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Postgres와 Snowflake가 매끄럽게 연동될 뿐입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-96b02a266e","height":"923","src":"/adobe/dynamicmedia/deliver/dm-aid--411db4c1-8728-4fdb-ab28-93ab101612d1/postgres-data-mirroring.jpg?quality=85&preferwebp=true","alt":"Overview of Postgres data mirroring.","lazyEnabled":true,"isLcpImage":true,"width":"2048","title":"자료 1: Postgres 데이터 미러링 개요",":type":"snowflake-site/components/image"},"blog_title":{"id":"blog-title-02180ce40f","propertiesId":"","type":"heading2","lines":["파이프라인 부담을 없애는 데이터 미러링"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_869256526":{"id":"blog-text-3ae15d174d","text":"\u003Cp\u003E데이터 팀에 가장 고된 업무가 무엇인지 물어보면 같은 답이 돌아옵니다. 바로 트랜잭션 시스템과 분석 시스템 사이에서 데이터를 옮기는 일입니다. 팀은 CDC 커넥터를 계속 관리하고, 깨지는 배치 작업과 어긋나는 스냅샷을 수습하느라 어려움을 겪습니다. 이 문제는 30년 동안 이어져 왔고, 우리는 이런 상황을 당연하게 받아들이는 데 너무 익숙해졌습니다. \u003C/p\u003E\n\u003Cp\u003EAI 시대에는 적시성이 중요합니다. 실시간으로 조치할 수 있는 위치에 데이터가 준비되어 있으면 기술 부채를 줄이고 팀이 필요한 데이터를 지체 없이 활용할 수 있어 비즈니스 가치를 창출할 수 있습니다. 지연되고 지속적인 관리가 필요한 파이프라인은 엔지니어링 시간만 소모하는 것이 아닙니다. 데이터를 실제 상황보다 몇 시간, 심지어 며칠씩 뒤처지게 만듭니다. 어젯밤 배치로 적재한 데이터로는 사기 탐지 모델이 오늘의 공격을 막을 수 없습니다.  데이터 미러링은 이러한 시차를 없애 줍니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_2013926849":{"id":"blog-title-308135b855","propertiesId":"postgres","type":"heading2","lines":["작동 방식: Postgres 데이터 미러링"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1386755699":{"id":"blog-text-fd4616d6fe","text":"\u003Cp\u003E데이터 미러링은 Postgres 데이터를 Snowflake로 지속적으로 복제하며, 변경 사항을 수초 내에 반영합니다. 미러를 생성하면 Snowflake는 원본 테이블의 현재 상태를 반영하도록 대상 테이블을 유지합니다. 스키마 변경 사항뿐 아니라 미러링 대상 스키마에 추가한 새 테이블도 반영됩니다. \u003C/p\u003E\n\u003Cp\u003ESnowsight 인터페이스에서 몇 번 클릭하거나 단일 SQL 명령으로 설정할 수 있으며, 더 간편하게 설정하려면 Snowflake CoCo에 요청하기만 하면 됩니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"wistia_video":{"id":"wistia-video-4becbed5e7","title":{"id":"title","type":"heading1","lines":["데이터 미러링 기능 정식 출시: 파이프라인 없이 Postgres 데이터를 Snowflake로"],":type":"snowflake-site/components/title"},"wistiaVideoId":"m8o2crknfz","captionDescription":"자료 2: 데이터 미러링 설정하기","showVideoLength":true,"showRewatchButton":false,":type":"snowflake-site/components/wistia-video"},"blog_text_217653424":{"id":"blog-text-50562cf4d6","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E데이터 미러링은 다음과 같은 이점을 제공합니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E추가 인프라 불필요: \u003C/b\u003E미러는 Snowflake 내부에서 실행되므로 처리해야 할 추가 서비스나 공급업체가 없습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E최신 읽기: \u003C/b\u003E미러링된 모든 테이블에는 이미 적용된 데이터와 진행 중인 변경 사항을 결합하는 실시간 $live 뷰가 포함됩니다. 