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general\u003C/h3\u003E\n","\u003Cp\u003ETe damos la bienvenida a la quickstart guide De cero a Snowflake de Tasty Bytes centrada en la colaboración. En esta quickstart guide explicaremos cómo puedes enriquecer tus datos de primera mano con datos meteorológicos a través de Snowflake Marketplace. En Snowflake Marketplace podrás encontrar, probar y comprar datos y aplicaciones de terceros sin tener que configurar procesos de extracción, transformación y carga (extract, transform, load; ETL), flujos de datos ni integraciones.\u003C/p\u003E\n","\u003Ch3\u003ERequisitos previos\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAntes de empezar, asegúrate de haber completado la \u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003E\u003Cstrong\u003Equickstart guide Introducción a Tasty Bytes\u003C/strong\u003E\u003C/a\u003E, donde aprenderás a configurar una cuenta de prueba y a implementar los conceptos esenciales de Tasty Bytes necesarios para completar esta quickstart guide.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EContenido que se enseñará\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECómo acceder a Snowflake Marketplace\u003C/li\u003E\u003Cli\u003ECómo obtener datos actualizados de Weather Source en tu cuenta\u003C/li\u003E\u003Cli\u003ECómo crear una vista\u003C/li\u003E\u003Cli\u003ECómo crear una función SQL\u003C/li\u003E\u003Cli\u003ECómo utilizar los gráficos de Snowsight para analizar la información visual\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECosas que podrás conseguir\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EArmonización de datos meteorológicos de primera mano y de terceros\u003C/li\u003E\u003Cli\u003EConversión de grados Fahrenheit a Celsius\u003C/li\u003E\u003Cli\u003EConversión de pulgadas a milímetros\u003C/li\u003E\u003Cli\u003ECómo obtener información adicional a través de Snowflake Marketplace\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECreación de una hoja de trabajo y copia en SQL\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EEn esta quickstart guide se narra una historia de Tasty Bytes mediante una hoja de trabajo SQL en Snowsight. Esta página servirá de guía completa paralela con comentarios adicionales, imágenes y enlaces a la documentación.\u003C/p\u003E\n","\u003Cp\u003EEn esta sección te explicaremos cómo iniciar sesión en Snowflake, crear una nueva hoja de trabajo, cambiar el nombre de la hoja de trabajo, copiar SQL de GitHub y pegar el que utilizaremos en esta quickstart guide.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: acceder a Snowflake con la URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAbre una ventana del navegador e introduce la URL de tu cuenta de Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPaso 2: iniciar sesión en Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EInicia sesión en tu cuenta de Snowflake.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/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\u003EPaso 3: acceder a las hojas de trabajo\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHaz clic en la pestaña Worksheets en la barra de navegación situada a la izquierda para acceder a las hojas de trabajo.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/worksheet_tab.png\" alt=\"assets/worksheet_tab.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPaso 4: crear una hoja de trabajo\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EEn la pestaña Worksheets, haz clic en el botón “+” en la esquina superior derecha de Snowsight y elige “SQL Worksheet”.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/+_sqlworksheet.png\" alt=\"assets/+_sqlworksheet.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPaso 5: cambiar el nombre de una hoja de trabajo\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECambia el nombre de la hoja de trabajo haciendo clic en el nombre generado automáticamente (marca de tiempo) e introduce “Tasty Bytes - Collaboration"\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/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\u003EPaso 6: acceder a la quickstart guide de SQL en GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHaz clic en el botón que aparece a continuación. Te redirigirá a nuestro archivo SQL de Tasty Bytes alojado en GitHub.\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\u003EPaso 7: copiar el código SQL de configuración de GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EEn GitHub, ve al lado derecho y haz clic en “Copy raw contents”. De esta forma, se copiará todo el código SQL necesario en el portapapeles.