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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"]}},":itemsOrder":["container_949147658"]},"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","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"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",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-6809a54cdb","type":"heading2","lines":["개요"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-832e2a8e74","text":"\u003Cp\u003E빅 데이터 분석은 대규모의 복잡한 데이터 세트를 처리하는 혁신적인 접근 방식입니다. 처음에는 다소 딱딱하게 들릴 수 있지만, 분석은 추세를 발견하고 운영을 최적화하며 산업 전반에서 데이터 기반 의사 결정을 가능하게 하는 가치 있는 인사이트를 제공합니다. 정형 및 비정형 데이터의 방대한 집합을 빠르게 분석하고 인텔리전스를 도출하는 능력은 헬스케어부터 제조에 이르기까지 다양한 분야에서 혁신과 발전을 이끌어 왔습니다. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"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":"is",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-0b310a6caf","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석이란?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-9adf0cab14","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E빅 데이터 분석은 방대하고 다양한 데이터 세트를 신속하게 수집하고 분석하여 의미 있는 상업적 또는 과학적 인사이트를 도출하는 과정입니다. 특히 빅 데이터 분석 서비스는 대규모의 데이터 볼륨, 빠른 데이터 흐름, 다양한 데이터 형식(구조화, 반구조화, 비정형)으로 인해 발생하는 문제를 해결하도록 설계되었습니다. 확장 가능한 클라우드 네이티브 컴퓨팅 파워를 활용하여, 분석은 레거시 처리 시스템에서는 보이지 않던 예측 인사이트와 추세를 추출합니다. 궁극적으로 조직이 단순한 사후 보고 중심의 분석에서 벗어나 선제적 데이터 기반 전략과 더 나은 의사 결정을 수행하도록 지원합니다. \u003C/p\u003E\r\n\u003Cp\u003E빅 데이터 분석은 단순한 요약이 아니라 데이터의 규모와 복잡성에 초점을 맞춘다는 점에서 기존 데이터 분석과 근본적으로 다릅니다. 기존 분석은 제한된 정형 데이터 샘플을 기반으로 과거 사건을 분석하는 반면, 빅 데이터 분석은 다양한 형식의 고속 데이터를 전체적으로 처리하여 예측 모델을 구축합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E빅 데이터 분석의 5V\u003C/h3\u003E\r\n\u003Cp\u003E전통적으로 빅 데이터의 복잡성은 데이터의 규모(volume), 속도(velocity), 다양성(variety)으로 정의되었지만, 현대적인 정의에서는 빅 데이터 분석의 핵심 과제와 목표를 보다 완전히 설명하기 위해 5V로 확장됩니다. 추가된 요소인 정확성(veracity)과 가치(value)는 데이터가 신뢰할 수 있는지, 그리고 궁극적으로 비즈니스에 실질적인 가치를 창출하는지를 결정합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E규모(Volume)\u003C/b\u003E: 매일 생성되는 데이터의 방대한 규모를 의미하며, 페타바이트 또는 엑사바이트 수준에 이를 수 있습니다. 기존 데이터베이스는 이러한 규모의 데이터를 저장하거나 효율적으로 조회하기 어렵습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E속도(Velocity)\u003C/b\u003E: 데이터가 생성되고 수집되며 처리되는 속도를 의미합니다. 사기 감지나 주식 거래와 같은 많은 최신 사용 사례에서는 인사이트를 준실시간에 가깝게, 때로는 밀리초 단위로 도출해야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E다양성(Variety)\u003C/b\u003E: 오늘날 데이터는 다양한 형식으로 존재합니다. 