사용자는 원본에서 커밋된 변경 사항을 수 초 내에 확인할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E트랜잭션 일관성: \u003C/b\u003E원본의 단일 트랜잭션에서 발생한 변경 사항은 대상 시스템에도 함께 반영됩니다. 외래 키 관계가 유지되고 조인 결과도 정확하게 유지됩니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E기본 제공되는 변경 이력:\u003C/b\u003E 모든 테이블에는 삽입, 업데이트, 삭제를 보여주는 7일간의 변경 피드($changes)가 제공됩니다. 이 피드는 Snowflake와 Postgres 양쪽에서 모두 쿼리할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E규모가 커져도 고처리량 유지: \u003C/b\u003E최적화된 적용 전략은 전체 테이블 스캔을 건너뛰므로 데이터가 증가해도 비용과 성능이 안정적으로 유지됩니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003E무엇보다 이 모든 것은 개방형 기반 위에 구축됩니다. \u003C/b\u003E내부적으로 미러링은 Snowflake의 오픈소스 Postgres 확장인 \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/snowflake-postgres/postgres-pg_lake\" target=\"_blank\"\u003Epg_lake\u003C/a\u003E를 사용해 표준 논리적 복제로 변경 사항을 캡처하고 Apache Iceberg™ 테이블에 기록합니다. 데이터베이스와 데이터 사이에 독점 계층이 존재하지 않습니다. 운영 단순성을 위해 표준 Snowflake 테이블에 미러링할 수도 있고, 완전한 이식성을 위해 자체 스토리지의 Iceberg 테이블에 미러링할 수도 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_946332686":{"id":"blog-title-515381bc3d","propertiesId":"","type":"heading2","lines":["데이터 미러링의 차별점: 풀 방식이 아닌 푸시 방식"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_74831782":{"id":"blog-text-a8606744b5","text":"\u003Cp\u003E대부분의 복제 도구는 데이터베이스와 Snowflake 사이에 별도의 서비스를 끼워 넣습니다. 이 서비스는 Postgres 내부에서 무슨 일이 일어나는지 알지 못한 채 네트워크를 통해 변경 사항을 가져옵니다. 예를 들어 스키마가 언제 변경됐는지, 스냅샷이 진행 중인 변경 사항과 어떻게 맞물리는지, 데이터베이스 다운인지 단순한 네트워크 문제인지 알 수 없습니다. 데이터 미러링은 이 모델을 뒤집습니다.\u003C/p\u003E\n\u003Cp\u003EPostgres 확장은 데이터베이스 내부에서 변경 사항을 캡처하고 이를 트랜잭션 단위 배치로 오브젝트 스토리지의 개방형 Iceberg 변경 로그에 푸시합니다. 그런 다음 Snowflake는 서버리스 컴퓨팅으로 해당 배치를 역시 트랜잭션 방식으로 적용합니다. 확장이 Postgres에서 일어나는 모든 일을 파악하므로 소스에서 스냅샷, 스키마 변경, 복구를 조율해 다운스트림에서 충돌하지 않도록 합니다.\u003C/p\u003E\n\u003Cp\u003E양쪽의 트랜잭션 덕분에 모든 미러링된 테이블이 함께 전진하며 Postgres 트랜잭션 경계에 정확히 랜딩합니다. 삽입은 업서트가 아니라 추가 방식으로 처리되므로, 규모가 크고 삽입이 빈번한 테이블도 데이터가 늘어날 때 속도와 비용 효율성을 유지할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E그 결과 다음과 같은 이점을 얻을 수 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E추가 공급업체 없음\u003C/b\u003E: 추가로 평가하거나 비용을 지불하고 관리해야 할 공급업체가 없습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E추가 인프라 없음\u003C/b\u003E: 새롭게 구축하거나 모니터링해야할 인프라가 없습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E느려지는 업서트 없음\u003C/b\u003E: 테이블이 커져도 성능 저하를 일으키지 않습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E변경 데이터에 대한 락인 없음\u003C/b\u003E: 변경 데이터가 개방형 Apache Iceberg 테이블에 랜딩되고 미러링이 Snowflake의 오픈소스 pg_lake 확장 위에 구축되기 때문입니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_279022932":{"id":"quote-item-73763e5caf","alignment":"left","layout":"card","quoteSourceName":"Mike Willett","quoteSourceTitle":"Head of Analytics Data Platform, SimCorp","showQuoteIcon":false,"quote":"\"데이터 미러링으로 데이터 관리가 간소화되어 데이터 복사 프로세스를 완전히 없앨 수 있었습니다. 