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/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\u003EPaso 8: pegar el código SQL de configuración de GitHub en tu hoja de trabajo de Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EVuelve a la hoja de trabajo que acabas de crear en Snowsight y pega (\u003Cem\u003ECMD + V para Mac o CTRL + V para Windows\u003C/em\u003E) lo que has copiado de GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPaso 9: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EInvestigación de los días sin ventas en los datos de primera mano\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003ELos analistas financieros de Tasty Bytes nos han informado de que, al ejecutar análisis interanuales, hay días en varias ciudades en los que las ventas de los food trucks se quedaron a 0 sin ningún motivo aparente. Un ejemplo que nos dieron fue Hamburgo (Alemania) en febrero de 2022.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: consultar los datos de los puntos de venta en busca de tendencias\u003C/h3\u003E\n","\u003Cp\u003EVamos a empezar ejecutando las tres consultas de este paso para configurar los contextos iniciales del rol \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E y del almacén \u003Ccode\u003Etasty_de_wh\u003C/code\u003E. Cuando hayamos establecido el contexto, consultaremos la vista de analíticas \u003Ccode\u003Eorders_v\u003C/code\u003E para proporcionar un conjunto de resultados de las ventas de Hamburgo (Alemania) en 2022.\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-es/3.1.orders_v.png\" alt=\"assets/3.1.orders_v.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBasándonos en lo que acabamos de ver, estamos de acuerdo con los analistas en que hay días de febrero en los que no tenemos registros de ventas. Por tanto, está claro que los analistas han descubierto algo. Veamos si podemos investigar aún más para saber por qué puede haber ocurrido esto en la siguiente sección.\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EUso de los datos de Weather Source desde Snowflake Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EComo hemos podido ver en la sección anterior, parece que nos faltan ventas entre el 16 y el 21 de febrero en Hamburgo (Alemania). En nuestros datos de primera mano no hay mucha más información que podamos utilizar para seguir investigando, pero debe de haber ocurrido algo importante.\u003C/p\u003E\n","\u003Cp\u003EUna idea que podemos estudiar directamente si utilizamos \u003Ca href=\"/es/data-cloud/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E son los fenómenos meteorológicos extremos y una lista pública gratuita proporcionada por Weather Source.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: obtener la lista de Snowflake Marketplace de Weather Source LLC: frostbyte\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Marketplace es la ubicación de referencia para buscar, probar y adquirir los datos y aplicaciones que necesitas para desarrollar soluciones empresariales innovadoras. En este paso, accederemos a la lista de \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte\"\u003EWeather Source LLC: frostbyte\u003C/a\u003E para poder realizar análisis adicionales sobre el desplome de las ventas en Hamburgo.\u003C/p\u003E\n","\u003Cp\u003ESigue los pasos y el vídeo que se muestran a continuación para conseguir esta lista en tu cuenta de Snowflake.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHaz clic en Home.\u003C/li\u003E\u003Cli\u003EHaz clic en Marketplace.\u003C/li\u003E\u003Cli\u003EBusca frostbyte.\u003C/li\u003E\u003Cli\u003EHaz clic en Weather Source LLC: frostbyte.\u003C/li\u003E\u003Cli\u003EHaz clic en Get.\u003C/li\u003E\u003Cli\u003ECambia el nombre de la base de datos a FROSTBYTE_WEATHERSOURCE (todo en mayúsculas).\u003C/li\u003E\u003Cli\u003EOtorga la condición PUBLIC a los roles adicionales.\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-es/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 Source es un destacado proveedor de datos meteorológicos y climáticos mundiales, y su conjunto de productos OnPoint proporciona a las empresas los datos meteorológicos y climáticos necesarios para generar rápidamente información práctica y útil en un amplio abanico de casos de uso en distintos sectores.