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대한 직접 액세스\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E협업을 집중적으로 다루는 Tasty Bytes - 제로부터 Snowflake까지 Quickstart에 오신 것을 환영합니다! 이 Quickstart에서는 Snowflake 마켓플레이스를 통해 자사 데이터를 날씨 데이터로 보강하는 방법을 집중적으로 다루겠습니다. Snowflake 마켓플레이스는 그 어떠한 ETL, 데이터 파이프라인 또는 통합도 설정하지 않고 서드 파티 데이터와 앱을 찾고, 체험하고, 구매하는 장소입니다.\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\u003ESnowflake 마켓플레이스에 액세스하기\u003C/li\u003E\u003Cli\u003E계정에서 라이브 Weather Source 데이터 획득하기\u003C/li\u003E\u003Cli\u003E뷰 생성하기\u003C/li\u003E\u003Cli\u003ESQL 함수 생성하기\u003C/li\u003E\u003Cli\u003E시각적 인사이트를 탐색하기 위해 Snowsight 그래프 활용하기\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E구축할 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E자사 매출 및 서드 파티 날씨 데이터 조정\u003C/li\u003E\u003Cli\u003E원활한 화씨 섭씨 변환 가능성\u003C/li\u003E\u003Cli\u003E원활한 인치 밀리미터 변환 가능성\u003C/li\u003E\u003Cli\u003ESnowflake 마켓플레이스를 통해 추가 인사이트를 얻는 방법 이해\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-collaboration-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-collaboration-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-collaboration-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 - Collaboration’을 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-collaboration-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.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.sql\"\u003Etb_zts_collaboration.sql\u003C/a\u003E</button>\u003C/p\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-collaboration-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\u003E자사 데이터에서 매출이 0인 날짜 조사\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003ETasty Bytes 금융 분석가는 전년 대비 분석을 실행할 때 여러 도시에서 트럭 매출이 0인 설명할 수 없는 날짜가 존재한다고 알려왔습니다. 금융 분석가가 제시한 하나의 사례는 2022년 2월 독일 함부르크입니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 추세를 위해 판매 시점 데이터 쿼리\u003C/h3\u003E\n","\u003Cp\u003E초기에 역할과 웨어하우스 콘텍스트를 \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E 및 \u003Ccode\u003Etasty_de_wh\u003C/code\u003E로 설정하기 위해 이 단계에서 3개의 쿼리를 실행하며 시작하겠습니다. 콘텍스트가 설정되면 분석 \u003Ccode\u003Eorders_v\u003C/code\u003E 뷰를 쿼리하여 2022년 독일 함부르크의 매출 결과 세트를 제공하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer;\nUSE WAREHOUSE tasty_de_wh;\n\nSELECT \n    o.date,\n    SUM(o.price) AS daily_sales\nFROM frostbyte_tasty_bytes.analytics.orders_v o\nWHERE 1=1\n    AND o.country = 'Germany'\n    AND o.primary_city = 'Hamburg'\n    AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28'\nGROUP BY o.date\nORDER BY o.date ASC;\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-collaboration-kr/3.1.orders_v.png\" alt=\"assets/3.1.orders_v.png\"\u003E\n위에서 확인한 내용에 따르면 2월의 몇몇 날짜에 일일 매출 기록이 없다는 점에서 분석가의 의견에 동의할 수 있습니다. 따라서 분석가는 확실히 문제가 있는 부분을 지적했습니다. 다음 섹션에서는 이러한 현상이 발생한 이유를 조금 더 자세히 알아보겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003ESnowflake 마켓플레이스의 Weather Source 데이터 활용\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E이전 섹션을 보면 2월 16일부터 2월 21일까지의 독일 함부르크에서 발생한 매출이 누락된 것 같습니다.  