 다릅니다. 어떤 테이블 형식이 적합한지 판단하려면 다음과 같은 차이점을 살펴보는 것이 좋습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E메타데이터와 거버넌스\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake는 모든 변경 사항을 트랜잭션 로그에 기록해 일관성을 유지하며 업데이트를 신뢰할 수 있도록 합니다. Iceberg는 스냅샷과 매니페스트 파일을 사용해 테이블 상태를 관리하며, 데이터 롤백이나 분기 작업이 더 수월합니다. 팀의 거버넌스와 감사 방식에 어떤 접근이 더 잘 맞는지가 핵심 판단 기준입니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E생태계 적합성\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake는 Apache Spark와 함께 널리 사용되며, Delta UniForm을 통해 멀티 엔진 상호운용성도 지원합니다. Iceberg는 엔진 애그노스틱을 핵심 설계 원칙으로 삼아 여러 쿼리 엔진과 다양한 클라우드 플랫폼 전반에서 일관되게 지원됩니다. 적합한 선택은 특정 환경에 대한 깊은 통합을 우선할지, 아니면 다양한 환경 전반에서의 실행 유연성을 우선할지에 따라 결정됩니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E성능 최적화\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake는 Z 오더링과 동적 클러스터링 같은 기법을 사용해 관련 데이터를 클러스터링함으로써 복잡한 필터 조건에서도 쿼리 속도를 향상시킵니다. Iceberg는 히든 파티셔닝과 메타데이터 프루닝을 활용해 쿼리가 불필요한 파일을 건너뛰도록 함으로써 전반적인 성능을 향상시킵니다. 팀의 과제는 각자의 워크로드 특성과 데이터 볼륨에 가장 적합한 접근 방식을 선택하는 것입니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E스키마 진화\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake와 Iceberg 모두 시간이 지남에 따라 스키마 변경을 지원합니다. \u003Ca href=\"https://medium.com/@pmpranjalmittal29/understanding-schema-evolution-in-delta-lake-06f0470ce9b5\"\u003EDelta Lake는 초기 단계에서 더 엄격하게 작동하며\u003C/a\u003E 요구 사항이 변화함에 따라 통제된 변경만 허용합니다. Iceberg는 설계 단계부터 더 유연하게 만들어져 파티션과 스키마 변경을 처음부터 처리할 수 있으며 데이터가 자주 빠르게 변화하는 환경에서는 운영 부담을 줄일 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E커뮤니티와 성숙도\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake는 비교적 오래된 프로젝트로, Spark 기반 환경에서 큰 사용자 기반을 보유하고 있습니다. Iceberg는 등장한 지 오래되지는 않았지만 \u003Ca href=\"https://www.datanami.com/2021/02/08/apache-iceberg-the-hub-of-an-emerging-data-service-ecosystem/\"\u003ENetflix\u003C/a\u003E, Snowflake 등 주요 기업들의 참여와 지원을 통해 빠르게 주목받고 있습니다. 결국 선택은 성숙한 프로젝트를 선호하는지, 빠르게 진화하는 기술을 선호하는지에 달려 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":items":{"title_v2":{"id":"title-v2-4f4e27eebe","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake 설정"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ebe5eaeb3d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E조직에 Delta Lake가 적합하다고 결정하더라도 기존 인프라를 전면 교체하지 않고 도입을 시작할 수 있습니다. 일반적으로는 기존 데이터 레이크 위에 호환 가능한 처리 엔진을 통해 Delta Lake를 계층으로 추가하는 방식입니다. 주요 단계는 다음과 같습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. 컴퓨팅 엔진 선택\u003C/h3\u003E\n\u003Cp\u003EDelta Lake는 일반적으로 Apache Spark와 함께 사용되지만 Snowflake, Presto, Trino, Hive 등 다양한 엔진과도 호환됩니다. 현재 데이터 환경과 향후 쿼리 및 처리 방식에 따라 적합한 엔진을 선택하면 됩니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. 