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센터","url":"https://staging.snowflake.com/content/snowflake-site/global/ko/developers/guides","currentPage":false},{"title":"developers","url":"https://staging.snowflake.com/content/snowflake-site/global/ko/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-1098ecd5da","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b5cd956f77",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d66dd3b575","description":"","title":"Tasty Bytes - 제로부터 Snowflake까지 - 반정형 데이터","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake에서 반정형 데이터 처리\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E반정형 데이터 처리를 집중적으로 다루는 Tasty Bytes 사용 - 제로부터 Snowflake까지 Quickstart에 오신 것을 환영합니다!\u003C/p\u003E\n","\u003Cp\u003E이 Quickstart에서는 VARIANT 데이터 유형, 점표기법과 래터럴 평면화를 결합하는 반정형 데이터 처리, 뷰 생성 및 Snowsight 그래프 생성으로 나눠 Snowflake에서 반정형 데이터를 처리하는 것에 대해 알아보겠습니다.\u003C/p\u003E\n","\u003Cp\u003ESnowflake의 반정형 데이터에 대한 상세 정보는 \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/semistructured-concepts\"\u003E반정형 데이터 개요 설명서\u003C/a\u003E에서 확인하십시오.\u003C/p\u003E\n","\u003Ch3\u003E사전 필요 조건 및 지식\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E시작하기 전에 평가판 계정 설정과 이 Quickstart를 완료하기 위해 필요한 Tasty Bytes 기초를 배포하는 것을 안내하는 \u003Ca href=\"/en/developers/guides/tasty-bytes-introduction-kr/\"\u003E\u003Cstrong\u003ETasty Bytes 소개 Quickstart\u003C/strong\u003E\u003C/a\u003E를 완료하십시오.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E알아볼 내용\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EVARIANT 데이터 유형의 정의\u003C/li\u003E\u003Cli\u003E반정형 데이터를 쿼리하기 위해 점표기법 사용하기\u003C/li\u003E\u003Cli\u003E반정형 데이터 평면화하기\u003C/li\u003E\u003Cli\u003E배열 분석하기\u003C/li\u003E\u003Cli\u003E뷰 생성하기\u003C/li\u003E\u003Cli\u003ESnowsight의 결과 세트에서 그래프 구축하기\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E구축할 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E엔드 투 엔드 반정형 데이터 처리\u003C/li\u003E\u003Cli\u003E조정(실버) 및 분석(골드) 뷰\u003C/li\u003E\u003Cli\u003E경영진을 위한 막대형 그래프\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003E워크시트 생성 및 SQL 복사\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E이 Quickstart에서는 Snowsight SQL 워크시트를 통해 테마가 Tasty Bytes인 스토리를 따라갈 것입니다. 이 페이지에는 추가 해설, 이미지 및 설명서 링크가 포함되어 있으며 나란히 사용할 수 있는 가이드입니다.\u003C/p\u003E\n","\u003Cp\u003E이 섹션은 Snowflake에 로그인하고, 새로운 워크시트를 생성하고, 워크시트의 이름을 바꾸고, SQL을 GitHub에서 복사하고, 이 Quickstart에서 활용할 SQL을 붙여넣는 방법을 안내합니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - URL을 통해 Snowflake에 액세스\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E브라우저 창을 열고 Snowflake 계정 URL을 입력합니다.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E2단계 - Snowflake에 로그인\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake 계정에 로그인합니다.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/log_into_snowflake.gif\" alt=\"assets/log_into_snowflake.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E3단계 - Worksheets로 이동\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E왼쪽 탐색 메뉴에 있는 Worksheets 탭을 클릭합니다.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/worksheet_tab.png\" alt=\"assets/worksheet_tab.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E4단계 - 워크시트 생성\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWorksheets 내에서 Snowsight 오른쪽 상단 모서리에 있는 ‘+’ 버튼을 클릭하고 ‘SQL Worksheet’를 선택합니다.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/+_sqlworksheet.png\" alt=\"assets/+_sqlworksheet.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E5단계 - 워크시트 이름 바꾸기\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E자동으로 생성된 Timestamp 이름을 클릭하고 ‘Tasty Bytes - Semi-Structured Data’를 입력하여 워크시트의 이름을 바꿉니다.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/rename_worksheet_tasty_bytes_setup.gif\" alt=\"assets/rename_worksheet_tasty_bytes_setup.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E6단계 - GitHub에서 Quickstart SQL에 액세스\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGitHub에서 호스팅된 Tasty Bytes SQL 파일과 연결된 아래 버튼을 클릭합니다. <button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql\"\u003Etb_zts_semi_structured_data.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E7단계 - GitHub에서 설정 SQL 복사\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EGitHub 내에서 오른쪽으로 이동하여 ‘Copy raw contents’를 클릭합니다. 