자사 데이터 내에서는 이 부분을 조사하기 위해 사용할 수 있는 것이 거의 없습니다. 하지만 더 큰 요인이 여기에 영향을 줬을 것입니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"/ko/data-cloud/marketplace/\"\u003ESnowflake 마켓플레이스\u003C/a\u003E를 활용하여 바로 탐색할 수 있는 하나의 아이디어는 기상 이변과 Weather Source에서 제공한 무료 공개 목록입니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - Weather Source LLC: frostbyte Snowflake 마켓플레이스 목록 획득\u003C/h3\u003E\n","\u003Cp\u003ESnowflake 마켓플레이스는 혁신적인 비즈니스 솔루션을 지원하기 위해 필요한 데이터와 애플리케이션을 찾고, 체험하고, 구매하기 가장 좋은 장소입니다. 이 단계에서는 \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte\"\u003EWeather Source LLC: frostbyte\u003C/a\u003E 목록에 액세스하여 함부르크 매출 하락에 대한 추가 분석을 수행하는 것을 돕겠습니다.\u003C/p\u003E\n","\u003Cp\u003E아래 단계와 동영상에 따라 Snowflake 계정에서 이 목록을 획득합니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E클릭 -> 홈\u003C/li\u003E\u003Cli\u003E클릭 -> Marketplace\u003C/li\u003E\u003Cli\u003E검색 -> frostbyte\u003C/li\u003E\u003Cli\u003E클릭 -> Weather Source LLC: frostbyte\u003C/li\u003E\u003Cli\u003E클릭 -> Get\u003C/li\u003E\u003Cli\u003E데이터베이스 이름 바꾸기 -> FROSTBYTE_WEATHERSOURCE(전부 대문자)\u003C/li\u003E\u003Cli\u003E추가 역할에 부여 -> PUBLIC\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.1.acquire_weather_source.gif\" alt=\"assets/4.1.acquire_weather_source.gif\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive Weather Source는 선도적인 전 세계 날씨 및 기후 데이터 공급자입니다. 이 회사의 OnPoint Product Suite는 비즈니스가 여러 산업에 걸쳐 다양한 사용 사례에 대한 의미 있고 실행 가능한 인사이트를 빠르게 생성하는 데 필요한 날씨 및 기후 데이터를 제공합니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E2단계 - 자사 및 서드 파티 데이터 조정\u003C/h3\u003E\n","\u003Cp\u003E공유 \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E 데이터베이스를 사용하는 상태로 이 단계의 쿼리를 실행하여 \u003Ccode\u003Eharmonized.daily_weather_v\u003C/code\u003E 뷰를 생성하십시오. 그러면 2개의 Weather Source 테이블과 Tasty Bytes 푸드 트럭이 운영되는 국가와 도시에 대한 국가 테이블을 결합할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\n    AS\nSELECT \n    hd.*,\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\n    pc.city_name AS city,\n    c.country AS country_desc\nFROM frostbyte_weathersource.onpoint_id.history_day hd\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\n    ON pc.postal_code = hd.postal_code\n    AND pc.country = hd.country\nJOIN frostbyte_tasty_bytes.raw_pos.country c\n    ON c.iso_country = hd.country\n    AND c.city = hd.city_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-collaboration-kr/4.2.daily_weather_v.png\" alt=\"assets/4.2.daily_weather_v.png\"\u003E\n위 뷰 정의에서 확인할 수 있듯이 \u003Ccode\u003Eonpoint_id\u003C/code\u003E 스키마 내 2개의 \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E 테이블을 결합한 다음 \u003Ccode\u003Efrostbyte_tasty_bytes\u003C/code\u003E 데이터베이스 및 \u003Ccode\u003Eraw_pos\u003C/code\u003E 스키마의 \u003Ccode\u003Ecountry\u003C/code\u003E 테이블과 이를 조정하고 있습니다.\u003C/p\u003E\n","\u003Cp\u003E이러한 작업은 일반적으로 조정 계층(실버 영역이라고도 함)에서 찾아볼 수 있습니다.\u003C/p\u003E\n","\u003Ch3\u003E3단계 - 일일 기온 시각화\u003C/h3\u003E\n","\u003Cp\u003E조정 스키마에서 \u003Ccode\u003Edaily_weather_v\u003C/code\u003E 뷰를 사용하는 상태로 다음 쿼리를 실행하여 2022년 2월 함부르크의 일평균 기온을 살펴보겠습니다.