예를 들어 트랜잭션 기록과 같은 정형 데이터, 센서 데이터와 웹 로그와 같은 반정형 데이터, 소셜 미디어 게시물이나 동영상과 같은 비정형 데이터가 있습니다. 효과적인 분석은 이러한 다양한 데이터 유형을 통합해 함께 분석할 수 있어야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E정확성(Veracity)\u003C/b\u003E: 데이터는 분석 또는 AI 모델에 사용하기 위해 신뢰할 수 있고 정확해야 하며, 그렇지 않으면 잘못된 결과로 이어질 수 있습니다. 정확성이 낮은 데이터를 사용할 경우 분석을 통해 도출된 인사이트가 신뢰 가능하고 왜곡되지 않도록 데이터 정제, 거버넌스 및 품질 보증과 같은 강력한 프로세스가 필요합니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E가치(Value)\u003C/b\u003E: 가치는 빅 데이터 분석의 궁극적인 목표로, 방대한 데이터 세트에서 의미 있고 유용하며 수익성 있는 인사이트를 추출하는 것입니다. 기업이 규모, 속도, 다양성 및 정확성을 성공적으로 달성하면 가치라는 비즈니스 성과가 창출됩니다. 가치에는 경쟁 우위, 비용 절감, 의사 결정 최적화, 혁신 또는 새로운 수익원이 포함됩니다. 즉, 데이터는 궁극적으로 조직에 가치를 창출할 때 비로소 ‘빅 데이터’라고 할 수 있습니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_2060034519":{"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":"work",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-afadad3b48","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터와 기존 데이터 비교"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-79368e74bd","text":"\u003Cp\u003E데이터가 빅 데이터인지 기존 데이터인지를 결정하는 단일 기준은 없습니다. 근본적인 차이에는 앞서 설명한 가치 요소와 기존 분석 도구로 효과적으로 분석할 수 있는지 여부가 포함됩니다. 기존 데이터는 데이터베이스에 저장된 것처럼 정형 구조를 가지며 SQL과 같은 전통적인 쿼리 도구와 통계 기법을 통해 분석됩니다. 빅 데이터는 빠르게 변화하며, 정형, 비정형 및 반정형 데이터를 포함한 방대한 데이터 세트를 포함합니다. 기존 데이터 분석 도구는 빅 데이터의 규모나 복잡성을 처리하거나 분석할 수 없으므로, 빅 데이터는 분석을 위해 분산 시스템과 머신러닝과 같은 고급 도구가 필요합니다. \u003C/p\u003E\n\u003Cp\u003E기존 데이터 분석은 단일 정형 금융 데이터베이스에서 하루 매출 보고서를 생성하는 것처럼, 예측 가능한 배치 처리 방식으로 관리 가능한 규모의 데이터를 처리합니다. 반면 빅 데이터 분석 솔루션은 전 세계 차량 공유 서비스가 수백만 대의 차량을 모니터링하는 것처럼, 대규모 스트리밍 데이터를 처리할 때 필요합니다. 이 경우 데이터는 밀리초 단위의 높은 속도로 수집되고 처리되어, 실시간 예상 도착 시간과 동적 요금을 계산할 수 있어야 합니다. \u003C/p\u003E\n\u003Cp\u003E여기서는 빅 데이터가 정형 GPS 좌표와 비정형 운전자 피드백 텍스트 및 이미지를 통합하여 다양한 데이터 유형을 함께 처리해야 합니다. 정확성(신뢰성)을 관리하고 궁극적인 비즈니스 가치를 추출하기 위해 정교한 기술이 필요하며, 이러한 복잡성은 기존 시스템에서 처리하도록 설계된 범위를 넘어섭니다.\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_copy","text_copy"]},"container_copy_copy_":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":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":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-41a239d251","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석의 작동 방식"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-150933a2e7","text":"\u003Cp\u003E빅 데이터 분석은 방대한 규모와 복잡성을 처리하도록 설계된 체계적인 엔드투엔드 워크플로우를 통해 작동하며, 궁극적으로 가공 전 데이터를 실행 가능한 인사이트로 전환합니다. 이 과정을 설명하기 위해, 실시간 금융 사기를 방지하려는 대형 신용카드 회사를 가상의 사례로 살펴보겠습니다.