이렇게 하면 모든 필수 SQL이 클립보드로 복사됩니다.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E8단계 - 설정 SQL을 GitHub에서 Snowflake 워크시트로 붙여넣기\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowsight 및 새롭게 생성한 워크시트로 되돌아가 방금 GitHub에서 복사한 것을 붙여넣습니다(\u003Cem\u003EMac 바로 가기 키 CMD + V, Windows 바로 가기 키 CTRL + V\u003C/em\u003E).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E9단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E반정형 메뉴 데이터 프로파일링\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003ETasty Bytes 데이터 엔지니어는 반정형 데이터 열이 포함된 메뉴 데이터를 프로파일링해야 합니다. 이 메뉴 테이블에서 최종 사용자에게 식사 및 재료 데이터를 노출하는 분석 계층 뷰를 생성해야 합니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 콘텍스트 설정 및 테이블 쿼리\u003C/h3\u003E\n","\u003Cp\u003E우선 다음 작업을 수행할 첫 3개의 쿼리를 함께 실행하겠습니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E역할 콘텍스트를 \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E로 설정\u003C/li\u003E\u003Cli\u003E웨어하우스 콘텍스트를 \u003Ccode\u003Etasty_de_wh\u003C/code\u003E로 설정\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eraw_pos.menu\u003C/code\u003E 테이블의 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/constructs/top_n\"\u003ETOP\u003C/a\u003E 10개 결과 세트 생성\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer;\nUSE WAREHOUSE tasty_de_wh;\n\nSELECT TOP 10\n    m.truck_brand_name,\n    m.menu_type,\n    m.menu_item_name,\n    m.menu_item_health_metrics_obj\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.1.menu.png\" alt=\"assets/3.1.menu.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E출력 내에서 \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E는 다운스트림을 제공해야 하는 지표가 포함되어 있다고 알려진 반정형 데이터여야 합니다.\u003C/p\u003E\n","\u003Cp\u003E이 열에서 셀 하나를 클릭하면 Snowsight는 통계 창을 자동으로 확장하여 내부 정보를 더 잘 보여줍니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.1.2.stats.png\" alt=\"assets/3.1.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 반정형 열 탐색\u003C/h3\u003E\n","\u003Cp\u003ESnowflake에 정의된 이 열을 심층적으로 알아보기 위해 \u003Ccode\u003Emenu\u003C/code\u003E 테이블에 있는 데이터 유형을 탐색하는 데 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/show-columns\"\u003ESHOW COLUMNS\u003C/a\u003E를 활용하는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.2.show_columns.png\" alt=\"assets/3.2.show_columns.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E결과 세트에서는 \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E가 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/data-types-semistructured\"\u003EVARIANT\u003C/a\u003E 데이터 유형임을 확인할 수 있습니다.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive 대부분이 일반적이고 사용 중인 반정형 형식의 기본 데이터 유형만 사용하는 데이터의 경우(예: JSON 형식의 경우 문자열 및 정수), VARIANT 열의 관계형 데이터 및 데이터 작업에 대한 스토리지 요구 사항 및 쿼리 성능은 매우 유사합니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E3단계 - 점표기법을 사용하여 반정형 데이터 순회\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E 열에 \u003Ccode\u003Emenu_item_id\u003C/code\u003E가 액세스해야 하는 더 중첩된 재료 및 식사 제한 데이터와 포함되어 있는 모습을 확인했습니다.\u003C/p\u003E\n","\u003Cp\u003E반정형 데이터를 순회하기 위해 \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/querying-semistructured#dot-notation\"\u003E점표기법\u003C/a\u003E을 활용하기 시작하는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.3.dot.png\" alt=\"assets/3.3.dot.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E점표기법을 사용하면 \u003Ccode\u003Emenu_item_id\u003C/code\u003E를 완벽하게 성공적으로 추출할 수 있었지만, \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E 열 출력에는 추가적인 반정형 객체가 아직 남아 있습니다.\u003C/p\u003E\n","\u003Cp\u003E또다시 자세히 살펴보기 위해 이 열에 있는 셀 하나를 클릭하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.3.2.stats.png\" alt=\"assets/3.3.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E잘 진행하고 있습니다!\u003C/strong\u003E 추가적인 Snowflake 함수를 사용하여 다음 섹션에서 \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E를 추가적으로 처리하는 방법을 알아보겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E4단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E반정형 데이터 평면화\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E점표기법을 사용하여 Variant 열에 있는 반정형 데이터를 얼마나 쉽게 쿼리할 수 있는지 확인했으니 Tasty Bytes 데이터 엔지니어는 내부 이해관계자가 요청한 데이터를 이들에게 제공할 수 있을 것입니다.