\u003C/p\u003E\n","\u003Cp\u003E이 과정에서 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/avg\"\u003EAVG\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/year\"\u003EYEAR\u003C/a\u003E 및 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/year\"\u003EMONTH\u003C/a\u003E 함수를 활용하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc = 'Germany'\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\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-collaboration-kr/4.3.results.png\" alt=\"assets/4.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E추세를 추가적으로 조사하기 위해 Snowsight 그래프 생성을 활용하여 시간의 흐름에 따른 평균 기온의 선 그래프를 생성하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.3.chart.png\" alt=\"assets/4.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E위에서 확인한 내용에 따르면 트럭에서 매출이 없는 날짜가 발생한 명백한 이유를 아직 찾지 못했습니다. 다음 단계에서 이유를 설명할 수도 있는 다른 요인을 찾아보겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E4단계 - 바람 데이터 가져오기\u003C/h3\u003E\n","\u003Cp\u003E이전 단계에서 확인했듯이 일평균 기온은 함부르크에서 매출이 0인 날짜가 발생한 이유가 아닌 것 같습니다. 다행히 Weather Source는 심층적으로 알아볼 수 있는 다른 날씨 지표를 제공합니다.\u003C/p\u003E\n","\u003Cp\u003E이제 바람 지표를 가져오기 위해 조정 뷰를 활용할 다음 쿼리를 실행하십시오. 이 쿼리에서는 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/min\"\u003EMAX\u003C/a\u003E 함수가 사용됩니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\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-collaboration-kr/4.4.result.png\" alt=\"assets/4.4.result.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E여기서도 빠른 Snowsight 그래프를 통해 이러한 데이터의 추세를 더 잘 나타낼 수 있습니다. 결과에서 그래프로 이동하기 위해 아래 스크린샷에 있는 화살표를 따르십시오.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.4.chart.png\" alt=\"assets/4.4.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E찾았습니다!\u003C/strong\u003E 매출이 0인 날짜에는 바람이 태풍 수준이었습니다. 이것이 바로 트럭이 이러한 날짜에 아무것도 판매할 수 없었던 더 타당한 이유인 것 같습니다. 하지만 조정에서 이 분석을 실행했기에 이제 분석가가 이러한 인사이트에 스스로 액세스할 수 있도록 분석에 이 내용을 제공하기 위한 여정을 시작하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E5단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E데이터 인사이트 민주화\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E이제 태풍 수준의 바람이 아마도 금융 분석가가 언급했던 매출이 0인 날짜에 영향을 준 것 같다고 판단했습니다.\u003C/p\u003E\n","\u003Cp\u003E모든 Tasty Bytes 직원이 액세스할 수 있는 분석 뷰를 배포하여 이제 이러한 연구를 조직 내 모두에게 제공하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계- SQL 함수 생성\u003C/h3\u003E\n","\u003Cp\u003E세계적인 회사로서 우선 화씨 섭씨 변환 및 인치 밀리미터 변환을 위한 2개의 SQL 함수를 생성하며 프로세스를 시작하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E이 단계에서는 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/create-function\"\u003ECREATE FUNCTION\u003C/a\u003E 명령을 활용하는 \u003Ccode\u003Efahrenheit_to_celsius\u003C/code\u003E 및 \u003Ccode\u003Einch_to_millimeter\u003C/code\u003E 함수를 생성하기 위해 2개의 쿼리를 하나씩 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\nRETURNS NUMBER(35,4)\nAS\n$$\n    (temp_f - 32) * (5/9)\n$$;\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-collaboration-kr/5.1.f_to_c.png\" alt=\"assets/5.1.f_to_c.png\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\nRETURNS NUMBER(35,4)\n    AS\n$$\n    inch * 25.4\n$$;\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-collaboration-kr/5.1.inch_to_mm.png\" alt=\"assets/5.1.inch_to_mm.