\u003C/p\u003E\r\n\u003Cp\u003E이 예시에서의 워크플로우는 빅 데이터 분석 인사이트가 트랜잭션 데이터의 지속적인 스트림을 예측 모델과 즉각적인 경고로 어떻게 전환하는지를 보여 줍니다. 이러한 과정의 각 단계에는 전문적인 클라우드 기술이 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 수집\u003C/h3\u003E\r\n\u003Cp\u003E이 초기 단계에서는 다양한 소스에서 대규모의 고속 데이터를 수집합니다. 예를 들어, 이 신용카드 회사는 구매, ATM 인출, 온라인 로그인 시도 등 전 세계 카드 결제 단말기, 전자상거래 API, 애플리케이션 로그에서 발생하는 수십억 건의 실시간 이벤트를 지속적으로 수집합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 정제 및 전처리\u003C/h3\u003E\r\n\u003Cp\u003E가공 전 데이터는 종종 정리가 되어 있지 않으므로 정확성을 보장하기 위해서는 즉각적인 정제, 표준화 및 보강이 필요합니다. 이는 데이터 변환 단계입니다. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-discovery/\"\u003E데이터 탐색\u003C/a\u003E은 이러한 단계의 핵심 과정으로 분석 전 또는 분석 중에 데이터를 탐색하고 이해하는 활동을 포함합니다. 중복, 오류 또는 취약한 데이터는 잘못된 인사이트를 초래할 수 있습니다. 신용카드 회사 사례에서는 데이터 스트림을 누락된 필드 확인, 표준화(예: 시간대 통일) 및 사기 이력 블랙리스트 또는 고객 위치 이력과 같은 외부 데이터로 보강하는 작업을 즉시 수행합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 저장 및 관리\u003C/h3\u003E\r\n\u003Cp\u003E변환된 방대한 데이터 세트는 컴퓨팅과 스토리지를 분리하는 확장 가능하고 유연한 아키텍처에 저장되어야 합니다. 신용카드 회사의 정제된 데이터는 페타바이트 규모의 데이터를 처리하는 클라우드 데이터 플랫폼에 저장되며, 여러 분석 팀이 성능 저하 없이 동일한 단일 진실 공급원에 접근할 수 있도록 합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 분석 \u003C/h3\u003E\r\n\u003Cp\u003E여기에서 머신러닝과 통계 모델링 같은 고급 기법을 적용해 패턴을 발견하고 결과를 예측합니다. 예를 들어 머신러닝 모델은 사용자의 구매 이력, 위치 및 소비 습관을 실시간 트랜잭션과 비교 분석할 수 있습니다. 모델이 통계적으로 유의미한 이상 징후(예: 1시간 내 두 개 대륙에서 결제 발생)를 감지하면 트랜잭션을 이상 거래로 표시합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 시각화 및 보고\u003C/h3\u003E\r\n\u003Cp\u003E마지막 단계에서는 복잡한 분석 결과를 대시보드나 자동화된 작업 형태로 비즈니스 사용자에게 제공합니다. 가상의 신용카드 회사에서는 자동 시스템이 의심스러운 트랜잭션을 즉시 차단하고 고객에게 사기 경고 메시지를 전송합니다. 한편, 데이터 분석가는 전략 계획을 위해 다양한 지역과 카드 유형의 사기 시도에 대한 집계 추세를 보여주는 대시보드를 확인합니다.\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"]},"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":"and",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-8e5e5cef88","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석이 중요한 이유"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1e034cb00b","text":"\u003Cp\u003E빅 데이터 분석은 사후 보고 중심의 분석을 넘어 예측 인사이트와 분석으로 확장된다는 점에서 비즈니스 인텔리전스의 핵심 구성 요소입니다. 방대한 데이터 저장소, 심지어 비정형 데이터를 실행 가능한 통찰력으로 전환하는 이 메커니즘은 수익에서 효율성, 고객 경험에 이르기까지 모든 것을 주도하여 기업에 막대한 경쟁 우위를 제공합니다. \u003C/p\u003E\n\u003Cp\u003E이 프로세스는 추측에 의존하던 의사 결정을 앞으로 어떤 일이 발생할 가능성이 있는지와 어떻게 대응하는 것이 가장 적절한지를 알려주는 인텔리전스로 대체함으로써 의사 결정을 즉각적으로 향상시키고 경쟁 우위를 창출합니다.