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPaso 2: armonizar los datos propios y de terceros\u003C/h3\u003E\n","\u003Cp\u003EUna vez establecida la base de datos compartida \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E, ejecuta la consulta de este paso para crear una vista \u003Ccode\u003Eharmonized.daily_weather_v\u003C/code\u003E que una dos tablas de Weather Source a nuestra tabla de países con los países y ciudades en los que operan los food trucks de Tasty Bytes.\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-es/4.2.daily_weather_v.png\" alt=\"assets/4.2.daily_weather_v.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EComo vemos en la definición de la vista un poco más arriba, vamos a combinar las dos tablas \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E en el esquema \u003Ccode\u003Eonpoint_id\u003C/code\u003E y, a continuación, vamos a armonizarlas con nuestra tabla \u003Ccode\u003Ecountry\u003C/code\u003E de la base de datos \u003Ccode\u003Efrostbyte_tasty_bytes\u003C/code\u003E y el esquema \u003Ccode\u003Eraw_pos\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EEste tipo de operación se suele encontrar en la capa armonizada, también conocida como zona Plata.\u003C/p\u003E\n","\u003Ch3\u003EPaso 3: visualizar las temperaturas diarias\u003C/h3\u003E\n","\u003Cp\u003ECon la vista \u003Ccode\u003Edaily_weather_v\u003C/code\u003E en el esquema armonizado, vamos a echar un vistazo a la temperatura media diaria de Hamburgo en febrero de 2022 ejecutando la siguiente consulta.\u003C/p\u003E\n","\u003Cp\u003EDurante el proceso utilizaremos las funciones \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/avg\"\u003EAVG\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/year\"\u003EYEAR\u003C/a\u003E y \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/year\"\u003EMONTH\u003C/a\u003E.\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-es/4.3.results.png\" alt=\"assets/4.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPara investigar más a fondo las tendencias, vamos a utilizar los gráficos de Snowsight para crear un gráfico lineal de la temperatura media a lo largo del tiempo.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/4.3.chart.png\" alt=\"assets/4.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe acuerdo con lo hemos visto antes, aún no hay ningún dato que nos llame la atención como para considerarlo el causante de los días sin ventas de los food trucks. Veamos en el siguiente paso qué más podemos encontrar para explicar lo ocurrido.\u003C/p\u003E\n","\u003Ch3\u003EPaso 4: añadir los datos sobre el viento\u003C/h3\u003E\n","\u003Cp\u003EComo hemos visto en el paso anterior, no parece que la temperatura media diaria sea la causante de los días sin ventas en Hamburgo. Afortunadamente, Weather Source proporciona otras métricas meteorológicas en las que también podemos profundizar.\u003C/p\u003E\n","\u003Cp\u003EEjecuta la consulta donde utilizaremos la vista armonizada para incluir las métricas de viento. En esta consulta veremos cómo se usa la función \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/min\"\u003EMAX\u003C/a\u003E.\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-es/4.4.result.png\" alt=\"assets/4.4.result.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe nuevo, este tipo de datos podría mostrar mejor las tendencias a través de un rápido gráfico de Snowsight. Sigue las flechas de las siguientes capturas de pantalla para ir de Results a Charts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/4.4.chart.png\" alt=\"assets/4.4.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E¡Eureka!\u003C/strong\u003E El viento de los días sin ventas estaba a niveles de huracán. Parece una buena razón para explicar por qué los food trucks no pudieron vender nada esos días. Sin embargo, ya que hemos ejecutado este análisis en la capa armonizada, vamos a empezar por hacerlo accesible en la capa analítica, donde los analistas podrán acceder a esta información por su cuenta.\u003C/p\u003E\n","\u003Ch3\u003EPaso 5: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EDemocratización de la información de los datos\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EAhora sabemos que probablemente los vientos huracanados influyeran en los días sin ventas que nos comunicaron los analistas financieros.\u003C/p\u003E\n","\u003Cp\u003EVamos a poner esta investigación a disposición de cualquier persona de la organización. Para ello, habrá que implementar una vista de analíticas a la que puedan acceder todos los empleados de Tasty Bytes.