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive UDF를 생성할 때 지원되는 언어 중 하나로 코드가 작성된 핸들러를 지정합니다. 핸들러의 언어에 따라 CREATE FUNCTION 문이 포함된 핸들러 소스 코드 인라인을 포함하거나 핸들러가 미리 컴파일되었거나 스테이지의 소스 코드인 경우에 CREATE FUNCTION에서 핸들러 위치를 참조할 수 있습니다.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E2단계 - 뷰를 위한 SQL 생성\u003C/h3\u003E\n","\u003Cp\u003E분석 뷰를 배포하기 전에 일일 매출과 날씨를 결합하기 위해 뷰에서 사용할 SQL을 생성하고 SQL 변환 함수를 활용하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E독일 함부르크를 필터링하고 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/zeroifnull\"\u003EZEROIFNULL\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/round\"\u003EROUND\u003C/a\u003E 및 \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/functions/to_date\"\u003EDATE\u003C/a\u003E와 같이 아직 확인하지 않은 몇몇 함수를 활용하는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nWHERE 1=1\n    AND fd.country_desc = 'Germany'\n    AND fd.city = 'Hamburg'\n    AND fd.yyyy_mm = '2022-02'\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\nORDER BY fd.date_valid_std ASC;\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-collaboration-kr/5.2.SQL.png\" alt=\"assets/5.2.SQL.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E방금 수신한 결과가 만족스럽습니다. 이제 다음 단계에서는 뷰에서 이 SQL을 래핑할 수 있습니다.\u003C/p\u003E\n","\u003Ch3\u003E3단계 - 분석 뷰 배포\u003C/h3\u003E\n","\u003Cp\u003E방금 탐색한 동일한 쿼리를 사용하여 WHERE 절에서 필터를 제거하고, \u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E를 추가하고, 이를 \u003Ccode\u003Edaily_city_metrics_v\u003C/code\u003E 뷰로 \u003Ccode\u003Eanalytics\u003C/code\u003E 스키마에 전달해야 합니다.\u003C/p\u003E\n","\u003Cp\u003E이 작업을 수행하기 위해 이제 이 섹션의 마지막 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\n    AS\nSELECT \n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nWHERE 1=1\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\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-collaboration-kr/5.3.view.png\" alt=\"assets/5.3.view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E이제 이러한 인사이트를 Tasty Bytes 조직을 대상으로 민주화했습니다. 다음 섹션에서 모두 결합하여 작업을 검증하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E4단계 - 다음 클릭 -->\u003C/h3\u003E\n","\u003Ch2\u003E매출 및 마켓플레이스 날씨 데이터에서 인사이트 도출\u003C/h2\u003E\n","\u003Ch3\u003E개요\u003C/h3\u003E\n","\u003Cp\u003E푸드 트럭이 운영되는 모든 도시에 대한 사용 가능한 매출 및 날씨 데이터를 통해 이제 금융 분석가에게 인사이트를 제공하는 데 걸리는 시간을 단축한 방법을 알아보겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E1단계 - 분석 간소화\u003C/h3\u003E\n","\u003Cp\u003E앞서 함부르크 매출 이슈를 조사하기 위해 판매 시점 및 Weather Source 데이터를 수동으로 결합했지만, \u003Ccode\u003Eanalytics.daily_city_metrics_v\u003C/code\u003E 뷰를 통해 이 프로세스를 크게 간소화했습니다.