\u003C/p\u003E\n\u003Cp\u003E전략적 활용을 넘어 빅 데이터는 수익성과 고객 관계에도 직접적인 영향을 미칩니다. 모든 기계와 시스템에서 생성되는 스트리밍 데이터를 분석하여 운영 효율성을 높이고, 이를 통해 예지 정비와 전사적 워크플로우 전반의 비효율적 비용을 제거할 수 있습니다. 동시에 트랜잭션 데이터와 소셜 감성 같은 다양한 데이터를 결합해 고객에 대한 세분화된 360도 뷰를 구축함으로써 초개인화된 오퍼를 지원하고 고객 충성도를 강화합니다. 초고속으로 생성되는 데이터를 분석하는 것은 위험 완화를 위한 주요 방어 수단으로 작동하며, 실시간으로 금융 사기나 사이버 침입과 같은 \u003Ca href=\"https://www.snowflake.com/ko/solutions/departments/cybersecurity/\"\u003E위협\u003C/a\u003E을 즉시 탐지하고 대응합니다.\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"]},"container_copy_copy_259718203":{"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",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-b1c3c64881","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석 방법"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-83029e691a","text":"\u003Cp\u003E빅 데이터 분석에는 몇 가지 주요 분석 접근 방식이 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E기술적 분석\u003C/h3\u003E\r\n\u003Cp\u003E기술적 분석은 과거에 무엇이 발생했는지를 이해하기 위해 데이터를 활용하는 가장 기본적인 분석 방식입니다. 과거 분기의 영업 데이터와 같이 과거 사건과 성과에 대한 컨텍스트를 제공하기 위해 데이터를 집계하고 계산하며 요약하는 과정을 포함합니다. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E진단 분석\u003C/h3\u003E\r\n\u003Cp\u003E데이터 마이닝과 인과관계 분석 같은 기법은 어떤 일이 왜 발생했는지를 밝혀 특정 결과의 근본 원인을 파악하는 데 목적이 있습니다. 예를 들어 고객 리드를 창출하거나 고객 이탈을 줄인 캠페인이 무엇이었는지 분석하는 경우가 이에 해당합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E예측 분석\u003C/h3\u003E\r\n\u003Cp\u003E통계 모델과 머신러닝을 활용하는 이 분석 방식은 과거 패턴을 기반으로 미래의 확률을 예측하며 “다음에 무엇이 일어날 가능성이 높은가?”라는 질문에 답합니다. 이러한 방식은 다양한 산업과 사용 사례에 적용될 수 있으며, 예를 들어 미래 영업 수요 예측, 차량 운영에서의 정비 시점 예측, 또는 학생들이 학업에서 뒤처질 가능성을 미리 파악해 선제적으로 지원하는 식으로 활용될 수 있습니다. 예측 분석은 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003E마케팅\u003C/a\u003E 분야에서도 강력한 도구로 활용되며, 데이터 중심 인사이트를 통해 캠페인을 설계하고 고객을 유치 및 유지하며 관계를 강화하는 데 도움을 줍니다. 여기서 빅 데이터 분석의 역량이 \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-intelligence/\"\u003EML 및 AI 모델\u003C/a\u003E의 활용을 가능하게 합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E처방적 분석\u003C/h3\u003E\r\n\u003Cp\u003E처방적 분석은 빅 데이터 분석의 가장 고도화된 형태 중 하나로, 발생 가능성을 예측하는 것을 넘어 어떤 조치를 취해야 하는지까지 제시하는 가장 고도화된 분석 방식 중 하나입니다. 이 분석 방식은 종종 최적화와 시뮬레이션을 활용해 실시간 의사 결정을 지원하며, 예를 들어 현재 재고와 수요를 기반으로 가격을 동적으로 조정하여 수익을 극대화하는 데 활용됩니다.\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"]},"container_copy_289473020":{"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":"equation",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-00d8864c6b","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석의 이점"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-99d26c93bd","text":"\u003Cp\u003E빅 데이터 분석은 수익성, 전략 및 리스크에 직접적인 영향을 미치는 핵심 데이터 중심 이점을 폭넓게 제공함으로써, 엔터프라이즈 전반에 걸쳐 측정 가능한 가치를 제공합니다. 