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: crear funciones SQL\u003C/h3\u003E\n","\u003Cp\u003EComo somos una empresa global, vamos a empezar el proceso con la creación de dos funciones SQL para convertir Fahrenheit a Celsius y pulgadas a milímetros.\u003C/p\u003E\n","\u003Cp\u003EEjecuta las dos consultas del primer paso una a una para crear las funciones \u003Ccode\u003Efahrenheit_to_celsius\u003C/code\u003E y \u003Ccode\u003Einch_to_millimeter\u003C/code\u003E, que utilizarán el comando \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-function\"\u003ECREATE FUNCTION\u003C/a\u003E.\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-es/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-es/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 Cuando crees una función definida por el usuario (user-defined function, UDF), tendrás que especificar un controlador cuyo código se escriba en uno de los lenguajes compatibles. En función del lenguaje del controlador, podrás incluir el código fuente del controlador en línea mediante la sentencia CREATE FUNCTION o hacer referencia a la ubicación del controlador desde CREATE FUNCTION, donde el controlador esté precompilado o el código fuente esté en una fase.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPaso 2: crear el SQL para nuestra vista\u003C/h3\u003E\n","\u003Cp\u003EAntes de implementar la vista de analíticas, vamos a crear el SQL que utilizaremos en la vista para combinar las ventas diarias y el tiempo, y también para utilizar nuestras funciones SQL de conversión.\u003C/p\u003E\n","\u003Cp\u003EEjecuta la siguiente consulta donde filtraremos por Hamburgo (Alemania) y utilizaremos algunas funciones que aún no hemos explicado: \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/zeroifnull\"\u003EZEROIFNULL\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/round\"\u003EROUND\u003C/a\u003E y \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/to_date\"\u003EDATE\u003C/a\u003E.\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-es/5.2.SQL.png\" alt=\"assets/5.2.SQL.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELos resultados que acabamos de recibir tienen buena pinta. Ahora, en el siguiente paso, vamos a incluir este código SQL en una vista.\u003C/p\u003E\n","\u003Ch3\u003EPaso 3: desplegar la vista analítica\u003C/h3\u003E\n","\u003Cp\u003EVamos a utilizar la misma consulta que acabamos de analizar, pero tendremos que eliminar los filtros de la cláusula WHERE, añadir \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E y promocionarlo en nuestro esquema \u003Ccode\u003Eanalytics\u003C/code\u003E como la vista \u003Ccode\u003Edaily_city_metrics_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EPara ello, inicia la última consulta de esta sección.\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-es/5.3.view.png\" alt=\"assets/5.3.view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E¡Genial! Ya hemos democratizado este tipo de información en la organización de Tasty Bytes. Vamos a recopilar todo esto en la siguiente sección y a comprobar nuestro trabajo.\u003C/p\u003E\n","\u003Ch3\u003EPaso 4: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EObtención de la información a partir de ventas y datos meteorológicos de Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EAhora que los datos de ventas y meteorológicos de todas las ciudades en las que operan nuestros food trucks ya están disponibles, vamos a ver cómo hemos reducido el tiempo que los analistas financieros dedican a obtener información.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: simplificar nuestros análisis\u003C/h3\u003E\n","\u003Cp\u003EHasta ahora, teníamos que unir manualmente los datos de los puntos de venta y Weather Source para poder investigar los problemas de ventas en Hamburgo, pero hemos simplificado enormemente ese proceso gracias a nuestra vista \u003Ccode\u003Eanalytics.daily_city_metrics_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EPon en marcha la siguiente consulta, que muestra cómo hemos simplificado este análisis al convertirlo en una sentencia SELECT en una sola vista.\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-es/6.1.results.png\" alt=\"assets/6.1.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E¡Toma ya!\u003C/strong\u003E Si los analistas financieros hubieran podido contar con esta información cuando comenzaron su investigación, no habrían tenido que recurrir a los equipos de datos, ya que toda la información estaba ahí.\u003C/p\u003E\n","\u003Cp\u003EAl completar esta quickstart guide, hemos visto la rapidez con la que podemos obtener valor empresarial real a partir de nuestro trabajo y lo fácil que es utilizar Snowflake Marketplace para obtener información adicional sobre los datos.