\u003C/p\u003E\n","\u003Cp\u003E단일 뷰에서 이를 단순한 SELECT 문으로 만들어 이 분석을 얼마나 더 단순하게 만들었는지를 보여주는 다음 쿼리를 실행하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    dcm.date,\n    dcm.city_name,\n    dcm.country_desc,\n    dcm.daily_sales,\n    dcm.avg_temperature_fahrenheit,\n    dcm.avg_temperature_celsius,\n    dcm.avg_precipitation_inches,\n    dcm.avg_precipitation_millimeters,\n    dcm.max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\nWHERE 1=1\n    AND dcm.country_desc = 'Germany'\n    AND dcm.city_name = 'Hamburg'\n    AND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\nORDER BY date DESC;\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-collaboration-kr/6.1.results.png\" alt=\"assets/6.1.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E좋습니다!\u003C/strong\u003E 금융 분석가가 초기에 연구를 진행할 때 이것이 제공되었다면 인사이트를 바로 확인할 수 있기에 데이터 팀에 도움을 요청하지 않아도 되었을 것입니다.\u003C/p\u003E\n","\u003Cp\u003E이 Quickstart를 완료하면서 작업을 통해 실제 비즈니스 가치를 얼마나 빠르게 도출할 수 있는지 그리고 추가적인 데이터 인사이트를 확보하기 위해 Snowflake 마켓플레이스를 사용하는 것이 얼마나 쉬운지 확인했습니다.\u003C/p\u003E\n","\u003Ch3\u003E2단계 - 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 - 협업 Quickstart를 성공적으로 완료하셨습니다.\u003C/p\u003E\n","\u003Cp\u003E이를 완료하면서 진행한 내용은 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake 마켓플레이스에 액세스\u003C/li\u003E\u003Cli\u003E계정에서 라이브 Weather Source 데이터 획득\u003C/li\u003E\u003Cli\u003E뷰 생성\u003C/li\u003E\u003Cli\u003ESQL 함수 생성\u003C/li\u003E\u003Cli\u003E시각적 인사이트를 탐색하기 위해 Snowsight 그래프 활용\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## Snowflake 마켓플레이스를 통한 서드 파티 데이터에 대한 직접 액세스\r\n\r\n\r\n### 개요\r\n\r\n협업을 집중적으로 다루는 Tasty Bytes - 제로부터 Snowflake까지 Quickstart에 오신 것을 환영합니다! 이 Quickstart에서는 Snowflake 마켓플레이스를 통해 자사 데이터를 날씨 데이터로 보강하는 방법을 집중적으로 다루겠습니다. Snowflake 마켓플레이스는 그 어떠한 ETL, 데이터 파이프라인 또는 통합도 설정하지 않고 서드 파티 데이터와 앱을 찾고, 체험하고, 구매하는 장소입니다.\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- Snowflake 마켓플레이스에 액세스하기\r\n- 계정에서 라이브 Weather Source 데이터 획득하기\r\n- 뷰 생성하기\r\n- SQL 함수 생성하기\r\n- 시각적 인사이트를 탐색하기 위해 Snowsight 그래프 활용하기\r\n\r\n### 구축할 것\r\n\r\n- 자사 매출 및 서드 파티 날씨 데이터 조정\r\n- 원활한 화씨 섭씨 변환 가능성\r\n- 원활한 인치 밀리미터 변환 가능성\r\n- Snowflake 마켓플레이스를 통해 추가 인사이트를 얻는 방법 이해\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-collaboration-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-collaboration-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-collaboration-kr/+_sqlworksheet.png)\r\n\r\n\r\n### 5단계 - 워크시트 이름 바꾸기\r\n\r\n- 자동으로 생성된 Timestamp 이름을 클릭하고 ‘Tasty Bytes - Collaboration’을 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-collaboration-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.\r\n\r\n\u003Cbutton\u003E[tb_zts_collaboration.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.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-collaboration-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## 자사 데이터에서 매출이 0인 날짜 조사\r\n\r\n\r\n### 개요\r\n\r\nTasty Bytes 금융 분석가는 전년 대비 분석을 실행할 때 여러 도시에서 트럭 매출이 0인 설명할 수 없는 날짜가 존재한다고 알려왔습니다. 금융 분석가가 제시한 하나의 사례는 2022년 2월 독일 함부르크입니다.\r\n\r\n### 1단계 - 추세를 위해 판매 시점 데이터 쿼리\r\n\r\n초기에 역할과 웨어하우스 콘텍스트를 `tasty_data_engineer` 및 `tasty_de_wh`로 설정하기 위해 이 단계에서 3개의 쿼리를 실행하며 시작하겠습니다. 콘텍스트가 설정되면 분석 `orders_v` 뷰를 쿼리하여 2022년 독일 함부르크의 매출 결과 세트를 제공하겠습니다.