\u003C/p\u003E\n","\u003Cp\u003E이 섹션에서는 요구 사항을 충족하기 위해 추가적인 반정형 데이터 처리를 수행하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 래터럴 평면화 소개\u003C/h3\u003E\n","\u003Cp\u003E다운스트림 사용자가 \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E 열에서 요구하는 데이터를 추가적으로 추출하려면 요청받은 첫 번째 \u003Ccode\u003Eingredient\u003C/code\u003E 배열을 제공하기 위해 방금 탐색한 점표기법 기능과 Snowflakes \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/flatten\"\u003EFLATTEN\u003C/a\u003E 함수 및 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/constructs/join-lateral\"\u003ELATERAL JOIN\u003C/a\u003E 기능을 활용하는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive \u003Cstrong\u003E평면화:\u003C/strong\u003E VARIANT, OBJECT 또는 ARRAY 열을 사용하는 테이블 함수이며 래터럴 뷰를 생성합니다. 평면화는 반정형 데이터를 관계형 표현으로 변환하는 데 사용할 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E래터럴 조인:\u003C/strong\u003E 래터럴이 아닌 조인의 출력과 달리, 래터럴 조인의 출력에는 인라인 뷰에서 생성된 행만 포함됩니다. 왼쪽의 행은 오른쪽과 결합될 필요가 없습니다. 왼쪽의 행은 인라인 뷰로 전달되어 이미 고려되었기 때문입니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.1.lat_flat.png\" alt=\"assets/4.1.lat_flat.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 배열 함수 탐색\u003C/h3\u003E\n","\u003Cp\u003E요청받은 식사 데이터를 추출하기 전에 Snowflake 배열 함수를 중점적으로 다루는 다음 쿼리를 실행하십시오. 이 함수는 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/array_contains\"\u003EARRAY_CONTAINS\u003C/a\u003E를 활용하여 상추가 포함된 모든 \u003Ccode\u003Emenu_item_name\u003C/code\u003E을 위해 \u003Ccode\u003Eingredients\u003C/code\u003E 열을 탐색합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:"ingredients"::VARIANT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.2.array_contains.png\" alt=\"assets/4.2.array_contains.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E출력에 따르면 상추가 포함된 메뉴 항목이 꽤 많습니다. 이러한 분석은 서비스를 제공하는 도시와 국가에서 음식을 회수해야 하는 상황에 공급망 조달 관리자에게 매우 유용할 것입니다.\u003C/p\u003E\n","\u003Ch3\u003E3단계 - 대규모로 반정형 데이터 구조화\u003C/h3\u003E\n","\u003Cp\u003E이미 조직에 제공할 수 있는 가치를 확인했으니 이제 이 섹션의 마지막 쿼리를 실행하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E이 쿼리는 점표기법, 결합된 래터럴 조인 및 평면화 테이블 함수를 사용하여 처음에 생성해 달라고 요청받았던 결과 세트를 제공합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.3.lat_flat_2.png\" alt=\"assets/4.3.lat_flat_2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E좋습니다!\u003C/strong\u003E 출력이 이해관계자가 요청한 요구 사항을 정확히 충족하는 것 같습니다. 다음 섹션에서는 이 출력에 액세스할 수 있는 분석 계층으로 이를 전달하는 방법을 알아보겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E4단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E반정형 데이터에 대한 정형 뷰 생성\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E마지막 섹션에서는 작업을 진행하는 동안 Snowflake 반정형 데이터 기능 세트를 사용하여 최종 사용자가 요구하는 출력을 정확히 제공하는 쿼리를 만들었습니다. 다음으로 조정을 통해 가공 전 계층으로 이 쿼리를 전달하는 프로세스를 진행하겠습니다. 결과적으로 이는 최종 사용자가 읽기 권한을 보유하고 있는 분석 계층으로 전달됩니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E참고:\u003C/strong\u003E 브론즈, 실버 및 골드 데이터 모델에 익숙한 사람이라면 가공 전을 브론즈, 조정을 실버, 분석을 골드로 생각하면 됩니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 반정형 평면화 SQL을 사용하여 조정 뷰 생성\u003C/h3\u003E\n","\u003Cp\u003E마지막 섹션을 끝낼 때 사용한 것과 동일한 쿼리를 사용하여 이제 이 SQL과 이미 구조화된 모든 \u003Ccode\u003Emenu\u003C/code\u003E 테이블 열을 포함한 다음 쿼리를 실행하십시오.\u003C/p\u003E\n","\u003Cp\u003E이 쿼리에서는 조정 스키마에서 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E를 사용하여 반정형 처리 로직과 추가 열을 테이블로 캡슐화합니다.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive 뷰를 사용하면 이것이 테이블인 것처럼 쿼리 결과에 액세스할 수 있습니다.  뷰는 데이터 결합, 분리 및 보호 등의 다양한 용도로 사용됩니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\n    AS\nSELECT \n    m.menu_id,\n    m.menu_type_id,\n    m.menu_type,\n    m.truck_brand_name,\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    m.item_category,\n    m.item_subcategory,\n    m.cost_of_goods_usd,\n    m.sale_price_usd,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/5.1.harmonized_view.png\" alt=\"assets/5.1.harmonized_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E 뷰가 생성되었으니 이제 여기에서 사용한 더 복잡한 SQL을 활용하지 않고도 이를 바로 쿼리할 수 있습니다. 하지만 이해관계자는 분석 계층에서 데이터에 액세스하기에 다음 단계에서 이를 분석 계층에 배치하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 조정에서 분석으로 쉽게 전달\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E를 참조하여 이제 값을 즉시 다운스트림으로 전달하기 시작하는 \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E를 생성할 다음 쿼리를 실행하십시오.