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AI 에이전트 역시 필요한 거버넌스와 풍부한 시맨틱 컨텍스트를 갖춘 데이터가 아닌, 오래되고 단절된 데이터를 기반으로 추론하게 됩니다.\u003C/p\u003E\r\n\u003Cp\u003E개방형 레이크하우스는 단일 플랫폼에 종속되지 않으면서도 데이터 단편화 문제를 해결하겠다는 약속과 함께 등장했습니다. 하지만 대부분의 조직에서는 데이터 형식의 개방성만 확보되었을 뿐, 거버넌스와 시맨틱 단편화 문제는 여전히 해결 과제로 남아 있었습니다. 이제 상황이 달라졌습니다. Snowflake는 Apache Iceberg™, Apache Polaris™, Open Semantic Interchange(OSI)를 기반으로 한 \u003Ca href=\"http://www.snowflake.com/interoperability\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Cb\u003E상호운용 가능한 레이크하우스\u003C/b\u003E\u003C/a\u003E를 정식 출시(GA)했습니다. 이 아키텍처는 데이터가 어디에 저장되어 있든, 공급업체 종속 없이 단일 거버넌스 데이터 사본을 기반으로 데이터를 연결, 액세스 및 관리하며, 운영할 수 있는 새로운 청사진을 제시합니다. 조직은 데이터에 대한 통제권을 공급업체가 아닌 데이터 소유자에게 돌려줌으로써 진정한 데이터 주도권을 확보할 수 있습니다. 동시에 아키텍처 비용을 절감하고, 모든 AI 이니셔티브를 신뢰할 수 있는 데이터 기반 위에서 추진할 수 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E데이터가 있는 곳에서 바로 활용\u003C/h2\u003E\r\n\u003Cp\u003E데이터 주도권을 확보하려면 먼저 연결된 데이터 기반이 필요합니다. 데이터를 옮기거나 복제하지 않고도 모든 데이터를 한곳에서 활용할 수 있어야 하기 때문입니다. 이번 발표를 통해 Snowflake는 데이터 액세스의 모든 계층에서 이러한 기반을 한층 강화했습니다. Snowflake의 Apache Iceberg v3 지원이 정식 출시(GA)되어 프로덕션 환경에서 바로 활용할 수 있습니다. 현재 시장에서 가장 폭넓은 Iceberg v3 기능을 갖춘 Snowflake는 이를 플랫폼 전반에 긴밀하게 통합해 뛰어난 상호운용성을 실현합니다. 또한 Snowflake Storage for Apache Iceberg™ 테이블을 사용하면 관리형 Iceberg를 CREATE TABLE 명령 한 번으로 손쉽게 시작할 수 있습니다. 제로 카피 통합(Zero-copy integration)은 시맨틱 컨텍스트를 유지하면서 주요 운영 시스템을 데이터 기반에 연결합니다. Horizon Context는 모든 팀과 AI 에이전트가 활용하는 비즈니스 정의를 하나로 연결합니다. 이를 통해 더 많은 데이터와 더 풍부한 컨텍스트를 단일 거버넌스 데이터 사본으로 연결할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EApache Iceberg는 원래 대규모 분석 데이터 세트를 위해 설계되었습니다. 그러나 반정형 데이터, 빈번한 소규모 업데이트, 지리 공간 분석, 변경 추적 파이프라인과 같은 워크로드에 대해서는 지원이 충분하지 않았습니다. \u003Cb\u003EApache Iceberg v3\u003C/b\u003E는 이러한 한계를 해소합니다. Snowflake는 현재 프로덕션 환경에서 가장 폭넓은 Apache Iceberg v3 기능을 제공합니다. 여기에는 반정형 데이터를 위한 VARIANT 지원을 비롯해 엔진 간 변경 사항 추적을 위한 행 계보(row lineage), 고성능 행 수준 삭제를 지원하는 삭제 벡터(deletion vectors), 고빈도 텔레메트리 및 금융 워크로드를 위한 나노초 단위 타임스탬프(nanosecond timestamps), 기본값(default values), 공간 데이터 유형(geospatial types) 등이 포함됩니다. 이를 통해 더 많은 워크로드가 상호운용 가능한 데이터 환경으로 자연스럽게 전환됩니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 데이터 형식이 아무리 뛰어나더라도 스토리지 관리에 따르는 운영 부담까지 해결해 주지는 못합니다. AWS와 Azure에서 정식 출시된 \u003Cb\u003ESnowflake Storage for Apache Iceberg™\u003C/b\u003E 테이블은 완전 관리형 Iceberg 환경을 제공합니다. Google Cloud 지원도 곧 프라이빗 프리뷰로 제공될 예정입니다. 