\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusión y siguientes pasos\u003C/h2\u003E\n","\u003Ch3\u003EConclusión\u003C/h3\u003E\n","\u003Cp\u003E¡Bien hecho! Has completado la quickstart guide De cero a Snowflake de Tasty Bytes sobre colaboración.\u003C/p\u003E\n","\u003Cp\u003ELo que quiere decir que habrás: - accedido a Snowflake Marketplace, - obtenido datos actualizados de Weather Source en tu cuenta, - creado una vista, - creado una función SQL, - utilizado los gráficos de Snowsight para analizar la información de forma visual.\u003C/p\u003E\n","\u003Cp\u003ESi deseas volver a empezar esta quickstart guide, ejecuta las secuencias de comandos de restauración que aparecen en la parte inferior de la hoja de trabajo asociada.\u003C/p\u003E\n","\u003Ch3\u003ESiguientes pasos\u003C/h3\u003E\n","\u003Cp\u003ESi quieres ampliar tu experiencia con Snowflake Data Cloud, haz clic en el siguiente enlace para descubrir otras quickstart guides Powered by Tasty Bytes.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/es/developers/guides/tasty-bytes-introduction-es/\"\u003EPowered by Tasty Bytes - Índice de las quickstart guides\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Tasty Bytes - De cero a Snowflake - Colaboración",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Acceso directo a datos de terceros a través de Snowflake Marketplace\r\n\r\n### Descripción general\r\nTe damos la bienvenida a la quickstart guide De cero a Snowflake de Tasty Bytes centrada en la colaboración. En esta quickstart guide explicaremos cómo puedes enriquecer tus datos de primera mano con datos meteorológicos a través de Snowflake Marketplace. En Snowflake Marketplace podrás encontrar, probar y comprar datos y aplicaciones de terceros sin tener que configurar procesos de extracción, transformación y carga (extract, transform, load; ETL), flujos de datos ni integraciones.\r\n\r\n\r\n### Requisitos previos\r\n- Antes de empezar, asegúrate de haber completado la [**quickstart guide Introducción a Tasty Bytes**](/en/developers/guides/tasty-bytes-introduction/), donde aprenderás a configurar una cuenta de prueba y a implementar los conceptos esenciales de Tasty Bytes necesarios para completar esta quickstart guide.\r\n\r\n### Contenido que se enseñará\r\n- Cómo acceder a Snowflake Marketplace\r\n- Cómo obtener datos actualizados de Weather Source en tu cuenta\r\n- Cómo crear una vista\r\n- Cómo crear una función SQL\r\n- Cómo utilizar los gráficos de Snowsight para analizar la información visual\r\n\r\n### Cosas que podrás conseguir\r\n- Armonización de datos meteorológicos de primera mano y de terceros\r\n- Conversión de grados Fahrenheit a Celsius\r\n- Conversión de pulgadas a milímetros\r\n- Cómo obtener información adicional a través de Snowflake Marketplace\r\n\r\n## Creación de una hoja de trabajo y copia en SQL\r\n\r\n### Descripción general\r\nEn esta quickstart guide se narra una historia de Tasty Bytes mediante una hoja de trabajo SQL en Snowsight. Esta página servirá de guía completa paralela con comentarios adicionales, imágenes y enlaces a la documentación.\r\n\r\nEn esta sección te explicaremos cómo iniciar sesión en Snowflake, crear una nueva hoja de trabajo, cambiar el nombre de la hoja de trabajo, copiar SQL de GitHub y pegar el que utilizaremos en esta quickstart guide.\r\n\r\n### Paso 1: acceder a Snowflake con la URL\r\n- Abre una ventana del navegador e introduce la URL de tu cuenta de Snowflake. \r\n\r\n### Paso 2: iniciar sesión en Snowflake\r\n- Inicia sesión en tu cuenta de Snowflake.\r\n    - ![assets/log_into_snowflake.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/log_into_snowflake.gif)\r\n\r\n### Paso 3: acceder a las hojas de trabajo\r\n- Haz clic en la pestaña Worksheets en la barra de navegación situada a la izquierda para acceder a las hojas de trabajo.\r\n    - ![assets/worksheet_tab.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/worksheet_tab.png)\r\n\r\n### Paso 4: crear una hoja de trabajo\r\n- En la pestaña Worksheets, haz clic en el botón “+” en la esquina superior derecha de Snowsight y elige “SQL Worksheet”.\r\n    - ![assets/+_sqlworksheet.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/+_sqlworksheet.png)\r\n\r\n### Paso 5: cambiar el nombre de una hoja de trabajo\r\n- Cambia el nombre de la hoja de trabajo haciendo clic en el nombre generado automáticamente (marca de tiempo) e introduce “Tasty Bytes - Collaboration\"\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-es/rename_worksheet_tasty_bytes_setup.gif)\r\n\r\n### Paso 6: acceder a la quickstart guide de SQL en GitHub\r\n- Haz clic en el botón que aparece a continuación. Te redirigirá a nuestro archivo SQL de Tasty Bytes alojado en GitHub.