\u003C/p\u003E\n","\u003Cp\u003E이 쿼리에서는 아직 다루지 않은 몇 개의 새로운 함수가 등장합니다. 우선 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E를 추가합니다. 이는 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/show\"\u003ESHOW VIEWS\u003C/a\u003E 명령이나 이 뷰를 쿼리할 때 사용자가 보게 될 것을 기록하기 위한 Snowsight 인터페이스에서 확인할 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E또한, 쿼리 또는 뷰 정의의 복잡성을 줄여 SQL 개발자의 삶을 훨씬 더 쉽게 만들 수 있는 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE 및 RENAME\u003C/a\u003E 매개변수를 활용합니다.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive \u003Cstrong\u003E제외:\u003C/strong\u003E 모든 열(SELECT * 또는 SELECT table_name.*)을 선택할 때 EXCLUDE는 결과에서 제외해야 하는 열을 지정합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E이름 바꾸기:\u003C/strong\u003E 모든 열(SELECT * 또는 SELECT table_name.*)을 선택할 때 결과에 사용해야 하는 열 별칭을 지정합니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/5.2.analytics_view.png\" alt=\"assets/5.2.analytics_view.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E3단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003ESnowsight에서 처리된 반정형 데이터 분석\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E분석 계층에서 사용할 수 있는 메뉴 뷰를 통해 추가 사본을 만들거나 그 어떠한 복잡한 처리도 수행하지 않고 Snowflake가 반정형 데이터에 대한 관계형 쿼리 경험을 지원하는 방법을 보여주는 최종 사용자에게 제공할 것에 대해 몇 개의 쿼리를 실행하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 배열 분석\u003C/h3\u003E\n","\u003Cp\u003E이전 섹션에서 쿼리 결과로 \u003Ccode\u003Eingredients\u003C/code\u003E 열을 확인했지만, 이는 이제 \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E 뷰에서 그 어떠한 점표기법 요구 사항도 없이 제공됩니다.\u003C/p\u003E\n","\u003Cp\u003E이를 통해 이제 2개의 추가 Snowflake 배열 함수인 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/array_intersection\"\u003EARRAY_INTERSECTION\u003C/a\u003E 및 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/arrays_overlap\"\u003EARRAYS_OVERLAP\u003C/a\u003E을 활용하는 다음 쿼리를 실행하여 각 푸드 트럭 브랜드 메뉴와 겹치는 음료가 아닌 메뉴 항목이 무엇인지 확인하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m1.menu_type,\n    m1.menu_item_name,\n    m2.menu_type AS overlap_menu_type,\n    m2.menu_item_name AS overlap_menu_item_name,\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\n    ON m1.menu_item_id <> m2.menu_item_id -- avoid joining the same menu item to itself\n    AND m1.menu_type <> m2.menu_type \nWHERE 1=1\n    AND m1.item_category <> 'Beverage' -- remove beverages\n    AND m2.item_category <> 'Beverage' -- remove beverages\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\nORDER BY m1.menu_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.1.arrays.png\" alt=\"assets/6.1.arrays.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E또다시 이러한 쿼리를 구축한 동일한 뷰를 사용하면 푸드 트럭 운영자가 매주 필요한 재료를 예측 및 주문하기 위한 조달 작업을 수행함에 따라 실제 비즈니스 가치를 제공할 수 있습니다.\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 경영진에 지표 제공\u003C/h3\u003E\n","\u003Cp\u003ETasty Bytes의 여러 브랜드에 걸쳐 현재 해결하고 있는 식사 제한에 관한 개괄적인 지표를 제공하여 Tasty Bytes 경영진이 데이터를 기반으로 메뉴에 대한 결정을 내리는 것을 돕는 방법을 알아보겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E에서 필수 지표를 집계하기 위해 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/count\"\u003ECOUNT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/sum\"\u003ESUM\u003C/a\u003E 및 조건 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/case\"\u003ECASE\u003C/a\u003E 문을 활용하는 다음 문을 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.2.metrics.png\" alt=\"assets/6.2.metrics.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E방금 수신한 출력을 통해 반정형 데이터가 포함된 가공 전 테이블에서 단일 집계 행으로 성공적으로 변환했습니다. 이 행은 더 많은 데이터를 기반으로 한 결정을 내릴 수 있도록 Tasty Bytes를 지원하기 위해 조직 내 모두가 쉽게 액세스할 수 있습니다.\u003C/p\u003E\n","\u003Ch3\u003E3단계 - 결과를 그래프로 변환\u003C/h3\u003E\n","\u003Cp\u003E일부 Tasty Bytes 경영진은 데이터의 시각적 표현을 선호하기에 이제 표 기반 결과를 Snowsight 내에서 이해하기 쉬운 시각적 그래프로 얼마나 쉽게 만들 수 있는지 알아보겠습니다.\u003C/p\u003E\n","\u003Cp\u003E앞서 실행한 SQL에 트럭 브랜드 이름 3개에 대한 필터를 추가하는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    m.brand_name,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\nGROUP BY m.brand_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.3.results.png\" alt=\"assets/6.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E기본값으로 Snowsight는 쿼리 결과를 표 기반 형식으로 반환합니다. 