이 솔루션은 처음부터 개방형으로 설계되었으며, Horizon Catalog를 통한 거버넌스와 모든 Iceberg 호환 엔진에서의 읽기 및 쓰기를 지원합니다. 또한 Azure에서 자체 스토리지를 운영하는 조직을 위해 \u003Cb\u003EAzure DFS 지원\u003C/b\u003E이 정식 출시(GA)되어, Azure Data Lake Storage Gen2의 네이티브 엔드포인트를 통한 완전한 상호운용성을 제공합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-b02ffada6c","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c0f15095-9821-4c38-aeed-28d299538930/summitrollup3-fig1.png?quality=85&preferwebp=true","alt":"자료 1: 정식 출시(GA)된 Snowflake Storage for Apache Iceberg™","lazyEnabled":true,"height":"1365","width":"2048","title":"자료 1: 정식 출시(GA)된 Snowflake Storage for Apache Iceberg™",":type":"snowflake-site/components/image"},"blog_text_copy_1233411464":{"id":"blog-text-c1939b73c6","text":"\u003Cp\u003E기존 데이터를 활용하기 위해 복잡한 마이그레이션이나 데이터 변환 작업을 수행할 필요는 없습니다. 현재 프라이빗 프리뷰로 제공되는 \u003Cb\u003EParquet Direct\u003C/b\u003E는 기존 Parquet 파일을 Iceberg 수준의 성능으로 바로 쿼리할 수 있도록 지원하며, 곧 정식 출시될 예정입니다. 이뿐만 아니라, \u003Cb\u003EGoogle Cloud Lakehouse 통합\u003C/b\u003E이 정식 출시되어 Google의 크로스 클라우드 레이크하우스 환경을 위한 Catalog Linked Database를 생성하고, 테이블 자동 탐색과 크로스 클라우드 읽기/쓰기 기능을 제공합니다. 프라이빗 프리뷰로 제공되는 외부 관리형 Iceberg용 \u003Cb\u003E실시간 메타데이터 갱신(Just-in-time refresh)\u003C/b\u003E 기능은 쿼리 시점에 오래된 메타데이터를 자동으로 감지해 최신 상태로 업데이트하므로, 별도의 예약 작업을 구성할 필요가 없습니다.\u003C/p\u003E\r\n\u003Cp\u003E기업의 핵심 데이터는 대부분 엔터프라이즈 플랫폼에 저장되어 있으며, 동시에 가장 많은 데이터 파이프라인 비용이 발생하는 영역이기도 합니다. \u003Ca href=\"https://www.snowflake.com/en/product/features/zero-copy-integrations\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E제로 카피 통합\u003C/a\u003E은 ETL 파이프라인을 구축하거나 시맨틱 컨텍스트를 다시 정의할 필요 없이 핵심 비즈니스 데이터를 준실시간으로 Snowflake 환경에서 활용할 수 있도록 지원합니다. 현재 이 기능은 \u003Ca href=\"https://www.snowflake.com/ko/blog/sap-snowflake-zero-copy-integration-enterprise-ai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Cb\u003ESAP\u003C/b\u003E에서는\u003C/a\u003E 정식 출시(GA)로 제공되며, \u003Ca href=\"https://www.salesforce.com/data/partners/snowflake/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E\u003Cb\u003ESalesforce\u003C/b\u003E\u003C/a\u003E, \u003Cb\u003EWorkday\u003C/b\u003E(PrPr)에서도 사용할 수 있습니다. 