지원 업무 부담과 비용이 줄었고, 고객은 채권 가격 산정에 필요한 신용 스프레드 데이터를 더 빠르고 안정적으로 받아볼 수 있게 되었습니다.\" \r\n","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_title_59317549":{"id":"blog-title-54e3c118b2","propertiesId":"","type":"heading2","lines":["데이터 미러링 활용 사례"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1391873441":{"id":"blog-text-8dbf8a7980","text":"\u003Cp\u003E데이터 미러링은 최신 트랜잭션 데이터가 필요한 분석 및 AI 워크로드 어디에나 유용합니다. 오늘날 팀들은 다음과 같이 활용하고 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E비용 지불 대상 공급업체 하나 줄이기: \u003C/b\u003EPostgres 데이터를 Snowflake로 가져오기 위해 미들웨어를 운영하는 데이터 플랫폼 팀은 단 한 번의 CREATE_MIRROR 호출로 전체 파이프라인을 대체할 수 있습니다. \u003Ca href=\"https://www.snowflake.com/ko/blog/postgres-data-mirroring/\"\u003EEricsson\u003C/a\u003E은 바로 이 방식을 적용해 \u003Cb\u003E네 개의 레거시 데이터베이스를 통합하고 동기화 파이프라인을 제거했으며 데이터 처리 시간을 99% 단축했습니다.\u003C/b\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E현재 상황을 보여 주는 대시보드: \u003C/b\u003E전자상거래 팀이 주문 테이블을 Snowflake로 미러링하면 모든 구매, 취소, 반품이 몇 초 안에 표시됩니다. 운영 대시보드는 전날 마감 수치가 아니라 실시간 매출을 반영합니다. 플래시 세일로 주문이 급증하면 다음 날 보고서를 기다리지 않고 즉시 확인할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E현재 상황을 정확히 파악하는 AI 에이전트:\u003C/b\u003E 지원 팀은 고객 계정과 주문 이력을 Snowflake에 미러링하고, Snowflake Cortex AI 에이전트는 이를 제품 데이터, 배송 상태, 과거 상호작용과 결합합니다. 고객이 ‘제가 주문한 물건은 어디까지 왔나요?’라고 물으면 에이전트는 현재 비즈니스 상태를 기반으로 답합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E실시간 트랜잭션 기반 사기 및 리스크 점수화:\u003C/b\u003E 금융 서비스 기업은 결제 이벤트를 Snowflake에 미러링하고 이를 대상으로 점수화 모델을 실시간으로 실행합니다. 의심스러운 패턴은 다음 날 아침 배치 작업이 마침내 실행된 뒤가 아니라 자금 정산 전에 플래그가 지정됩니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E별도로 구축할 필요 없는 감사 추적: \u003C/b\u003E규정 준수 팀은 사용자 지정 이력 테이블을 유지 관리하지 않고도 $changes 피드를 사용해 ‘무엇이 언제 변경됐는가?’에 답할 수 있습니다. 미러링된 테이블 전반의 모든 삽입, 업데이트, 삭제는 7일 동안 쿼리할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-bfead85d73","alignment":"left","layout":"card","quoteSourceName":"Alan Buxton","quoteSourceTitle":"Chief Technology Officer, Simfoni","showQuoteIcon":false,"quote":"이전에는 애플리케이션 데이터와 분석 데이터가 서로 다른 곳에 있었습니다. 이제는 모두 Snowflake에 있습니다. 트랜잭션 데이터가 Snowflake로 자동 미러링되므로, 고객은 별도의 작업 없이 이 데이터에 AI를 활용할 수 있습니다.”","showHangingPunctuation":true,":type":"snowflake-site/components/quote-item"},"blog_title_276493659":{"id":"blog-title-a607831cac","propertiesId":"","type":"heading2","lines":["데이터 미러링 빠르게 시작하기"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1093640949":{"id":"blog-text-4cb0d83b72","text":"\u003Cp\u003E데이터 미러링은 현재 \u003Ca href=\"https://www.snowflake.com/ko/product/features/postgres/\" target=\"_blank\"\u003ESnowflake Postgres\u003C/a\u003E가 실행되는 모든 리전에서 AWS와 Microsoft Azure를 통해 사용할 수 있습니다. Postgres 데이터 미러링은 다음 세 단계로 시작할 수 있습니다.