라이브러리 구성\u003C/h3\u003E\n\u003Cp\u003E다음으로 선택한 컴퓨팅 엔진 내에 Delta Lake 라이브러리를 설치하고 구성합니다. 이를 통해 엔진이 Delta 테이블을 읽고 쓰며, 스키마를 강제 적용하고 트랜잭션 로그로 변경 사항을 추적할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. 데이터 읽기 및 쓰기\u003C/h3\u003E\n\u003Cp\u003E구성이 완료되면 기존 데이터 레이크 스토리지에서 바로 Delta 테이블을 생성할 수 있습니다. 데이터는 Parquet 형식으로 저장되지만 Delta의 트랜잭션 로그를 통해 관리되며 이를 통해 업데이트, 삭제, 타임 트래블 쿼리를 지원합니다. 이후에는 익숙한 SQL 명령으로 데이터 세트를 쿼리, 수정, 병합할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1413360324":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":items":{"title_v2_copy":{"id":"title-v2-049b31663b","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake의 5가지 모범 사례"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-3abb24d61f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake 배포에서 최대한의 가치를 얻으려면, 팀은 몇 가지 검증된 모범 사례를 염두에 두어야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E효율적인 데이터 파티셔닝\u003C/h3\u003E\r\n\u003Cp\u003E대규모 데이터 세트를 날짜나 리전과 같은 논리적 기준으로 파티셔닝해 쿼리가 전체 데이터 레이크를 스캔하는 대신 필요한 파일 하위 집합만 집중적으로 쿼리할 수 있도록 합니다. 예를 들어 뉴스 사이트는 클릭스트림 데이터를 날짜별로 파티셔닝해 수개월치의 관련 없는 레코드를 모두 살펴보지 않고도 일일 보고서를 쉽게 실행할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E대규모 메타데이터 관리\u003C/h3\u003E\r\n\u003Cp\u003E데이터가 늘어날수록 트랜잭션 로그와 테이블 메타데이터를 지속적으로 관리해야 합니다. 쿼리 속도 저하와 스토리지 비대화를 방지하기 위해 작은 파일을 정기적으로 컴팩션하세요. 이 간단한 단계만으로도 수천 개의 작은 파일이 쿼리 성능을 심각하게 저하시키는 상황을 막을 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EZ 오더링 활용으로 쿼리 성능 극대화\u003C/h3\u003E\r\n\u003Cp\u003EZ 오더링을 적용해 관련 데이터를 함께 클러스터링하세요. 이는 여러 컬럼을 기준으로 필터링하는 쿼리의 성능을 개선하고 읽어야 하는 파일 수를 줄여줍니다. 예를 들어 \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/retail-consumer-goods/\"\u003E전자상거래 기업\u003C/a\u003E은 추천 쿼리 속도를 높이기 위해 고객 데이터를 위치와 구매 이력을 기준으로 Z 오더링할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 품질 검사 자동화\u003C/h3\u003E\r\n\u003Cp\u003E새 데이터가 유입될 때 유효 범위, 유형, 형식에 대한 규칙을 강제 적용합니다. 오류를 조기에 발견하면 잘못된 레코드가 다운스트림으로 확산되는 것을 막을 수 있습니다. 센서가 발생해서는 안 되는 음수 온도 값을 전송할 경우 시스템은 사전에 플래그 처리하거나 차단해 데이터 세트의 손상을 방지합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E타임 트래블의 전략적 활용\u003C/h3\u003E\r\n\u003Cp\u003E타임 트래블은 오류 복구에만 사용하는 기능이 아닙니다. 감사, 과거 분석 재현, 그리고 새로운 로직이 과거 데이터에서 어떻게 동작했을지를 테스트하는 데에도 유용합니다. 