또한 \u003Cb\u003EAVEVA\u003C/b\u003E 및 \u003Cb\u003EIBM\u003C/b\u003E과의 새로운 파트너십을 통해 AVEVA CONNECT의 운영 기술(OT) 및 산업 데이터와 IBM의 엔터프라이즈 데이터 플랫폼까지 이 모델을 확장할 예정입니다. 이를 바탕으로 비즈니스 정의와 컨텍스트를 통합해 보다 일관되고 AI 활용에 최적화된 데이터를 구축할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 시스템이 연결되어 있다고 해서 의미까지 연결되는 것은 아닙니다. 매출, 이탈률, 고객 수는 시스템마다 서로 다른 의미를 가질 수 있습니다. 이러한 비즈니스 기준이 하나의 연결된 계층에서 관리되지 않는 한, 데이터에 대한 공통된 이해를 기대하기 어렵습니다. \u003Cb\u003EHorizon Context\u003C/b\u003E는 바로 이러한 문제를 해결하기 위해 등장했습니다. Horizon Context는 데이터베이스, 데이터 레이크, BI 도구 전반에 흩어져 있는 비즈니스 정의를 연결하여 Snowflake 내부와 외부의 모든 팀은 물론 AI 에이전트까지 동일한 비즈니스 기준을 바탕으로 데이터를 해석하고 활용할 수 있도록 지원합니다. 또한 PostgreSQL, Microsoft SQL Server, Tableau, Microsoft Power BI, dbt를 비롯한 외부 데이터베이스, BI 및 데이터 파이프라인 시스템에 연결해 스키마, 쿼리 로그, 대시보드 정의 등 다양한 메타데이터를 수집할 수 있습니다(PrPr). Horizon Context는 다음과 같은 통합 기능을 통해 이러한 기반을 구현합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E기본 제공 커넥터:\u003C/b\u003E PostgreSQL, Microsoft SQL Server, Tableau, Microsoft Power BI, dbt와 같은 도구에 연결해 쿼리 로그, 사용 현황(popularity), 스키마 등 다양한 컨텍스트 정보를 여러 소스에서 수집하고, 이를 하나의 검색 가능한 카탈로그로 통합할 수 있습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E엔드투엔드 컬럼 수준 계보:\u003C/b\u003E 계보(lineage)는 데이터 자산 간 관계를 이해하는 데 핵심적인 역할을 합니다. Horizon Context는 Snowflake와 외부 데이터베이스의 쿼리 로그, BI 시스템, OpenLineage 피드에서 계보 정보를 수집한 뒤 이를 통합하여 완전한 엔드투엔드 계보 그래프를 구축합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESemantic Studio(PrPr)\u003C/b\u003E는 Snowflake Workspaces 내에서 제공되는 AI 지원 IDE로, SQL 전문 지식 없이도 팀이 공통 비즈니스 로직을 정의하고 테스트하며 배포할 수 있도록 지원합니다. 또한 Snowflake CoCo 통합과 Git 동기화를 통해 버전 관리 기능도 제공합니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESemantic View Autopilot(GA)\u003C/b\u003E은 기존 쿼리 패턴을 분석해 시맨틱 뷰를 자동 생성하고 지속적으로 개선하여 변화하는 데이터 환경에서도 컨텍스트 계층의 정확성과 최신성을 유지할 수 있도록 지원합니다. CoCo는 검색, SQL 생성, 복합 분석 과정에서 비즈니스 컨텍스트를 활용합니다.\u003C/li\u003E\r\n\u003Cli\u003E또한 이러한 정의는 \u003Cb\u003EOpen Semantic Interchange(OSI)\u003C/b\u003E를 통해 Snowflake를 넘어, 54개 참여 공급업체와 공개 표준 사양을 기반으로 하는 BI 및 AI 생태계 전반으로 확장됩니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E데이터에 질문을 던지고 원하는 답을 얻는 과정은 자연스럽게 이루어져야 합니다. 