\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### Paso 7: copiar el código SQL de configuración de GitHub\r\n- En GitHub, ve al lado derecho y haz clic en “Copy raw contents”. De esta forma, se copiará todo el código SQL necesario en el portapapeles.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-es/github_copy_raw_contents.png)\r\n\r\n### Paso 8: pegar el código SQL de configuración de GitHub en tu hoja de trabajo de Snowflake\r\n- Vuelve a la hoja de trabajo que acabas de crear en Snowsight y pega (*CMD + V para Mac o CTRL + V para Windows*) lo que has copiado de GitHub.\r\n\r\n### Paso 9: hacer clic en Next --\u003E\r\n\r\n## Investigación de los días sin ventas en los datos de primera mano\r\n\r\n### Descripción general\r\nLos analistas financieros de Tasty Bytes nos han informado de que, al ejecutar análisis interanuales, hay días en varias ciudades en los que las ventas de los food trucks se quedaron a 0 sin ningún motivo aparente. Un ejemplo que nos dieron fue Hamburgo (Alemania) en febrero de 2022.\r\n\r\n\r\n### Paso 1: consultar los datos de los puntos de venta en busca de tendencias\r\nVamos a empezar ejecutando las tres consultas de este paso para configurar los contextos iniciales del rol `tasty_data_engineer` y del almacén `tasty_de_wh`. Cuando hayamos establecido el contexto, consultaremos la vista de analíticas `orders_v` para proporcionar un conjunto de resultados de las ventas de Hamburgo (Alemania) en 2022.\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-es/3.1.orders_v.png)\r\n\r\nBasándonos en lo que acabamos de ver, estamos de acuerdo con los analistas en que hay días de febrero en los que no tenemos registros de ventas. Por tanto, está claro que los analistas han descubierto algo. Veamos si podemos investigar aún más para saber por qué puede haber ocurrido esto en la siguiente sección.\r\n\r\n### Paso 2: hacer clic en Next --\u003E\r\n\r\n## Uso de los datos de Weather Source desde Snowflake Marketplace\r\n\r\n### Descripción general\r\nComo hemos podido ver en la sección anterior, parece que nos faltan ventas entre el 16 y el 21 de febrero en Hamburgo (Alemania). En nuestros datos de primera mano no hay mucha más información que podamos utilizar para seguir investigando, pero debe de haber ocurrido algo importante. \r\n        \r\nUna idea que podemos estudiar directamente si utilizamos [Snowflake Marketplace](/es/data-cloud/marketplace/) son los fenómenos meteorológicos extremos y una lista pública gratuita proporcionada por Weather Source.\r\n\r\n### Paso 1: obtener la lista de Snowflake Marketplace de Weather Source LLC: frostbyte\r\nSnowflake Marketplace es la ubicación de referencia para buscar, probar y adquirir los datos y aplicaciones que necesitas para desarrollar soluciones empresariales innovadoras. En este paso, accederemos a la lista de [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) para poder realizar análisis adicionales sobre el desplome de las ventas en Hamburgo.\r\n\r\nSigue los pasos y el vídeo que se muestran a continuación para conseguir esta lista en tu cuenta de Snowflake.\r\n\r\n- Haz clic en Home.\r\n- Haz clic en Marketplace.\r\n- Busca frostbyte.\r\n- Haz clic en Weather Source LLC: frostbyte.\r\n- Haz clic en Get.\r\n- Cambia el nombre de la base de datos a FROSTBYTE_WEATHERSOURCE (todo en mayúsculas).\r\n- Otorga la condición PUBLIC a los roles adicionales.\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-es/4.1.acquire_weather_source.gif)\r\n\r\n\u003Easide positive Weather Source es un destacado proveedor de datos meteorológicos y climáticos mundiales, y su conjunto de productos OnPoint proporciona a las empresas los datos meteorológicos y climáticos necesarios para generar rápidamente información práctica y útil en un amplio abanico de casos de uso en distintos sectores.\r\n\u003E\r\n\r\n### Paso 2: armonizar los datos propios y de terceros\r\nUna vez establecida la base de datos compartida `frostbyte_weathersource`, ejecuta la consulta de este paso para crear una vista `harmonized.daily_weather_v` que una dos tablas de Weather Source a nuestra tabla de países con los países y ciudades en los que operan los food trucks de Tasty Bytes.