대표적인 이점은 다음과 같습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E비용 절감 및 운영 효율성\u003C/h3\u003E\r\n\u003Cp\u003E공급망과 센서에서 유입되는 스트리밍 데이터를 분석하면 기업은 낭비 요소를 식별하고 제거할 수 있으며, 이를 통해 정밀한 리소스 할당이 가능해지고 예지 정비를 통해 고비용의 장비 가동 중지 시간을 방지할 수 있습니다. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E고도화된 고객 인사이트\u003C/h3\u003E\r\n\u003Cp\u003E조직은 트랜잭션 데이터와 소셜 미디어 감정과 같은 비정형 데이터를 통합하고 분석함으로써 소비자에 대한 세분화된 360도 뷰를 확보하고, 단순한 인구통계학적 이해를 넘어서는 깊이 있는 이해를 얻을 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E의사 결정 속도 개선\u003C/h3\u003E\r\n\u003Cp\u003E빅 데이터는 리더가 추측에 의존한 의사 결정을 넘어 미래 결과를 예측하고 최적의 대응 전략까지 제시하는 데이터 중심의 고정밀 인텔리전스를 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E제품 개발 개선\u003C/h3\u003E\r\n\u003Cp\u003E분석은 고객 행동과 선호에서 숨겨진 패턴을 찾아내어, 제품 팀이 기존 제품을 개선하거나 시장 수요를 직접 겨냥한 혁신적인 신규 기능을 신속하게 개발하도록 안내합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E경쟁 우위\u003C/h3\u003E\r\n\u003Cp\u003E빅데이터 분석을 통해 기업은 전략과 혁신에 필요한 깊이 있고 시의적절한 인사이트를 활용하여 시장에서의 입지를 강화하고, 매출 증대와 고객 만족도 향상을 이끌 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E위험 관리 및 사기 감지\u003C/h3\u003E\r\n\u003Cp\u003E초고속으로 데이터를 처리하는 능력은 최신 보안 환경에서 필수적인 역량으로, 금융 기관과 보안 팀이 이상 거래나 사이버 침입을 실시간으로 감지하고 차단할 수 있도록 지원합니다.\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"]},"container_copy_copy__1985496925":{"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":"why",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a1fbb8da41","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석의 과제"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d182167a62","text":"\u003Cp\u003E빅 데이터 분석이 지닌 혁신적인 잠재력에도 불구하고 조직은 5V에 내재된 방대한 규모와 복잡성 때문에 이 데이터를 효과적으로 활용하는 데 상당한 과제에 직면합니다. 이러한 어려움은 기술, 보안 및 인재 영역 전반에 걸쳐 나타나며, 이를 극복하려면 최신 통합 솔루션이 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 볼륨\u003C/h3\u003E\r\n\u003Cp\u003E페타바이트 규모의 정보를 저장하고 처리하는 것만으로도 상당한 비용이 발생하며 기존 인프라는 매일 생성되는 방대한 규모의 데이터를 처리하는 데 한계를 보입니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 다양성\u003C/h3\u003E\r\n\u003Cp\u003E정형 데이터베이스부터 비정형 비디오와 텍스트에 이르기까지 다양한 데이터 형식을 통합하고 조정해야 하는 필요는 복잡하고 노동 집약적인 데이터 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003E파이프라인\u003C/a\u003E을 발생시킵니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 