이는 연결된 상호운용 가능한 기반이 뒷받침되었을 때 실현할 수 있습니다. \u003Cb\u003EAgentic Queries(GA)\u003C/b\u003E를 사용하면 팀은 Snowflake와 데이터 레이크는 물론, 프라이빗 프리뷰로 제공되는 외부 관계형 시스템 전반에 걸쳐 자연어로 질문할 수 있습니다. \u003Cb\u003EHorizon Context\u003C/b\u003E는 거버넌스가 적용된 답변을 거의 실시간으로 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 이는 시작에 불과합니다. 개방형 형식을 포함한 공유 데이터 역시 대화형으로 활용할 수 있어야 합니다. 퍼블릭 프리뷰로 제공되는 \u003Cb\u003EAuto-gen Agents for Data Shares and Listings\u003C/b\u003E는 별도의 수작업 개발 없이 모든 데이터 리스팅 또는 보안 데이터 공유로부터 시맨틱 뷰와 에이전트를 자동으로 생성합니다. 이어서 퍼블릭 프리뷰로 제공되는 \u003Cb\u003ECortex Agent Sharing\u003C/b\u003E은 해당 에이전트를 Snowflake 계정 전반에 배포하고, 내부 팀과 파트너, 나아가 Marketplace를 통해 외부 생태계와도 공유할 수 있도록 지원합니다. 이러한 기능을 함께 활용하면 동일한 거버넌스 데이터 세트를 기반으로 새로운 사용자층과 다양한 활용 사례를 대화형 경험으로 확장할 수 있습니다. 또한 사용자는 공유된 데이터를 자체 퍼스트 파티 데이터와 결합해 더욱 풍부한 인사이트를 얻을 수 있습니다. 이 모든 과정에는 기본적으로 거버넌스가 적용됩니다.\u003C/p\u003E\r\n\u003Ch2\u003E범용 거버넌스\u003C/h2\u003E\r\n\u003Cp\u003E데이터를 이동하지 않고 활용하는 것만으로는 충분하지 않습니다. 이는 문제의 절반만 해결하는 데 그치기 때문입니다. 실제로 구축을 시작하면 더 큰 과제가 드러납니다. 바로 누가, 어디서, 어떤 방식으로 데이터를 관리할 것인가입니다. 멀티 카탈로그 환경에서는 정책이 분산되고, 멀티 엔진 접근 환경에서는 관리 복잡성이 더욱 커집니다. 우회 방안이 늘어날수록 데이터 주도권은 약화됩니다. 액세스 정책을 하나의 범용 카탈로그에서 단 한 번만 설정할 수 있다면 어떨까요? Snowflake는 Apache Polaris™ 기반의 \u003Cb\u003EHorizon Catalog\u003C/b\u003E에 새로운 기능을 추가했습니다. 이제 Snowflake 관리형 Iceberg뿐만 아니라 조직 전체의 모든 Iceberg 테이블에 일관된 거버넌스를 적용할 수 있습니다. Horizon에서 정의한 범용 거버넌스 정책은 공급업체 종속 없이 모든 IRC 호환 엔진에서 그대로 적용됩니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 비전은 프로덕션 환경에 적합한 상호운용 기반을 구축하는 데서 시작됩니다. Horizon Catalog는 \u003Cb\u003E이제 외부 엔진의 읽기 및 쓰기 액세스를 모두 정식 지원\u003C/b\u003E합니다. 이를 통해 Iceberg REST 프로토콜에 정의된 개방형 보안 메커니즘인 \u003Ca href=\"https://www.snowflake.com/ko/blog/snowflake-commitment-iceberg-interoperability/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E벤더 관리형 자격 증명(vended credentials)\u003C/a\u003E을 활용하여 Snowflake 관리형 Iceberg 테이블에 대해 완전한 양방향 상호운용성을 제공합니다. Spark, Trino, PyIceberg 및 모든 호환 엔진은 Snowflake 사용자가 활용하는 것과 동일한 거버넌스 데이터 사본에 대해 읽기와 쓰기를 수행할 수 있습니다. 하나의 카탈로그, 하나의 정책 집합만으로 선호하는 엔진을 자유롭게 사용하면서도 거버넌스 정책을 일관되게 유지할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E대부분의 기업은 여러 개의 카탈로그를 보유하고 있으며, 이 환경에서 일관된 거버넌스 정책을 적용하는 일은 비용과 복잡성이 모두 높습니다. 범용 거버넌스를 구현하려면 대규모 마이그레이션을 수행하거나, 각 카탈로그마다 거버넌스·감사·모니터링 제어를 중복 구축해 복잡성과 운영 부담을 데이터 팀에 전가하는 선택을 해야 하는 경우가 많습니다. 이는 결국 데이터에 대한 통제력을 약화시킵니다. Snowflake는 지난해 데이터를 저장된 위치에서 활용한다는 원칙에 기반하여 \u003Cb\u003E카탈로그 연결 데이터베이스(GA)\u003C/b\u003E를 출시했습니다. 