\r\n\r\n```\r\nUSE ROLE tasty_data_engineer;\r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT \r\n    o.date,\r\n    SUM(o.price) AS daily_sales\r\nFROM frostbyte_tasty_bytes.analytics.orders_v o\r\nWHERE 1=1\r\n    AND o.country = 'Germany'\r\n    AND o.primary_city = 'Hamburg'\r\n    AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28'\r\nGROUP BY o.date\r\nORDER BY o.date ASC;\r\n```\r\n\r\n![assets/3.1.orders_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/3.1.orders_v.png)\r\n위에서 확인한 내용에 따르면 2월의 몇몇 날짜에 일일 매출 기록이 없다는 점에서 분석가의 의견에 동의할 수 있습니다. 따라서 분석가는 확실히 문제가 있는 부분을 지적했습니다. 다음 섹션에서는 이러한 현상이 발생한 이유를 조금 더 자세히 알아보겠습니다.\r\n\r\n### 2단계 - 다음 클릭 --\u003E\r\n\r\n## Snowflake 마켓플레이스의 Weather Source 데이터 활용\r\n\r\n\r\n### 개요\r\n\r\n이전 섹션을 보면 2월 16일부터 2월 21일까지의 독일 함부르크에서 발생한 매출이 누락된 것 같습니다.  자사 데이터 내에서는 이 부분을 조사하기 위해 사용할 수 있는 것이 거의 없습니다. 하지만 더 큰 요인이 여기에 영향을 줬을 것입니다.\r\n\r\n[Snowflake 마켓플레이스](/ko/data-cloud/marketplace/)를 활용하여 바로 탐색할 수 있는 하나의 아이디어는 기상 이변과 Weather Source에서 제공한 무료 공개 목록입니다.\r\n\r\n### 1단계 - Weather Source LLC: frostbyte Snowflake 마켓플레이스 목록 획득\r\n\r\nSnowflake 마켓플레이스는 혁신적인 비즈니스 솔루션을 지원하기 위해 필요한 데이터와 애플리케이션을 찾고, 체험하고, 구매하기 가장 좋은 장소입니다. 이 단계에서는 [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) 목록에 액세스하여 함부르크 매출 하락에 대한 추가 분석을 수행하는 것을 돕겠습니다.\r\n\r\n아래 단계와 동영상에 따라 Snowflake 계정에서 이 목록을 획득합니다.\r\n\r\n- 클릭 -\u003E 홈\r\n- 클릭 -\u003E Marketplace\r\n- 검색 -\u003E frostbyte\r\n- 클릭 -\u003E Weather Source LLC: frostbyte\r\n- 클릭 -\u003E Get\r\n- 데이터베이스 이름 바꾸기 -\u003E FROSTBYTE_WEATHERSOURCE(전부 대문자)\r\n- 추가 역할에 부여 -\u003E PUBLIC\r\n\r\n![assets/4.1.acquire_weather_source.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.1.acquire_weather_source.gif)\r\n\u003E aside positive Weather Source는 선도적인 전 세계 날씨 및 기후 데이터 공급자입니다. 이 회사의 OnPoint Product Suite는 비즈니스가 여러 산업에 걸쳐 다양한 사용 사례에 대한 의미 있고 실행 가능한 인사이트를 빠르게 생성하는 데 필요한 날씨 및 기후 데이터를 제공합니다.\r\n\r\n### 2단계 - 자사 및 서드 파티 데이터 조정\r\n\r\n공유 `frostbyte_weathersource` 데이터베이스를 사용하는 상태로 이 단계의 쿼리를 실행하여 `harmonized.daily_weather_v` 뷰를 생성하십시오. 그러면 2개의 Weather Source 테이블과 Tasty Bytes 푸드 트럭이 운영되는 국가와 도시에 대한 국가 테이블을 결합할 수 있습니다.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\r\n    AS\r\nSELECT \r\n    hd.*,\r\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\r\n    pc.city_name AS city,\r\n    c.country AS country_desc\r\nFROM frostbyte_weathersource.onpoint_id.history_day hd\r\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\r\n    ON pc.postal_code = hd.postal_code\r\n    AND pc.country = hd.country\r\nJOIN frostbyte_tasty_bytes.raw_pos.country c\r\n    ON c.iso_country = hd.country\r\n    AND c.city = hd.city_name;\r\n```\r\n\r\n![assets/4.2.daily_weather_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.2.daily_weather_v.png)\r\n위 뷰 정의에서 확인할 수 있듯이 `onpoint_id` 스키마 내 2개의 `frostbyte_weathersource` 테이블을 결합한 다음 `frostbyte_tasty_bytes` 데이터베이스 및 `raw_pos` 스키마의 `country` 테이블과 이를 조정하고 있습니다.\r\n\r\n이러한 작업은 일반적으로 조정 계층(실버 영역이라고도 함)에서 찾아볼 수 있습니다.