하지만 아직 다루지 않은 Snowsight의 강력한 기능인 \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/ui-snowsight-visualizations#using-charts\"\u003E그래프 사용\u003C/a\u003E이 있습니다.\u003C/p\u003E\n","\u003Cp\u003E이제 아래 스크린샷에 있는 화살표를 따라 각기 다른 푸드 트럭 브랜드가 특정 식사 제한을 해결하기 위해 제공하는 메뉴 항목을 비교하는 막대형 그래프를 생성하십시오.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.3.chart.png\" alt=\"assets/6.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E이 Quickstart를 완료하면서 Tasty Bytes 경영진이 스스로 이러한 분석을 수행하는 것이 얼마나 쉬운지 강조하고 싶습니다. 경영진은 구축한 뷰에 캡슐화한 반정형 데이터 처리에 대해 아무것도 알 필요가 없습니다. 이렇게 Tasty Bytes 조직에서 데이터 민주화를 촉진하는 것을 도왔습니다.\u003C/p\u003E\n","\u003Ch3\u003E4단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E종료 및 다음 단계\u003C/h2\u003E\n","\u003Ch3\u003E결론\u003C/h3\u003E\n","\u003Cp\u003E훌륭합니다! Tasty Bytes - 제로부터 Snowflake까지 - 반정형 데이터 Quickstart를 성공적으로 완료하셨습니다.\u003C/p\u003E\n","\u003Cp\u003E이를 완료하면서 진행한 내용은 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EVARIANT 데이터 유형의 정의 학습\u003C/li\u003E\u003Cli\u003E반정형 데이터를 쿼리하기 위해 점표기법 사용\u003C/li\u003E\u003Cli\u003E반정형 데이터 평면화\u003C/li\u003E\u003Cli\u003E배열 분석\u003C/li\u003E\u003Cli\u003E2개의 뷰 생성\u003C/li\u003E\u003Cli\u003ESnowsight의 결과 세트에서 그래프 구축\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E이 Quickstart를 다시 실행하려면 관련 워크시트 하단에 있는 초기화 스크립트를 활용하십시오.\u003C/p\u003E\n","\u003Ch3\u003E다음 단계\u003C/h3\u003E\n","\u003Cp\u003ESnowflake 데이터 클라우드에서의 여정을 계속하려면 이제 아래 링크를 방문하여 사용 가능한 모든 Tasty Bytes 활용 - Quickstart를 확인하십시오.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/en/developers/guides/tasty-bytes-introduction-kr/\"\u003ETasty Bytes 사용 - Quickstarts 목차\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Snowflake에서 반정형 데이터 처리\r\n\r\n\r\n### 개요\r\n\r\n반정형 데이터 처리를 집중적으로 다루는 Tasty Bytes 사용 - 제로부터 Snowflake까지 Quickstart에 오신 것을 환영합니다!\r\n\r\n이 Quickstart에서는 VARIANT 데이터 유형, 점표기법과 래터럴 평면화를 결합하는 반정형 데이터 처리, 뷰 생성 및 Snowsight 그래프 생성으로 나눠 Snowflake에서 반정형 데이터를 처리하는 것에 대해 알아보겠습니다.\r\n\r\nSnowflake의 반정형 데이터에 대한 상세 정보는 [반정형 데이터 개요 설명서](https://docs.snowflake.com/ko/user-guide/semistructured-concepts)에서 확인하십시오.\r\n\r\n### 사전 필요 조건 및 지식\r\n\r\n- 시작하기 전에 평가판 계정 설정과 이 Quickstart를 완료하기 위해 필요한 Tasty Bytes 기초를 배포하는 것을 안내하는 [**Tasty Bytes 소개 Quickstart**](/en/developers/guides/tasty-bytes-introduction-kr/)를 완료하십시오.\r\n\r\n### 알아볼 내용\r\n\r\n- VARIANT 데이터 유형의 정의\r\n- 반정형 데이터를 쿼리하기 위해 점표기법 사용하기\r\n- 반정형 데이터 평면화하기\r\n- 배열 분석하기\r\n- 뷰 생성하기\r\n- Snowsight의 결과 세트에서 그래프 구축하기\r\n\r\n### 구축할 것\r\n\r\n- 엔드 투 엔드 반정형 데이터 처리\r\n- 조정(실버) 및 분석(골드) 뷰\r\n- 경영진을 위한 막대형 그래프\r\n\r\n## 워크시트 생성 및 SQL 복사\r\n\r\n\r\n### 개요\r\n\r\n이 Quickstart에서는 Snowsight SQL 워크시트를 통해 테마가 Tasty Bytes인 스토리를 따라갈 것입니다. 이 페이지에는 추가 해설, 이미지 및 설명서 링크가 포함되어 있으며 나란히 사용할 수 있는 가이드입니다.\r\n\r\n이 섹션은 Snowflake에 로그인하고, 새로운 워크시트를 생성하고, 워크시트의 이름을 바꾸고, SQL을 GitHub에서 복사하고, 이 Quickstart에서 활용할 SQL을 붙여넣는 방법을 안내합니다.\r\n\r\n### 1단계 - URL을 통해 Snowflake에 액세스\r\n\r\n- 브라우저 창을 열고 Snowflake 계정 URL을 입력합니다.\r\n\r\n### 2단계 - Snowflake에 로그인\r\n\r\n- Snowflake 계정에 로그인합니다.\r\n  - ![assets/log_into_snowflake.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/log_into_snowflake.gif)\r\n\r\n\r\n### 3단계 - Worksheets로 이동\r\n\r\n- 왼쪽 탐색 메뉴에 있는 Worksheets 탭을 클릭합니다.\r\n  - ![assets/worksheet_tab.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/worksheet_tab.png)\r\n\r\n\r\n### 4단계 - 워크시트 생성\r\n\r\n- Worksheets 내에서 Snowsight 오른쪽 상단 모서리에 있는 ‘+’ 버튼을 클릭하고 ‘SQL Worksheet’를 선택합니다.\r\n  - ![assets/+_sqlworksheet.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/+_sqlworksheet.png)\r\n\r\n\r\n### 5단계 - 워크시트 이름 바꾸기\r\n\r\n- 자동으로 생성된 Timestamp 이름을 클릭하고 ‘Tasty Bytes - Semi-Structured Data’를 입력하여 워크시트의 이름을 바꿉니다.\r\n  - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/rename_worksheet_tasty_bytes_setup.gif)\r\n\r\n\r\n### 6단계 - GitHub에서 Quickstart SQL에 액세스\r\n\r\n- GitHub에서 호스팅된 Tasty Bytes SQL 파일과 연결된 아래 버튼을 클릭합니다. \u003Cbutton\u003E[tb_zts_semi_structured_data.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql)\u003C/button\u003E\r\n\r\n### 7단계 - GitHub에서 설정 SQL 복사\r\n\r\n- GitHub 내에서 오른쪽으로 이동하여 ‘Copy raw contents’를 클릭합니다. 이렇게 하면 모든 필수 SQL이 클립보드로 복사됩니다.\r\n  - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/github_copy_raw_contents.png)\r\n\r\n\r\n### 8단계 - 설정 SQL을 GitHub에서 Snowflake 워크시트로 붙여넣기\r\n\r\n- Snowsight 및 새롭게 생성한 워크시트로 되돌아가 방금 GitHub에서 복사한 것을 붙여넣습니다(*Mac 바로 가기 키 CMD + V, Windows 바로 가기 키 CTRL + V*).