이를 통해 Snowflake에서 모든 외부 Iceberg 테이블을 자동으로 탐색하고 안전하게 읽고 쓸 수 있게 되었습니다. 올해는 데이터를 이동하지 않고도 거버넌스를 적용할 수 있도록 지원하여 강제적인 마이그레이션 없이 데이터를 관리할 수 있게 되었습니다. 또한 현재 프라이빗 프리뷰로 제공되는 Horizon Iceberg REST Catalog API를 활용하면 외부 Iceberg 테이블에 대한 엔진 액세스를 읽기 및 쓰기 수준에서 안전하게 관리할 수 있습니다. 이를 통해 Horizon Catalog는 모든 Iceberg 테이블을 아우르는 범용 거버넌스 계층으로 진화합니다. 이제 어떤 엔진에서 수행되는 작업이든 하나의 플랫폼에서 거버넌스, 감사 및 옵저버빌리티 기능을 통합적으로 제공할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E카탈로그가 분산되는 또 다른 주요 원인은 세분화된 액세스 제어가 특정 엔진에 연결된 카탈로그 내에서만 적용되었기 때문입니다. 이러한 제약은 멀티 엔진 환경을 관리하는 부담을 높일 뿐 아니라, 정책 설정 오류로 인한 데이터 유출 위험도 증가시킵니다. 현재 프라이빗 프리뷰로 제공되는 \u003Cb\u003EIceberg REST Scan Plan API\u003C/b\u003E 지원은 이러한 제약을 해소합니다. 이 기능을 통해 세분화된 액세스 정책은 데이터가 쿼리되는 위치와 관계없이 데이터와 함께 이동합니다. 따라서 Horizon Catalog에서 Snowflake 관리형 Iceberg 테이블에 정의한 행 액세스 및 동적 데이터 마스킹 정책을 외부 엔진에서도 그대로 적용할 수 있습니다. 또한, 새로운 \u003Cb\u003ESnowflake Connector for Apache Spark\u003C/b\u003E(GA)는 이러한 정책을 Spark 환경에서도 적용할 수 있도록 지원하여 즉시 활용 가능한 프로덕션 수준의 솔루션을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake는 \u003Ca href=\"https://www.snowflake.com/en/blog/data-sharing-open-table-formats/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EOpen Data Sharing\u003C/a\u003E의 활용 범위도 확장하고 있습니다. 고객은 곧 정식 출시(GA)될 \u003Cb\u003ECatalog Linked Databases를 통해 페더레이션 카탈로그를 공유\u003C/b\u003E할 수 있습니다. 이뿐만 아니라 퍼블릭 프리뷰로 제공되는 Open Data Sharing 개선 사항을 통해 \u003Cb\u003ESnowflake 계정 없이 모든 IRC 호환 외부 엔진에서 데이터 공유를 활용할 수 있습니다\u003C/b\u003E. 이 두 기능을 함께 사용하면 고객은 Horizon을 통해 액세스 가능한 모든 개방형 테이블 형식을 외부 엔진에서 안전하게 활용할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E정책이 일관되게 적용될 수 있는 이유는 연결 자체가 안전하게 보호되기 때문입니다. \u003Cb\u003E외부 카탈로그 및 스토리지에 대한 Private Link가 정식 출시(GA)되어\u003C/b\u003E Snowflake가 외부 데이터 레이크에 연결하는 과정에서도 데이터가 퍼블릭 인터넷을 거치지 않도록 보호합니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 개방형 표준을 기반으로 하기 때문에 가능합니다. Apache Polaris는 Apache Software Foundation의 최상위 프로젝트로 자리매김했으며, Snowflake 엔지니어들은 Apache Iceberg 프로젝트에 Scan Planning API 사양을 기여하며 생태계 발전에 힘을 보태고 있습니다. 그 결과 범용 거버넌스는 단순한 Snowflake 기능이 아니라, 생태계 전반을 위한 솔루션으로 발전하고 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E엔터프라이즈 환경에 바로 적용 가능한 기반\u003C/h2\u003E\r\n\u003Cp\u003E데이터를 저장된 위치에서 활용하고 일관된 거버넌스를 적용하는 것이 바로 이 아키텍처의 핵심입니다. 