속도\u003C/h3\u003E\r\n\u003Cp\u003E지속적으로 유입되는 고속 데이터 스트림을 실시간으로 처리하려면 효율적으로 구축하고 관리할 수 있는 전문적인 스트리밍 기술이 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 품질 및 정확성\u003C/h3\u003E\r\n\u003Cp\u003E데이터가 서로 다른 신뢰 수준의 다양한 소스에서 생성되기 때문에, 정보의 정확성, 일관성 및 신뢰성을 보장하는 일은 여전히 근본적인 과제로 남아 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E보안 및 개인정보 보호\u003C/h3\u003E\r\n\u003Cp\u003E고객 민감 정보를 포함하는 대규모 분산 데이터 세트를 보호하는 과정에서는 복잡한 규정 준수 리스크 및 거버넌스 과제가 발생하며, 이는 다양한 글로벌 규제의 적용을 받을 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E확장성 및 성능 \u003C/h3\u003E\r\n\u003Cp\u003E조직은 쿼리 속도를 저하시키거나 과도한 운영 비용을 발생시키지 않으면서 변동하는 수요에 맞춰 데이터 플랫폼을 즉시 확장할 수 있도록 하는 과제에 직면합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E인재 및 전문 지식\u003C/h3\u003E\r\n\u003Cp\u003E빅 데이터 분석 방법에는 복잡한 빅데이터 기술을 관리하고 최적화하며 가치를 도출할 수 있는 숙련된 데이터 과학자와 엔지니어가 필요합니다.\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"]},"container_copy":{"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":"types",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-e9130917ba","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석 사용 사례"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-15cbcfccbd","text":"\u003Cp\u003E최신 클라우드 데이터 플랫폼을 기반으로 할 때 빅 데이터 분석을 활용하면 주요 산업 전반의 특정 데이터 과제에 맞춘 차별화된 고부가가치 사용 사례를 구현할 수 있습니다. 대표적인 사용 사례는 다음과 같습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E헬스케어\u003C/h3\u003E\r\n\u003Cp\u003E비정형 환자 기록, 임상 시험 데이터, 청구 정보를 통합함으로써 의료 기관은 환자에 대한 360도 뷰를 구축할 수 있으며 이를 통해 치료 경로를 개인화하고 전반적인 건강 결과를 개선할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E금융\u003C/h3\u003E\r\n\u003Cp\u003E금융 기관은 빅 데이터 분석의 고속 처리 역량을 활용해 수십억 건의 트랜잭션을 실시간 분석하여 즉각적인 사기 감지, 예측 분석 및 고도화된 알고리즘 트레이딩 전략을 수행합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E리테일\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-for-retailers/\"\u003E리테일 업체\u003C/a\u003E는 온라인 클릭스트림 데이터, 로열티 프로그램 기록 및 재고 수준을 결합해 수요를 정확히 예측하고 매출 증대로 이어지는 초개인화 제품 추천을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E제조\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-manufacturing/\"\u003E제조\u003C/a\u003E 현장에서 생성되는 방대한 IT 및 운영 기술(OT) 센서 데이터를 통합하면 예지 정비를 지원하고 생산 프로세스를 최적화하여 운영 효율성을 높이는 데 도움이 됩니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E운송 및 물류\u003C/h3\u003E\r\n\u003Cp\u003E조직은 실시간 GPS 및 기상 데이터를 과거 추세와 함께 분석하여 복잡한 배송 경로를 최적화하고 플릿 운영을 선제적으로 관리하며 공급망 회복탄력성을 강화합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E교육\u003C/h3\u003E\r\n\u003Cp\u003E대학은 학생, 교수진 및 행정 기록을 중앙 집중화하여 360도 뷰를 구축합니다. 