\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cp\u003ESnowflake Postgres 인스턴스를 새로 생성하거나 기존 인스턴스를 사용합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ESnowsight에서 몇 번 클릭하거나 단일 SQL 명령을 사용하거나 CoCo에 요청해 미러를 생성합니다. 특정 테이블, 전체 스키마 또는 데이터베이스를 선택합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ESnowflake에서 대상 테이블을 통해 데이터를 쿼리합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ol\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-c162909bd6","language":"sql","codeSnippet":"CALL SNOWFLAKE.POSTGRES.CREATE_MIRROR(\r\n    mirror_name       =\u003E 'data_mirror',\r\n    postgres_instance =\u003E 'my-pg',\r\n    postgres_database =\u003E 'postgres',\r\n    target_database   =\u003E 'MIRRORTOSNOWFLAKE',\r\n    postgres_tables   =\u003E NULL,\r\n    postgres_schemas  =\u003E ['public'],\r\n    refresh_interval  =\u003E '1 minute',\r\n    warehouse         =\u003E NULL\r\n);\r\n","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_195343161":{"id":"blog-text-6ed0b45310","text":"\u003Cp\u003E한 번 설정하면 이후에는 신경 쓸 필요가 없습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E한 가지 더,  \u003C/b\u003ESnowflake 분석 환경의 데이터를 Postgres로 다시 미러링하는 방법이 궁금하시다면 다음 소식을 기대해 주세요. \u003C/p\u003E\n\u003Cp\u003E데이터 미러링과 Snowflake Postgres에 대한 자세한 내용은 다음 리소스를 확인하세요.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring\" target=\"_blank\"\u003E데이터 미러링 개요\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-data-mirroring-create\" target=\"_blank\"\u003E첫 번째 미러 생성하기\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/engineering/postgres-to-snowflake-replication-mirroring/\" target=\"_blank\"\u003ECDC를 Postgres 안으로 밀어 넣은 방법\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/deep-dive-replicate-data-from-postgres-to-snowflake-analytics-in-real-time-with-data-mirroring-2026-10-09/\" target=\"_blank\"\u003E웨비나 보기: ‘데이터 미러링으로 Postgres 데이터를 Snowflake 분석에 실시간 복제하기’\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Ci\u003E미래 전망 진술\u003C/i\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\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_title","blog_text_869256526","blog_title_2013926849","blog_text_1386755699","wistia_video","blog_text_217653424","blog_title_946332686","blog_text_74831782","quote_item_279022932","blog_title_59317549","blog_text_1391873441","quote_item","blog_title_276493659","blog_text_1093640949","code_snippet","blog_text_195343161"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"author_chip":"aem-GridColumn 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