하지만 이를 실제 프로덕션 환경에서 안정적으로 운영하는 것은 또 다른 과제입니다. 대부분의 레이크하우스 아키텍처에서는 이러한 책임이 결국 아키텍트와 운영팀에 돌아갑니다. 상태 점검 체계를 직접 구축해야 하고, 여러 엔진에 흩어진 감사 로그를 통합해야 하며, 장애 복원력 역시 별도의 프로젝트로 구현해야 합니다. 이제 이러한 운영 부담을 줄일 수 있습니다. 현재 프라이빗 프리뷰로 제공되는 Access History의 포괄적 감사(comprehensive auditing) 기능은 외부 엔진에서 수행되는 모든 작업을 Snowflake Access History에 직접 기록합니다. 이를 통해 사용한 엔진이나 액세스한 테이블과 관계없이 규정 준수 및 보안 팀은 사용자 수준의 모든 테이블 작업을 단일 감사 기록에서 확인할 수 있습니다. 또한 프라이빗 프리뷰로 제공되는 카탈로그 연결 데이터베이스용 운영 상태 모니터링(operational health monitoring) 기능은 외부 관리형 Iceberg 테이블의 최신성 및 새로 고침 관련 문제를 사전에 감지하여, 프로덕션 환경에 영향을 주기 전에 대응할 수 있도록 지원합니다. 곧 정식 출시(GA)될 관리형 Iceberg 복제 기능은 동일한 개방형 데이터 기반에 기본적인 장애 복원력을 제공합니다. 이를 통해 별도의 통합 프로젝트 없이도 엔터프라이즈 환경에 적합한 운영 체계를 구축할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E규정 준수 팀은 오랫동안 여러 엔진에 분산된 감사 로그를 일일이 대조해야 했습니다. 프라이빗 프리뷰로 제공되는 Access History의 포괄적 감사 기능은 외부 엔진의 모든 작업을 Snowflake Access History에 직접 기록함으로써 이러한 부담을 해소합니다. 모든 액세스 이벤트는 하나의 감사 기록에 통합되어 누가, 언제, 어디서, 어떤 데이터에 접근했는지를 한곳에서 확인할 수 있습니다. 이를 통해 아키텍트는 단일 플랫폼에서 감사 요구사항에 대응할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E프라이빗 프리뷰로 제공되는 Snowsight의 Iceberg Health Insights는 플랫폼 팀에 외부 관리형 Iceberg 환경 전반에 대한 통합 운영 가시성을 제공합니다. 자동 새로 고침 상태, 테이블 탐색 현황, 최신성 지표 등을 확인할 수 있으며, 여러 클라우드 콘솔을 오가거나 별도의 모니터링 시스템을 구축할 필요가 없습니다. 카탈로그 연결 데이터베이스에서 오래된 메타데이터가 감지되거나 새로 고침 파이프라인이 중단되면, 팀은 하나의 화면에서 문제를 확인하고 다운스트림 쿼리가 오래된 결과를 반환하기 전에 해결할 수 있습니다. 이 기능은 향후 정식 출시를 거쳐 Snowflake 관리형 및 외부 관리형을 포함한 전체 Iceberg 환경으로 확장될 예정이며, 프로덕션 레이크하우스 운영에 필요한 가시성과 신뢰성을 제공하게 됩니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_copy":{"id":"image-d60767d229","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3ac044c5-e8f3-4a2f-95d6-daca7236d8aa/summitrollup3-fig2.png?quality=85&preferwebp=true","alt":"자료 2: 단일 대시보드를 통한 외부 테이블 상태 모니터링 및 테이블 연결/새로 고침 문제 진단","lazyEnabled":true,"height":"1324","width":"2048","title":"자료 2: 단일 대시보드를 통한 외부 테이블 상태 모니터링 및 테이블 연결/새로 고침 문제 진단",":type":"snowflake-site/components/image"},"image_copy_2041984129":{"id":"image-a47dd37fb1","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--75beb559-0b8a-46c9-8bf7-47f29acb7dba/summitrollup3-fig3.png?quality=85&preferwebp=true","alt":"자료 3: Cortex Code 기반 테이블 새로 고침 문제 해결 및 Refresh Issues 화면을 통한 오류 분석","lazyEnabled":true,"height":"1815","width":"2048","title":"자료 3: Cortex Code 기반 테이블 새로 고침 문제 해결 및 Refresh Issues 화면을 통한 오류 분석",":type":"snowflake-site/components/image"},"blog_text_copy":{"id":"blog-text-1c3ce9ead8","text":"\u003Cp\u003E복원력은 별도의 프로젝트가 아니라 데이터 기반 자체에 내재되어야 합니다. 