\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-es/4.2.daily_weather_v.png)\r\n\r\nComo vemos en la definición de la vista un poco más arriba, vamos a combinar las dos tablas `frostbyte_weathersource` en el esquema `onpoint_id` y, a continuación, vamos a armonizarlas con nuestra tabla `country` de la base de datos `frostbyte_tasty_bytes` y el esquema `raw_pos`. \r\n\r\nEste tipo de operación se suele encontrar en la capa armonizada, también conocida como zona Plata.\r\n\r\n### Paso 3: visualizar las temperaturas diarias\r\nCon la vista `daily_weather_v` en el esquema armonizado, vamos a echar un vistazo a la temperatura media diaria de Hamburgo en febrero de 2022 ejecutando la siguiente consulta.\r\n\r\nDurante el proceso utilizaremos las funciones [AVG](https://docs.snowflake.com/en/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/en/sql-reference/functions/year) y [MONTH](https://docs.snowflake.com/en/sql-reference/functions/year).\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-es/4.3.results.png)\r\n\r\nPara investigar más a fondo las tendencias, vamos a utilizar los gráficos de Snowsight para crear un gráfico lineal de la temperatura media a lo largo del tiempo.\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-es/4.3.chart.png)\r\n\r\nDe acuerdo con lo hemos visto antes, aún no hay ningún dato que nos llame la atención como para considerarlo el causante de los días sin ventas de los food trucks. Veamos en el siguiente paso qué más podemos encontrar para explicar lo ocurrido.\r\n\r\n### Paso 4: añadir los datos sobre el viento\r\nComo hemos visto en el paso anterior, no parece que la temperatura media diaria sea la causante de los días sin ventas en Hamburgo. Afortunadamente, Weather Source proporciona otras métricas meteorológicas en las que también podemos profundizar. \r\n\r\nEjecuta la consulta donde utilizaremos la vista armonizada para incluir las métricas de viento. En esta consulta veremos cómo se usa la función [MAX](https://docs.snowflake.com/en/sql-reference/functions/min).\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-es/4.4.result.png)\r\n\r\nDe nuevo, este tipo de datos podría mostrar mejor las tendencias a través de un rápido gráfico de Snowsight. Sigue las flechas de las siguientes capturas de pantalla para ir de Results a Charts.\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-es/4.4.chart.png)\r\n\r\n**¡Eureka!** El viento de los días sin ventas estaba a niveles de huracán. Parece una buena razón para explicar por qué los food trucks no pudieron vender nada esos días. Sin embargo, ya que hemos ejecutado este análisis en la capa armonizada, vamos a empezar por hacerlo accesible en la capa analítica, donde los analistas podrán acceder a esta información por su cuenta.\r\n\r\n### Paso 5: hacer clic en Next --\u003E\r\n\r\n## Democratización de la información de los datos\r\n\r\n### Descripción general\r\nAhora sabemos que probablemente los vientos huracanados influyeran en los días sin ventas que nos comunicaron los analistas financieros.\r\n\r\nVamos a poner esta investigación a disposición de cualquier persona de la organización. Para ello, habrá que implementar una vista de analíticas a la que puedan acceder todos los empleados de Tasty Bytes.\r\n\r\n### Paso 1: crear funciones SQL\r\nComo somos una empresa global, vamos a empezar el proceso con la creación de dos funciones SQL para convertir Fahrenheit a Celsius y pulgadas a milímetros. \r\n\r\nEjecuta las dos consultas del primer paso una a una para crear las funciones `fahrenheit_to_celsius` y `inch_to_millimeter`, que utilizarán el comando [CREATE FUNCTION](https://docs.snowflake.com/en/sql-reference/sql/create-function).\r\n\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-es/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-es/5.1.inch_to_mm.png)\r\n\r\n\u003Easide positive Cuando crees una función definida por el usuario (user-defined function, UDF), tendrás que especificar un controlador cuyo código se escriba en uno de los lenguajes compatibles. En función del lenguaje del controlador, podrás incluir el código fuente del controlador en línea mediante la sentencia CREATE FUNCTION o hacer referencia a la ubicación del controlador desde CREATE FUNCTION, donde el controlador esté precompilado o el código fuente esté en una fase.\r\n\u003E\r\n\r\n### Paso 2: crear el SQL para nuestra vista\r\nAntes de implementar la vista de analíticas, vamos a crear el SQL que utilizaremos en la vista para combinar las ventas diarias y el tiempo, y también para utilizar nuestras funciones SQL de conversión. \r\n\r\nEjecuta la siguiente consulta donde filtraremos por Hamburgo (Alemania) y utilizaremos algunas funciones que aún no hemos explicado: [ZEROIFNULL](https://docs.snowflake.com/en/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/en/sql-reference/functions/round) y [DATE](https://docs.snowflake.com/en/sql-reference/functions/to_date).