이를 통해 학업 중단 위험이 있는 학생을 식별하고 학습 개입을 개인화하여 재학생 유지율을 높일 수 있습니다. 분석은 건물 사용에서의 낭비를 줄이고 동문 관계 관리 및 기부 활동도 지원합니다. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E엔터테인먼트\u003C/h3\u003E\r\n\u003Cp\u003E미디어 기업은 시청 패턴과 소셜 감성 데이터를 대규모로 분석해 콘텐츠 제작 전략을 수립하고, 실시간 콘텐츠 추천을 최적화해 구독자 참여와 유지율을 극대화합니다.\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"]},"container":{"columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy_copy":{"id":"title-v2-5f09620d86","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석 도구"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-6adf014b6b","text":"\u003Cp\u003E최신 데이터의 방대한 규모와 복잡성 때문에 단일 서버 기반의 관계형 도구만으로는 더 이상 충분하지 않습니다. 빅 데이터 분석에는 전문화된 기술로 구성된 강력한 생태계가 필요하며 이 도구들은 보통 기능에 따라 스토리지, 처리 및 분석으로 분류됩니다. 다음은 널리 사용되는 빅 데이터 분석 도구의 예입니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EHadoop\u003C/h3\u003E\r\n\u003Cp\u003EApache Hadoop은 표준 서버 네트워크 전반에 워크로드를 분산해 방대한 데이터 세트를 관리하고 처리하도록 구축된 대표적인 오픈 소스 프레임워크로, 내결함성, 페타바이트 규모의 스토리지 및 배치 처리라는 핵심 기능을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ESpark\u003C/h3\u003E\r\n\u003Cp\u003EApache Spark는 속도에 최적화된 고급 멀티 언어 처리 엔진입니다. 클러스터 전반에 걸쳐 데이터를 메모리에 유지함으로써 분석 워크로드, 특히 머신러닝 작업을 가속화하며 디스크 기반 시스템보다 뛰어난 성능을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E스트림 분석 도구\u003C/h3\u003E\r\n\u003Cp\u003E이 특화된 소프트웨어는 데이터가 생성되는 즉시 수집하고 분석하는 ‘이동 중인 데이터’ 접근 방식으로 사용됩니다. 이러한 방식은 사물인터넷(IoT) 센서 모니터링이나 밀리초 단위의 금융 사기 감지처럼 즉각적인 인사이트가 필요한 사용 사례에서 중요합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E분산 스토리지\u003C/h3\u003E\r\n\u003Cp\u003E이 아키텍처는 대용량 파일을 청크로 분할해 여러 물리 또는 가상 머신에 안전하게 분산 저장하도록 설계되었습니다. 이 설계를 통해 사실상 무제한의 확장성과 높은 데이터 내구성을 보장하며 단일 장애 지점을 제거합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 마이닝 도구\u003C/h3\u003E\r\n\u003Cp\u003E이 소프트웨어는 처리 및 스토리지 계층 위에서 작동하며 고급 통계 및 머신러닝 알고리즘을 사용해 방대한 데이터 세트를 자동으로 탐색합니다. 숨겨진 상관관계를 찾아내고 이상값을 식별하며, 예측 모델링을 위한 패턴을 발견할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ENoSQL 데이터베이스\u003C/h3\u003E\r\n\u003Cp\u003ENoSQL은 반정형 및 비정형 데이터를 포함한 유연한 데이터 모델을 처리하도록 설계된 비관계형 데이터베이스 기술군입니다. 엄격한 고정 스키마 데이터베이스로는 제공할 수 없는 민첩성과 대규모 수평 확장성을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E데이터 웨어하우스\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003E데이터 웨어하우스\u003C/a\u003E는 정제된 대규모 정형 데이터에 대한 고성능 분석 쿼리에 최적화된 최신 클라우드 네이티브 플랫폼입니다. 