이를 통해 팀은 현재 운영 중인 프로덕션 데이터를 덮어쓰지 않고도, 작년 데이터로 새로운 예측 모델을 검증할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_1858830768":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"solutions",":items":{},":itemsOrder":[],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_115652453":{"columnClassNames":{"text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"last",":items":{"title_v2_copy_copy_c":{"id":"title-v2-a94d4678e4","additionalClasses":"headline-decoration","type":"heading2","lines":["결론"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy_copy_":{"id":"text-38730fe844","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake는 데이터 레이크의 유연성과 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003E데이터 웨어하우스\u003C/a\u003E의 신뢰성 사이의 간극을 메웁니다. 기존 스토리지 위에 ACID 트랜잭션, 스키마 규정 준수 관리, 메타데이터 관리, 타임 트래블 같은 테이블 기능을 추가함으로써 BI 대시보드와 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003E머신러닝 모델\u003C/a\u003E 모두를 지원하며 신뢰할 수 있는 단일 데이터 소스를 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E최신 데이터 플랫폼을 구축하는 조직에게 Delta Lake는 아키텍처를 단순화하고 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/a-practical-guide-to-data-quality/\"\u003E데이터 품질\u003C/a\u003E을 개선하며 거버넌스를 강화하는 기반 기술로 자리 잡았습니다. 앞으로의 흐름은 데이터가 비즈니스 전반에서 더 쉽게 활용될 수 있도록 개방적이고 지능적이며 통합된 포맷으로 향하고 있습니다. Delta Lake는 그 방향을 선도하며 데이터 아키텍처의 미래를 보여주고 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy_c","text_copy_copy_copy_"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":items":{"title_v2":{"id":"title-v2-d92e68ab33","additionalClasses":"headline-decoration","type":"heading2","lines":["Delta Lake 자주 묻는 질문"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-5057d41671","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Delta 테이블이란?","richText":"\u003Cp\u003EDelta 테이블은 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003E데이터 레이크\u003C/a\u003E에 저장되지만 Delta Lake의 트랜잭션 로그로 관리되는 데이터 세트입니다. 일반 테이블과 동일한 방식으로 작동하므로 SQL 쿼리는 물론 업데이트와 삭제 작업도 손쉽게 수행할 수 있습니다. 트랜잭션 로그는 모든 변경 이력을 기록함으로써 데이터 일관성을 강화하고 타임 트래블과 같은 기능을 지원합니다.\u003C/p\u003E\n"},{"title":"데이터 레이크와 Delta Lake의 차이는 무엇인가요?","richText":"\u003Cp\u003E전통적인 데이터 레이크는 원시 파일을 저장할 수 있는 유연한 리포지토리이지만 트랜잭션, 스키마 규정 준수 관리, 버전 관리와 같은 테이블 수준 제어 기능은 갖추지 못하고 있습니다. Delta Lake는 이러한 기능을 데이터 레이크 위에 추가합니다. 마치 단순한 파일 집합을 여러 팀이 공통으로 신뢰하며 쿼리할 수 있는 테이블 구조로 전환하는 과정이라고 볼 수 있습니다.\u003C/p\u003E\n"},{"title":"데이터 레이크하우스란 무엇인가요?","richText":"\u003Cp\u003E데이터 레이크하우스는 데이터 레이크와 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003E데이터 웨어하우스\u003C/a\u003E의 장점을 결합한 아키텍처입니다. Delta Lake와 같은 오픈 테이블 형식을 사용해 가공 전 데이터와 정형 데이터를 함께 저장하면서도 안정적인 쿼리, 거버넌스 및 성능을 지원합니다. 그 결과 기업은 시스템 간에 데이터를 옮기지 않고도 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/ai-for-business-intelligence/\"\u003EBI와 AI\u003C/a\u003E 워크로드를 모두 실행할 수 있는 단일 플랫폼을 확보할 수 있습니다.\u003C/p\u003E\n"},{"title":"Delta Lake는 오픈 소스인가요?","richText":"\u003Cp\u003E네. 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