\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-es/5.2.SQL.png)\r\n\r\n\r\nLos resultados que acabamos de recibir tienen buena pinta. Ahora, en el siguiente paso, vamos a incluir este código SQL en una vista.\r\n\r\n### Paso 3: desplegar la vista analítica\r\nVamos a utilizar la misma consulta que acabamos de analizar, pero tendremos que eliminar los filtros de la cláusula WHERE, añadir [COMMENT](https://docs.snowflake.com/en/sql-reference/sql/comment) y promocionarlo en nuestro esquema `analytics` como la vista `daily_city_metrics_v`.\r\n\r\nPara ello, inicia la última consulta de esta sección.\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-es/5.3.view.png)\r\n\r\n¡Genial! Ya hemos democratizado este tipo de información en la organización de Tasty Bytes. Vamos a recopilar todo esto en la siguiente sección y a comprobar nuestro trabajo.\r\n\r\n### Paso 4: hacer clic en Next --\u003E\r\n\r\n## Obtención de la información a partir de ventas y datos meteorológicos de Marketplace\r\n\r\n### Descripción general\r\nAhora que los datos de ventas y meteorológicos de todas las ciudades en las que operan nuestros food trucks ya están disponibles, vamos a ver cómo hemos reducido el tiempo que los analistas financieros dedican a obtener información.\r\n\r\n### Paso 1: simplificar nuestros análisis\r\nHasta ahora, teníamos que unir manualmente los datos de los puntos de venta y Weather Source para poder investigar los problemas de ventas en Hamburgo, pero hemos simplificado enormemente ese proceso gracias a nuestra vista `analytics.daily_city_metrics_v`. \r\n\r\nPon en marcha la siguiente consulta, que muestra cómo hemos simplificado este análisis al convertirlo en una sentencia SELECT en una sola vista.\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-es/6.1.results.png)\r\n\r\n**¡Toma ya!** Si los analistas financieros hubieran podido contar con esta información cuando comenzaron su investigación, no habrían tenido que recurrir a los equipos de datos, ya que toda la información estaba ahí. \r\n\r\nAl completar esta quickstart guide, hemos visto la rapidez con la que podemos obtener valor empresarial real a partir de nuestro trabajo y lo fácil que es utilizar Snowflake Marketplace para obtener información adicional sobre los datos.\r\n\r\n### Paso 2: hacer clic en Next --\u003E\r\n\r\n## Conclusión y siguientes pasos\r\n\r\n### Conclusión\r\n¡Bien hecho! Has completado la quickstart guide De cero a Snowflake de Tasty Bytes sobre colaboración. \r\n\r\nLo que quiere decir que habrás: - accedido a Snowflake Marketplace, - obtenido datos actualizados de Weather Source en tu cuenta, - creado una vista, - creado una función SQL, - utilizado los gráficos de Snowsight para analizar la información de forma visual.\r\n\r\nSi deseas volver a empezar esta quickstart guide, ejecuta las secuencias de comandos de restauración que aparecen en la parte inferior de la hoja de trabajo asociada.\r\n\r\n### Siguientes pasos\r\nSi quieres ampliar tu experiencia con Snowflake Data Cloud, haz clic en el siguiente enlace para descubrir otras quickstart guides Powered by Tasty Bytes.\r\n\r\n- ### [Powered by Tasty Bytes - Índice de las quickstart guides](/es/developers/guides/tasty-bytes-introduction-es/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-bcd8a093bb","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-11d64313ac",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-81cb481036","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-10-04",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7b5b4159c7","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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directo a datos de terceros a través de Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003ECreación de una hoja de trabajo y copia en SQL\u003C/h2\u003E","\u003Ch2\u003EInvestigación de los días sin ventas en los datos de primera mano\u003C/h2\u003E","\u003Ch2\u003EUso de los datos de Weather Source desde Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003EDemocratización de la información de los datos\u003C/h2\u003E","\u003Ch2\u003EObtención de la información a partir de ventas y datos meteorológicos de Marketplace\u003C/h2\u003E","\u003Ch2\u003EConclusión y siguientes 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