이 플랫폼은 컴퓨팅과 스토리지를 분리하도록 설계되어 변동하는 수요에 따라 리소스를 독립적으로 확장 또는 축소할 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_copy","text_copy_copy"]},"container_copy_copy__1190438074":{"columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"container_copy_2083166428":{"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":"conslusion",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-bffb6fa334","additionalClasses":"headline-decoration","type":"heading2","lines":["결론"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-4007f2256b","text":"\u003Cp\u003E빅 데이터 분석은 더 이상 선택적인 IT 기능이 아닙니다. 이제는 최신 비즈니스 리더십을 좌우하는 전략 필수 요소입니다. 5V(규모, 속도, 다양성, 정확성, 가치)의 복잡성을 관리하면, 조직은 과거 중심 보고를 넘어 방대한 가공 전 데이터와 다양한 데이터 자산을 정밀한 미래 지향적 인텔리전스로 전환할 수 있습니다. 이 분야의 궁극적인 힘은 데이터 중심 세상에서 혁신을 이끌고 경쟁력을 유지하도록 지원하는 데 있습니다. 분석은 기업이 워크플로우를 최적화하고 시장 변화를 예측하며 초개인화 고객 경험을 제공하도록 지원해 수익과 운영 효율성을 함께 높입니다. 데이터 자산이 기하급수적으로 증가하는 환경에서, 데이터를 실행 가능한 인사이트로 전환하는 역량은 지속적인 비즈니스 우위를 달성하는 데 있어 가장 결정적인 요소입니다.\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_copy","text_copy"]},"container_copy_20831":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"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",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-07a53723c8","additionalClasses":"headline-decoration","type":"heading2","lines":["빅 데이터 분석 관련 자주 묻는 질문"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-cf362f0bc1","showDivider":false,"accordionItemsList":[{"title":"빅 데이터 분석과 비즈니스 분석은 어떤 관련이 있나요?","richText":"\u003Cp\u003E빅 데이터 분석은 최신 비즈니스 인텔리전스의 엔진 역할을 하며, 방대하고 복잡한 데이터 세트를 처리하는 데 필요한 처리 성능과 고급 모델링 기능을 제공합니다. 비즈니스 분석은 이렇게 도출된 인사이트, 모델 및 추세를 운영 전략과 실행 가능한 관리 의사 결정으로 직접 전환합니다.\u003C/p\u003E\n"},{"title":"빅 데이터 분석과 데이터 사이언스의 차이점은 무엇인가요?","richText":"\u003Cp\u003E이 두 분야는 많은 부분에서 겹치고 일부 동일한 도구를 사용하지만 적용 범위는 서로 다릅니다. 빅 데이터 분석은 방대한 데이터 세트를 수집, 정제 및 처리하는 엔진인 반면, 데이터 사이언스는 그 인프라를 활용해 고급 예측 지식과 근본적으로 새로운 알고리즘을 개발하는 학문 분야입니다.\u003C/p\u003E\n"},{"title":"클라우드 기반 빅 데이터 분석의 보안 수준은 어느 정도인가요?","richText":"\u003Cp\u003E빅 데이터 분석 플랫폼은 일반적으로 암호화와 액세스 제어를 포함한 보안 기능을 내장하지만, 전반적인 보안 수준은 적절한 구성 및 거버넌스에 달려 있습니다. 방대한 민감 데이터를 처리하는 과정에서 위험이 발생할 수 있지만 이러한 시스템은 멀티 팩터 인증(MFA)과 모든 데이터에 대한 지속적인 자동 암호화와 같은 필수 보안 조치를 사용해 이를 보호합니다. 궁극적으로 보안은 기업이 이러한 도구를 올바르게 사용하는지에 달려 있습니다. 예를 들어 역할 기반 접근 제어(RBAC)를 사용해 특정 데이터에 접근할 수 있는 사용자를 제어할 수 있습니다. \u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"},"container":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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