\r\n\r\n### 3단계 - 일일 기온 시각화\r\n\r\n조정 스키마에서 `daily_weather_v` 뷰를 사용하는 상태로 다음 쿼리를 실행하여 2022년 2월 함부르크의 일평균 기온을 살펴보겠습니다.\r\n\r\n이 과정에서 [AVG](https://docs.snowflake.com/ko/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/ko/sql-reference/functions/year) 및 [MONTH](https://docs.snowflake.com/ko/sql-reference/functions/year) 함수를 활용하겠습니다.\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc = 'Germany'\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.3.results.png)\r\n\r\n추세를 추가적으로 조사하기 위해 Snowsight 그래프 생성을 활용하여 시간의 흐름에 따른 평균 기온의 선 그래프를 생성하겠습니다.\r\n\r\n![assets/4.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.3.chart.png)\r\n\r\n위에서 확인한 내용에 따르면 트럭에서 매출이 없는 날짜가 발생한 명백한 이유를 아직 찾지 못했습니다. 다음 단계에서 이유를 설명할 수도 있는 다른 요인을 찾아보겠습니다.\r\n\r\n### 4단계 - 바람 데이터 가져오기\r\n\r\n이전 단계에서 확인했듯이 일평균 기온은 함부르크에서 매출이 0인 날짜가 발생한 이유가 아닌 것 같습니다. 다행히 Weather Source는 심층적으로 알아볼 수 있는 다른 날씨 지표를 제공합니다.\r\n\r\n이제 바람 지표를 가져오기 위해 조정 뷰를 활용할 다음 쿼리를 실행하십시오. 이 쿼리에서는 [MAX](https://docs.snowflake.com/ko/sql-reference/functions/min) 함수가 사용됩니다.\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc IN ('Germany')\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.4.result.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.4.result.png)\r\n\r\n여기서도 빠른 Snowsight 그래프를 통해 이러한 데이터의 추세를 더 잘 나타낼 수 있습니다. 결과에서 그래프로 이동하기 위해 아래 스크린샷에 있는 화살표를 따르십시오.\r\n\r\n![assets/4.4.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/4.4.chart.png)\r\n\r\n**찾았습니다!** 매출이 0인 날짜에는 바람이 태풍 수준이었습니다. 이것이 바로 트럭이 이러한 날짜에 아무것도 판매할 수 없었던 더 타당한 이유인 것 같습니다. 하지만 조정에서 이 분석을 실행했기에 이제 분석가가 이러한 인사이트에 스스로 액세스할 수 있도록 분석에 이 내용을 제공하기 위한 여정을 시작하겠습니다.\r\n\r\n### 5단계 - 다음 클릭 --\u003E\r\n\r\n## 데이터 인사이트 민주화\r\n\r\n\r\n### 개요\r\n\r\n이제 태풍 수준의 바람이 아마도 금융 분석가가 언급했던 매출이 0인 날짜에 영향을 준 것 같다고 판단했습니다.\r\n\r\n모든 Tasty Bytes 직원이 액세스할 수 있는 분석 뷰를 배포하여 이제 이러한 연구를 조직 내 모두에게 제공하겠습니다.\r\n\r\n### 1단계- SQL 함수 생성\r\n\r\n세계적인 회사로서 우선 화씨 섭씨 변환 및 인치 밀리미터 변환을 위한 2개의 SQL 함수를 생성하며 프로세스를 시작하겠습니다.\r\n\r\n이 단계에서는 [CREATE FUNCTION](https://docs.snowflake.com/ko/sql-reference/sql/create-function) 명령을 활용하는 `fahrenheit_to_celsius` 및 `inch_to_millimeter` 함수를 생성하기 위해 2개의 쿼리를 하나씩 실행하십시오.\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\nAS\r\n$$\r\n    (temp_f - 32) * (5/9)\r\n$$;\r\n```\r\n\r\n![assets/5.1.f_to_c.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/5.1.f_to_c.png)\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\n    AS\r\n$$\r\n    inch * 25.4\r\n$$;\r\n```\r\n\r\n![assets/5.1.inch_to_mm.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/5.1.inch_to_mm.png)\r\n\r\n\u003E aside positive UDF를 생성할 때 지원되는 언어 중 하나로 코드가 작성된 핸들러를 지정합니다. 핸들러의 언어에 따라 CREATE FUNCTION 문이 포함된 핸들러 소스 코드 인라인을 포함하거나 핸들러가 미리 컴파일되었거나 스테이지의 소스 코드인 경우에 CREATE FUNCTION에서 핸들러 위치를 참조할 수 있습니다.\r\n\r\n### 2단계 - 뷰를 위한 SQL 생성\r\n\r\n분석 뷰를 배포하기 전에 일일 매출과 날씨를 결합하기 위해 뷰에서 사용할 SQL을 생성하고 SQL 변환 함수를 활용하겠습니다.\r\n\r\n독일 함부르크를 필터링하고 [ZEROIFNULL](https://docs.snowflake.com/ko/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/ko/sql-reference/functions/round) 및 [DATE](https://docs.snowflake.com/ko/sql-reference/functions/to_date)와 같이 아직 확인하지 않은 몇몇 함수를 활용하는 다음 쿼리를 실행하십시오.