\r\n\r\n### 9단계 - 다음 클릭 --\u003E\r\n\r\n## 반정형 메뉴 데이터 프로파일링\r\n\r\n\r\n### 개요\r\n\r\nTasty Bytes 데이터 엔지니어는 반정형 데이터 열이 포함된 메뉴 데이터를 프로파일링해야 합니다. 이 메뉴 테이블에서 최종 사용자에게 식사 및 재료 데이터를 노출하는 분석 계층 뷰를 생성해야 합니다.\r\n\r\n### 1단계 - 콘텍스트 설정 및 테이블 쿼리\r\n\r\n우선 다음 작업을 수행할 첫 3개의 쿼리를 함께 실행하겠습니다.\r\n\r\n- 역할 콘텍스트를 `tasty_data_engineer`로 설정\r\n- 웨어하우스 콘텍스트를 `tasty_de_wh`로 설정\r\n- `raw_pos.menu` 테이블의 [TOP](https://docs.snowflake.com/ko/sql-reference/constructs/top_n) 10개 결과 세트 생성\r\n\r\n```\r\nUSE ROLE tasty_data_engineer;\r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT TOP 10\r\n    m.truck_brand_name,\r\n    m.menu_type,\r\n    m.menu_item_name,\r\n    m.menu_item_health_metrics_obj\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.1.menu.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.1.menu.png)\r\n\r\n출력 내에서 `menu_item_health_metrics_obj`는 다운스트림을 제공해야 하는 지표가 포함되어 있다고 알려진 반정형 데이터여야 합니다.\r\n\r\n이 열에서 셀 하나를 클릭하면 Snowsight는 통계 창을 자동으로 확장하여 내부 정보를 더 잘 보여줍니다.\r\n\r\n![assets/3.1.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.1.2.stats.png)\r\n\r\n### 2단계 - 반정형 열 탐색\r\n\r\nSnowflake에 정의된 이 열을 심층적으로 알아보기 위해 `menu` 테이블에 있는 데이터 유형을 탐색하는 데 [SHOW COLUMNS](https://docs.snowflake.com/ko/sql-reference/sql/show-columns)를 활용하는 다음 쿼리를 실행하십시오.\r\n\r\n```\r\nSHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\r\n```\r\n\r\n![assets/3.2.show_columns.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.2.show_columns.png)\r\n\r\n결과 세트에서는 `menu_item_health_metrics_obj`가 [VARIANT](https://docs.snowflake.com/ko/sql-reference/data-types-semistructured) 데이터 유형임을 확인할 수 있습니다.\r\n\r\n\u003E aside positive 대부분이 일반적이고 사용 중인 반정형 형식의 기본 데이터 유형만 사용하는 데이터의 경우(예: JSON 형식의 경우 문자열 및 정수), VARIANT 열의 관계형 데이터 및 데이터 작업에 대한 스토리지 요구 사항 및 쿼리 성능은 매우 유사합니다.\r\n\r\n### 3단계 - 점표기법을 사용하여 반정형 데이터 순회\r\n\r\n`menu_item_health_metrics_obj` 열에 `menu_item_id`가 액세스해야 하는 더 중첩된 재료 및 식사 제한 데이터와 포함되어 있는 모습을 확인했습니다.\r\n\r\n반정형 데이터를 순회하기 위해 [점표기법](https://docs.snowflake.com/ko/user-guide/querying-semistructured#dot-notation)을 활용하기 시작하는 다음 쿼리를 실행하십시오.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\r\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.3.dot.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.3.dot.png)\r\n\r\n점표기법을 사용하면 `menu_item_id`를 완벽하게 성공적으로 추출할 수 있었지만, `menu_item_health_metrics` 열 출력에는 추가적인 반정형 객체가 아직 남아 있습니다.\r\n\r\n또다시 자세히 살펴보기 위해 이 열에 있는 셀 하나를 클릭하겠습니다.\r\n\r\n![assets/3.3.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/3.3.2.stats.png)\r\n\r\n**잘 진행하고 있습니다!** 추가적인 Snowflake 함수를 사용하여 다음 섹션에서 `menu_item_health_metrics`를 추가적으로 처리하는 방법을 알아보겠습니다.\r\n\r\n### 4단계 - 다음 클릭 --\u003E\r\n\r\n## 반정형 데이터 평면화\r\n\r\n\r\n### 개요\r\n\r\n점표기법을 사용하여 Variant 열에 있는 반정형 데이터를 얼마나 쉽게 쿼리할 수 있는지 확인했으니 Tasty Bytes 데이터 엔지니어는 내부 이해관계자가 요청한 데이터를 이들에게 제공할 수 있을 것입니다.\r\n\r\n이 섹션에서는 요구 사항을 충족하기 위해 추가적인 반정형 데이터 처리를 수행하겠습니다.\r\n\r\n### 1단계 - 래터럴 평면화 소개\r\n\r\n다운스트림 사용자가 `menu_item_health_metrics_obj` 열에서 요구하는 데이터를 추가적으로 추출하려면 요청받은 첫 번째 `ingredient` 배열을 제공하기 위해 방금 탐색한 점표기법 기능과 Snowflakes [FLATTEN](https://docs.snowflake.com/ko/sql-reference/functions/flatten) 함수 및 [LATERAL JOIN](https://docs.snowflake.com/ko/sql-reference/constructs/join-lateral) 기능을 활용하는 다음 쿼리를 실행하십시오.\r\n\r\n\u003E aside positive **평면화:** VARIANT, OBJECT 또는 ARRAY 열을 사용하는 테이블 함수이며 래터럴 뷰를 생성합니다. 평면화는 반정형 데이터를 관계형 표현으로 변환하는 데 사용할 수 있습니다.\r\n\u003E \r\n\u003E **래터럴 조인:** 래터럴이 아닌 조인의 출력과 달리, 래터럴 조인의 출력에는 인라인 뷰에서 생성된 행만 포함됩니다. 왼쪽의 행은 오른쪽과 결합될 필요가 없습니다. 왼쪽의 행은 인라인 뷰로 전달되어 이미 고려되었기 때문입니다.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.1.lat_flat.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.1.lat_flat.png)\r\n\r\n### 2단계 - 배열 함수 탐색\r\n\r\n요청받은 식사 데이터를 추출하기 전에 Snowflake 배열 함수를 중점적으로 다루는 다음 쿼리를 실행하십시오. 이 함수는 [ARRAY_CONTAINS](https://docs.snowflake.com/ko/sql-reference/functions/array_contains)를 활용하여 상추가 포함된 모든 `menu_item_name`을 위해 `ingredients` 열을 탐색합니다.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\r\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:\"ingredients\"::VARIANT);\r\n```\r\n\r\n![assets/4.2.array_contains.