곧 \u003Cb\u003E정식 출시\u003C/b\u003E될 Snowflake의 \u003Cb\u003E관리형 Iceberg 복제 및 장애 조치(Managed Iceberg replication and failover)\u003C/b\u003E는 계정 복제 및 장애 조치 기능을 Snowflake 관리형 Iceberg 테이블까지 확장합니다. 이를 통해 조직은 개방형 데이터 기반의 장애 복원력을 한층 강화할 수 있습니다. 현재 퍼블릭 프리뷰로 제공되는 \u003Cb\u003EOptimized Refresh\u003C/b\u003E는 장애 조치 그룹(failover group)을 위한 새로운 복제 기능으로 복원력을 한층 강화합니다. Snowflake의 차세대 로그 기반 복제 엔진을 기반으로 하는 Optimized Refresh는 변경 사항을 실시간으로 추적하고 필요한 부분만 업데이트합니다. 프리뷰 고객들은 기존 대비 1.6배에서 최대 22배까지 향상된 복제 성능을 경험했으며, 이를 통해 복제 데이터 양에 기반한 예측 가능한 비용 구조를 유지하면서도 미션 크리티컬 워크로드의 복구 시점 목표(RPO)를 더욱 단축할 수 있었습니다.\u003C/p\u003E\n\u003Cp\u003E이러한 기능이 Snowflake 플랫폼에 기본 제공되면서 조직은 환경을 재설계하지 않고도 데이터, 애플리케이션, 파이프라인에 대한 장애 조치를 최소한의 운영 부담으로 수행할 수 있습니다. 그 결과 조직은 핵심 워크로드에 필요한 운영 복원력을 유지하면서도 안심하고 Iceberg를 전사적으로 활용할 수 있습니다.\u003C/p\u003E\n\u003Ch2\u003E데이터 주도권 확보\u003C/h2\u003E\n\u003Cp\u003E개방형 레이크하우스는 데이터 이동을 줄이고 더 큰 가치를 창출할 수 있다고 약속했습니다. 하지만 대부분의 기업에서 개방성은 테이블 형식 수준에 머물렀습니다. 거버넌스는 분산되었고, 시맨틱은 사일로화되었으며, 프로덕션 환경의 요구사항을 충족하기 위해서는 여전히 별도의 프로젝트가 필요했습니다. AI의 등장으로 거버넌스와 시맨틱 단편화 문제는 더 이상 간과할 수 없는 과제가 되었습니다. 오래되고 단절된 데이터를 기반으로 추론하는 에이전트는 조직이 구축하는 시스템에 대한 신뢰를 약화시킵니다.\u003C/p\u003E\n\u003Cp\u003E상호운용 가능한 레이크하우스는 데이터 형식만으로는 해결할 수 없었던 문제를 해소합니다. 스토리지부터 거버넌스, 시맨틱에 이르기까지 모든 계층에서 상호운용성을 제공하며, 각 요소가 서로를 강화하는 연결된 데이터 기반을 구축합니다. 이것이 실제로 의미하는 바는 무엇일까요? 엔지니어는 데이터를 복제하지 않고도 각 워크로드에 가장 적합한 엔진을 선택할 수 있습니다. 거버넌스 팀은 정책을 한 번만 정의하면 Snowflake, Apache Spark, Trino 등 다양한 환경에 동일하게 적용할 수 있습니다. 또한 별도의 운영 프로젝트 없이도 Iceberg 환경 전반에 대한 옵저버빌리티, 감사 기능, 복원력을 확보할 수 있습니다. 이를 통해 처음부터 거버넌스가 적용된 풍부한 시맨틱 데이터 위에서 AI 이니셔티브를 실행할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이것이 바로 진정한 데이터 주도권이며, 단순한 메시지가 아닌, 데이터 전략을 이끄는 핵심 원칙입니다. 아키텍처는 공급업체가 허용하는 범위가 아니라, 비즈니스와 AI가 필요로 하는 방향에 맞춰 설계되어야 합니다.\u003C/p\u003E\n\u003Cp\u003E상호운용 가능한 데이터 기반은 이미 준비되어 있습니다.\u003C/p\u003E\n\u003Cp\u003E이제 그 기반 위에서 구축하세요.