\r\n\r\n```\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\n    AND fd.country_desc = 'Germany'\r\n    AND fd.city = 'Hamburg'\r\n    AND fd.yyyy_mm = '2022-02'\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\r\nORDER BY fd.date_valid_std ASC;\r\n```\r\n\r\n![assets/5.2.SQL.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/5.2.SQL.png)\r\n\r\n방금 수신한 결과가 만족스럽습니다. 이제 다음 단계에서는 뷰에서 이 SQL을 래핑할 수 있습니다.\r\n\r\n### 3단계 - 분석 뷰 배포\r\n\r\n방금 탐색한 동일한 쿼리를 사용하여 WHERE 절에서 필터를 제거하고, [COMMENT](https://docs.snowflake.com/ko/sql-reference/sql/comment)를 추가하고, 이를 `daily_city_metrics_v` 뷰로 `analytics` 스키마에 전달해야 합니다.\r\n\r\n이 작업을 수행하기 위해 이제 이 섹션의 마지막 쿼리를 실행하십시오.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\r\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\r\n    AS\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\r\n```\r\n\r\n![assets/5.3.view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/5.3.view.png)\r\n\r\n이제 이러한 인사이트를 Tasty Bytes 조직을 대상으로 민주화했습니다. 다음 섹션에서 모두 결합하여 작업을 검증하겠습니다.\r\n\r\n### 4단계 - 다음 클릭 --\u003E\r\n\r\n## 매출 및 마켓플레이스 날씨 데이터에서 인사이트 도출\r\n\r\n\r\n### 개요\r\n\r\n푸드 트럭이 운영되는 모든 도시에 대한 사용 가능한 매출 및 날씨 데이터를 통해 이제 금융 분석가에게 인사이트를 제공하는 데 걸리는 시간을 단축한 방법을 알아보겠습니다.\r\n\r\n### 1단계 - 분석 간소화\r\n\r\n앞서 함부르크 매출 이슈를 조사하기 위해 판매 시점 및 Weather Source 데이터를 수동으로 결합했지만, `analytics.daily_city_metrics_v` 뷰를 통해 이 프로세스를 크게 간소화했습니다.\r\n\r\n단일 뷰에서 이를 단순한 SELECT 문으로 만들어 이 분석을 얼마나 더 단순하게 만들었는지를 보여주는 다음 쿼리를 실행하십시오.\r\n\r\n```\r\nSELECT \r\n    dcm.date,\r\n    dcm.city_name,\r\n    dcm.country_desc,\r\n    dcm.daily_sales,\r\n    dcm.avg_temperature_fahrenheit,\r\n    dcm.avg_temperature_celsius,\r\n    dcm.avg_precipitation_inches,\r\n    dcm.avg_precipitation_millimeters,\r\n    dcm.max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\r\nWHERE 1=1\r\n    AND dcm.country_desc = 'Germany'\r\n    AND dcm.city_name = 'Hamburg'\r\n    AND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\r\nORDER BY date DESC;\r\n```\r\n\r\n![assets/6.1.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-kr/6.1.results.png)\r\n\r\n**좋습니다!** 금융 분석가가 초기에 연구를 진행할 때 이것이 제공되었다면 인사이트를 바로 확인할 수 있기에 데이터 팀에 도움을 요청하지 않아도 되었을 것입니다.\r\n\r\n이 Quickstart를 완료하면서 작업을 통해 실제 비즈니스 가치를 얼마나 빠르게 도출할 수 있는지 그리고 추가적인 데이터 인사이트를 확보하기 위해 Snowflake 마켓플레이스를 사용하는 것이 얼마나 쉬운지 확인했습니다.\r\n\r\n### 2단계 - 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 - 협업 Quickstart를 성공적으로 완료하셨습니다.\r\n\r\n이를 완료하면서 진행한 내용은 다음과 같습니다.\r\n\r\n- Snowflake 마켓플레이스에 액세스\r\n- 계정에서 라이브 Weather Source 데이터 획득\r\n- 뷰 생성\r\n- SQL 함수 생성\r\n- 시각적 인사이트를 탐색하기 위해 Snowsight 그래프 활용\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-1171385625","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-6467e4fc00",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-02b80e7243","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-2bbedb6cd5","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. 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