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.2.array_contains.png)\r\n\r\n출력에 따르면 상추가 포함된 메뉴 항목이 꽤 많습니다. 이러한 분석은 서비스를 제공하는 도시와 국가에서 음식을 회수해야 하는 상황에 공급망 조달 관리자에게 매우 유용할 것입니다.\r\n\r\n### 3단계 - 대규모로 반정형 데이터 구조화\r\n\r\n이미 조직에 제공할 수 있는 가치를 확인했으니 이제 이 섹션의 마지막 쿼리를 실행하겠습니다.\r\n\r\n이 쿼리는 점표기법, 결합된 래터럴 조인 및 평면화 테이블 함수를 사용하여 처음에 생성해 달라고 요청받았던 결과 세트를 제공합니다.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.3.lat_flat_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/4.3.lat_flat_2.png)\r\n\r\n**좋습니다!** 출력이 이해관계자가 요청한 요구 사항을 정확히 충족하는 것 같습니다. 다음 섹션에서는 이 출력에 액세스할 수 있는 분석 계층으로 이를 전달하는 방법을 알아보겠습니다.\r\n\r\n### 4단계 - 다음 클릭 --\u003E\r\n\r\n## 반정형 데이터에 대한 정형 뷰 생성\r\n\r\n\r\n### 개요\r\n\r\n마지막 섹션에서는 작업을 진행하는 동안 Snowflake 반정형 데이터 기능 세트를 사용하여 최종 사용자가 요구하는 출력을 정확히 제공하는 쿼리를 만들었습니다. 다음으로 조정을 통해 가공 전 계층으로 이 쿼리를 전달하는 프로세스를 진행하겠습니다. 결과적으로 이는 최종 사용자가 읽기 권한을 보유하고 있는 분석 계층으로 전달됩니다.\r\n\r\n**참고:** 브론즈, 실버 및 골드 데이터 모델에 익숙한 사람이라면 가공 전을 브론즈, 조정을 실버, 분석을 골드로 생각하면 됩니다.\r\n\r\n### 1단계 - 반정형 평면화 SQL을 사용하여 조정 뷰 생성\r\n\r\n마지막 섹션을 끝낼 때 사용한 것과 동일한 쿼리를 사용하여 이제 이 SQL과 이미 구조화된 모든 `menu` 테이블 열을 포함한 다음 쿼리를 실행하십시오.\r\n\r\n이 쿼리에서는 조정 스키마에서 [CREATE VIEW](https://docs.snowflake.com/ko/sql-reference/sql/create-view)를 사용하여 반정형 처리 로직과 추가 열을 테이블로 캡슐화합니다.\r\n\r\n\u003E aside positive 뷰를 사용하면 이것이 테이블인 것처럼 쿼리 결과에 액세스할 수 있습니다.  뷰는 데이터 결합, 분리 및 보호 등의 다양한 용도로 사용됩니다.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\r\n    AS\r\nSELECT \r\n    m.menu_id,\r\n    m.menu_type_id,\r\n    m.menu_type,\r\n    m.truck_brand_name,\r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    m.item_category,\r\n    m.item_subcategory,\r\n    m.cost_of_goods_usd,\r\n    m.sale_price_usd,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/5.1.harmonized_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/5.1.harmonized_view.png)\r\n\r\n`harmonized.menu_v` 뷰가 생성되었으니 이제 여기에서 사용한 더 복잡한 SQL을 활용하지 않고도 이를 바로 쿼리할 수 있습니다. 하지만 이해관계자는 분석 계층에서 데이터에 액세스하기에 다음 단계에서 이를 분석 계층에 배치하겠습니다.\r\n\r\n### 2단계 - 조정에서 분석으로 쉽게 전달\r\n\r\n`harmonized.menu_v`를 참조하여 이제 값을 즉시 다운스트림으로 전달하기 시작하는 `analytics.menu_v`를 생성할 다음 쿼리를 실행하십시오.\r\n\r\n이 쿼리에서는 아직 다루지 않은 몇 개의 새로운 함수가 등장합니다. 우선 [COMMENT](https://docs.snowflake.com/ko/sql-reference/sql/comment)를 추가합니다. 이는 [SHOW VIEWS](https://docs.snowflake.com/ko/sql-reference/sql/show) 명령이나 이 뷰를 쿼리할 때 사용자가 보게 될 것을 기록하기 위한 Snowsight 인터페이스에서 확인할 수 있습니다.\r\n\r\n또한, 쿼리 또는 뷰 정의의 복잡성을 줄여 SQL 개발자의 삶을 훨씬 더 쉽게 만들 수 있는 [SELECT * EXCLUDE 및 RENAME](https://docs.snowflake.com/ko/sql-reference/sql/select#parameters) 매개변수를 활용합니다.\r\n\r\n\u003E aside positive **제외:** 모든 열(SELECT * 또는 SELECT table_name.*)을 선택할 때 EXCLUDE는 결과에서 제외해야 하는 열을 지정합니다.\r\n\u003E \r\n\u003E **이름 바꾸기:** 모든 열(SELECT * 또는 SELECT table_name.*)을 선택할 때 결과에 사용해야 하는 열 별칭을 지정합니다.\r\n\r\n![assets/5.2.analytics_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/5.2.analytics_view.png)\r\n\r\n### 3단계 - 다음 클릭 --\u003E\r\n\r\n## Snowsight에서 처리된 반정형 데이터 분석\r\n\r\n\r\n### 개요\r\n\r\n분석 계층에서 사용할 수 있는 메뉴 뷰를 통해 추가 사본을 만들거나 그 어떠한 복잡한 처리도 수행하지 않고 Snowflake가 반정형 데이터에 대한 관계형 쿼리 경험을 지원하는 방법을 보여주는 최종 사용자에게 제공할 것에 대해 몇 개의 쿼리를 실행하겠습니다.\r\n\r\n### 1단계 - 배열 분석\r\n\r\n이전 섹션에서 쿼리 결과로 `ingredients` 열을 확인했지만, 이는 이제 `analytics.menu_v` 뷰에서 그 어떠한 점표기법 요구 사항도 없이 제공됩니다.\r\n\r\n이를 통해 이제 2개의 추가 Snowflake 배열 함수인 [ARRAY_INTERSECTION](https://docs.snowflake.com/ko/sql-reference/functions/array_intersection) 및 [ARRAYS_OVERLAP](https://docs.snowflake.com/ko/sql-reference/functions/arrays_overlap)을 활용하는 다음 쿼리를 실행하여 각 푸드 트럭 브랜드 메뉴와 겹치는 음료가 아닌 메뉴 항목이 무엇인지 확인하십시오.\r\n\r\n```\r\nSELECT \r\n    m1.menu_type,\r\n    m1.menu_item_name,\r\n    m2.menu_type AS overlap_menu_type,\r\n    m2.menu_item_name AS overlap_menu_item_name,\r\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\r\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\r\n    ON m1.menu_item_id \u003C\u003E m2.menu_item_id -- avoid joining the same menu item to itself\r\n    AND m1.menu_type \u003C\u003E m2.menu_type \r\nWHERE 1=1\r\n    AND m1.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND m2.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\r\nORDER BY m1.menu_type;\r\n```\r\n\r\n![assets/6.1.arrays.