\u003C/p\u003E\n\u003Cp\u003E데이터 주도권을 되찾기 위한 첫걸음으로 Snowflake의 \u003Ca href=\"http://www.snowflake.com/interoperability\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E상호운용 가능한 레이크하우스\u003C/a\u003E 페이지를 방문해 관련 솔루션을 살펴보세요. 또한 무료 eBook ‘\u003Ca href=\"https://www.snowflake.com/en/resources/ebook/building-the-interoperable-lakehouse-data-strategies-for-ai-leaders/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBuilding the Interoperable Lakehouse: Data Strategies for AI Leaders\u003C/a\u003E’를 다운로드하거나 Snowflake 제품 관리 디렉터 James Roland-Jones가 함께하는 \u003Ca href=\"https://tdwi.org/webcasts/2026/05/arch-all-building-the-interoperable-lakehouse-a-data-foundation-for-enterprise-ai.aspx\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETDWI 웨비나\u003C/a\u003E를 통해 자세한 내용을 확인할 수 있습니다. 이후 ‘\u003Ca href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/Build-a-multi-engine-stack-on-snowflake-storage-for-iceberg-and-horizon-catalog-2026-05-19/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBuild a Multi-Engine Stack on Snowflake Storage for Iceberg and Horizon Catalog\u003C/a\u003E’ 가상 핸즈온 랩에서 직접 기능을 체험해 보세요.\u003C/p\u003E\n\u003Chr\u003E\n\n\u003Cp\u003E\u003Ci\u003E미래 전망 진술\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E본 콘텐츠에는 향후 제품 제공에 대한 미래 전망 진술이 포함되어 있으며, 이는 제품 제공에 대한 약속이 아닙니다. 실제 결과와 제공 내용은 달라질 수 있으며, 알려져 있거나 알려지지 않은 위험과 불확실성의 영향을 받을 수 있습니다. 보다 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_copy_723428476","image","blog_text_copy_1233411464","image_copy","image_copy_2041984129","blog_text_copy"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"gridClassNames":"aem-Grid aem-Grid--12 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Management"},{"authorImage":{"id":"image-e63dd61f8a","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--896ed406-dc05-44fc-923f-c331742e5dcc/carlos-nai.jpg?quality=85&preferwebp=true","alt":"Headshot-style photo of Carlos Nai","lazyEnabled":true,"height":"450","width":"450",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-5013e00a8c","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/carlos-nai/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Carlos Nai"},"authorTitle":"Product Marketing Lead, Data Lakes at 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