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.1.arrays.png)\r\n\r\n또다시 이러한 쿼리를 구축한 동일한 뷰를 사용하면 푸드 트럭 운영자가 매주 필요한 재료를 예측 및 주문하기 위한 조달 작업을 수행함에 따라 실제 비즈니스 가치를 제공할 수 있습니다.\r\n\r\n### 2단계 - 경영진에 지표 제공\r\n\r\nTasty Bytes의 여러 브랜드에 걸쳐 현재 해결하고 있는 식사 제한에 관한 개괄적인 지표를 제공하여 Tasty Bytes 경영진이 데이터를 기반으로 메뉴에 대한 결정을 내리는 것을 돕는 방법을 알아보겠습니다.\r\n\r\n`analytics.menu_v`에서 필수 지표를 집계하기 위해 [COUNT](https://docs.snowflake.com/ko/sql-reference/functions/count), [SUM](https://docs.snowflake.com/ko/sql-reference/functions/sum) 및 조건 [CASE](https://docs.snowflake.com/ko/sql-reference/functions/case) 문을 활용하는 다음 문을 실행하십시오.\r\n\r\n```\r\nSELECT\r\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\r\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\r\n```\r\n\r\n![assets/6.2.metrics.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.2.metrics.png)\r\n\r\n방금 수신한 출력을 통해 반정형 데이터가 포함된 가공 전 테이블에서 단일 집계 행으로 성공적으로 변환했습니다. 이 행은 더 많은 데이터를 기반으로 한 결정을 내릴 수 있도록 Tasty Bytes를 지원하기 위해 조직 내 모두가 쉽게 액세스할 수 있습니다.\r\n\r\n### 3단계 - 결과를 그래프로 변환\r\n\r\n일부 Tasty Bytes 경영진은 데이터의 시각적 표현을 선호하기에 이제 표 기반 결과를 Snowsight 내에서 이해하기 쉬운 시각적 그래프로 얼마나 쉽게 만들 수 있는지 알아보겠습니다.\r\n\r\n앞서 실행한 SQL에 트럭 브랜드 이름 3개에 대한 필터를 추가하는 다음 쿼리를 실행하십시오.\r\n\r\n```\r\nSELECT\r\n    m.brand_name,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m\r\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\r\nGROUP BY m.brand_name;\r\n```\r\n\r\n![assets/6.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.3.results.png)\r\n\r\n기본값으로 Snowsight는 쿼리 결과를 표 기반 형식으로 반환합니다. 하지만 아직 다루지 않은 Snowsight의 강력한 기능인 [그래프 사용](https://docs.snowflake.com/ko/user-guide/ui-snowsight-visualizations#using-charts)이 있습니다.\r\n\r\n이제 아래 스크린샷에 있는 화살표를 따라 각기 다른 푸드 트럭 브랜드가 특정 식사 제한을 해결하기 위해 제공하는 메뉴 항목을 비교하는 막대형 그래프를 생성하십시오.\r\n\r\n![assets/6.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-kr/6.3.chart.png)\r\n\r\n이 Quickstart를 완료하면서 Tasty Bytes 경영진이 스스로 이러한 분석을 수행하는 것이 얼마나 쉬운지 강조하고 싶습니다. 경영진은 구축한 뷰에 캡슐화한 반정형 데이터 처리에 대해 아무것도 알 필요가 없습니다. 이렇게 Tasty Bytes 조직에서 데이터 민주화를 촉진하는 것을 도왔습니다.\r\n\r\n### 4단계 - 다음 클릭 --\u003E\r\n\r\n## 종료 및 다음 단계\r\n\r\n\r\n### 결론\r\n\r\n훌륭합니다! Tasty Bytes - 제로부터 Snowflake까지 - 반정형 데이터 Quickstart를 성공적으로 완료하셨습니다.\r\n\r\n이를 완료하면서 진행한 내용은 다음과 같습니다.\r\n\r\n- VARIANT 데이터 유형의 정의 학습\r\n- 반정형 데이터를 쿼리하기 위해 점표기법 사용\r\n- 반정형 데이터 평면화\r\n- 배열 분석\r\n- 2개의 뷰 생성\r\n- Snowsight의 결과 세트에서 그래프 구축\r\n\r\n이 Quickstart를 다시 실행하려면 관련 워크시트 하단에 있는 초기화 스크립트를 활용하십시오.\r\n\r\n### 다음 단계\r\n\r\nSnowflake 데이터 클라우드에서의 여정을 계속하려면 이제 아래 링크를 방문하여 사용 가능한 모든 Tasty Bytes 활용 - Quickstart를 확인하십시오.\r\n\r\n- ### [Tasty Bytes 사용 - Quickstarts 목차](/en/developers/guides/tasty-bytes-introduction-kr/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-9c33c20180","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-520ca2685e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ed8269c41c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-03-11",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-733a523026","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-e5ee0b4a1c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-de63ff683b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-8491bc8017","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-a58658ee9c","headings":["\u003Ch2\u003ESnowflake에서 반정형 데이터 처리\u003C/h2\u003E","\u003Ch2\u003E워크시트 생성 및 SQL 복사\u003C/h2\u003E","\u003Ch2\u003E반정형 메뉴 데이터 프로파일링\u003C/h2\u003E","\u003Ch2\u003E반정형 데이터 평면화\u003C/h2\u003E","\u003Ch2\u003E반정형 데이터에 대한 정형 뷰 생성\u003C/h2\u003E","\u003Ch2\u003ESnowsight에서 처리된 반정형 데이터 분석\u003C/h2\u003E","\u003Ch2\u003E종료 및 다음 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aem-GridColumn--default--12"},"id":"container-47249ed89b","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-ad024489f4","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-f7f5de7974","additionalClasses":"sf-footer__newsletter-title","text":"\u003Cp\u003E\u003Cb\u003ESubscribe to our monthly newsletter\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EStay up to date on Snowflake’s latest products, expert insights and resources—right in your 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