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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-6118f112d1","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Tasty Bytes - De cero a Snowflake - Datos semiestructurados"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Jacob Kranzler","quickstartHeroForkRepoLink":{"id":"button-b832b60e6e","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/tasty-bytes-zero-to-snowflake-semi-structured-data-es"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Tasty Bytes - De cero a Snowflake - Datos semiestructurados","url":"https://www.snowflake.com/content/snowflake-site/global/es/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es","currentPage":true},{"title":"Guías para desarrolladores","url":"https://www.snowflake.com/content/snowflake-site/global/es/developers/guides","currentPage":false},{"title":"Snowflake para desarrolladores","url":"https://www.snowflake.com/content/snowflake-site/global/es/developers","currentPage":false}],":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/es/content-fragments/quickstarts/tasty-bytes-zero-to-snowflake-semi-structured-data-es","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""}},"flexible_column_cont":{"id":"flexible-column-container-7042b148d1","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b14ecb501c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d41eb6485c","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EProcesamiento de datos semiestructurados en Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003ETe damos la bienvenida a la quickstart guide Tasty Bytes - De cero a Snowflake centrada en el procesamiento de datos semiestructurados.\u003C/p\u003E\n","\u003Cp\u003EEn esta quickstart guide veremos cómo procesar datos semiestructurados en Snowflake analizando en detalle el tipo de datos VARIANT, el procesamiento de datos semiestructurados con notación de puntos y cómo quitar el formato lateral, así como la creación de vistas y los gráficos de Snowsight.\u003C/p\u003E\n","\u003Cp\u003EPara obtener más información sobre los datos semiestructurados en Snowflake, consulta la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/semistructured-concepts\"\u003Edescripción general de los datos semiestructurados.\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003ERequisitos previos\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAntes de empezar, asegúrate de haber completado la \u003Ca href=\"/es/developers/guides/tasty-bytes-introduction-es/\"\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\u003EQué es el tipo de datos VARIANT\u003C/li\u003E\u003Cli\u003ECómo usar las notación de puntos para consultar datos semiestructurados\u003C/li\u003E\u003Cli\u003ECómo quitar el formato de los datos semiestructurados\u003C/li\u003E\u003Cli\u003ECómo analizar arrays\u003C/li\u003E\u003Cli\u003ECómo crear una vista\u003C/li\u003E\u003Cli\u003ECómo crear un gráfico a partir de un conjunto de resultados en Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECosas que podrás conseguir\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUn proceso integral de datos semiestructurados\u003C/li\u003E\u003Cli\u003EUna vista armonizada (plata) y analítica (oro)\u003C/li\u003E\u003Cli\u003EUn gráfico de barras útil a nivel ejecutivo\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-semi-structured-data-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-semi-structured-data-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-semi-structured-data-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 con (marca de tiempo) y escribe “Tasty Bytes - Datos semiestructurados"\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-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. <button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql\"\u003Etb_zts_semi_structured_data.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\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-semi-structured-data-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\u003EElaboración de perfiles de datos semiestructurados de la carta\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EComo ingeniero de datos de Tasty Bytes, debes elaborar un perfil para los datos de nuestra carta, lo que incluye una columna para datos semiestructurados. A partir de esta tabla con la carta tendrás que generar la vista de una capa analítica que incluya los datos de los ingredientes y alérgenos para los usuarios finales.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: configurar el contexto y la consulta en la tabla\u003C/h3\u003E\n","\u003Cp\u003EPara empezar, ejecutaremos las tres primeras consultas juntas. De esta forma: - Se definirá el contexto del rol como \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E. - Se definirá el contexto del almacén como \u003Ccode\u003Etasty_de_wh\u003C/code\u003E. - Se generará un conjunto con los diez resultados principales mediante el comando \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/top_n\"\u003ETOP\u003C/a\u003E para nuestra tabla \u003Ccode\u003Eraw_pos.menu\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer; \nUSE WAREHOUSE tasty_de_wh;\n\nSELECT TOP 10 \n    m.truck_brand_name, \n    m.menu_type, \n    m.menu_item_name, \n    m.menu_item_health_metrics_obj \nFROM frostbyte_tasty_bytes.raw_pos.menu m; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.1.menu.png\" alt=\"assets/3.1.menu.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEn el resultado, podemos ver que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E debe ser el dato semiestructurado que nos han dicho que contiene las métricas que necesitamos proporcionar posteriormente.\u003C/p\u003E\n","\u003Cp\u003EAl hacer clic en una de las celdas de esta columna, veremos que Snowsight amplía automáticamente los paneles de estadísticas para que veamos mejor los contenidos.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.1.2.stats.png\" alt=\"assets/3.1.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: explorar la columna de datos semiestructurados\u003C/h3\u003E\n","\u003Cp\u003EPara examinar en más profundidad cómo se define esta columna en Snowflake, ejecuta la siguiente consulta en la que usaremos \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/show-columns\"\u003ESHOW COLUMNS\u003C/a\u003E para explorar los tipos de datos presentes en nuestra tabla \u003Ccode\u003Emenu\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.2.show_columns.png\" alt=\"assets/3.2.show_columns.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESi observamos el conjunto de resultados, podemos ver que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E es del tipo de datos \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/data-types-semistructured\"\u003EVARIANT\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EEn el caso de datos mayormente regulares que solo usan tipos nativos del formato semiestructurado en cuestión (como cadenas o enteros en formato JSON), los requisitos de almacenamiento y rendimiento de consultas para las operaciones en datos relacionales y en una columna VARIANT son muy similares.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPaso 3: recorrer datos semiestructurados mediante la notación de puntos\u003C/h3\u003E\n","\u003Cp\u003EEn la columna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E, vemos que se incluyó \u003Ccode\u003Emenu_item_id\u003C/code\u003E junto con los datos más anidados sobre ingredientes y alérgenos a los que necesitamos acceder.\u003C/p\u003E\n","\u003Cp\u003EEjecuta la siguiente consulta en la que usamos la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation\"\u003Enotación de puntos\u003C/a\u003E para recorrer nuestros datos semiestructurados.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.3.dot.png\" alt=\"assets/3.3.dot.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAl usar la notación de puntos podemos extraer \u003Ccode\u003Emenu_item_id\u003C/code\u003E al completo, pero parece que aún quedan objetos semiestructurados adicionales en los resultados de la columna \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EUna vez más, vamos a hacer clic en una de las celdas de esta columna para analizarla en detalle.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.3.2.stats.png\" alt=\"assets/3.3.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E¡Estamos avanzando!\u003C/strong\u003E Veamos cómo podemos procesar más \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E en la siguiente sección, con funciones adicionales de Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPaso 4: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECómo quitar el formato de datos semiestructurados\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EDespués de haber visto cómo podemos consultar fácilmente los datos semiestructurados tal y como aparecen en una columna Variant mediante la notación de puntos, nuestro ingeniero de datos de Tasty ya puede proporcionar los datos solicitados a los colaboradores internos.\u003C/p\u003E\n","\u003Cp\u003EEn esta sección, realizaremos procesamientos adicionales de datos semiestructurados para cumplir algunos de los requisitos.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: familiarizarse con la función FLATTEN\u003C/h3\u003E\n","\u003Cp\u003EPara seguir extrayendo datos, los usuarios posteriores hacen consultas relativas a la columna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E. Ejecuta la siguiente consulta, que usa la funcionalidad de la notación de puntos que acabamos de ver, junto con la función \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/flatten\"\u003EFLATTEN\u003C/a\u003E y la capacidad \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/join-lateral\"\u003ELATERAL JOIN\u003C/a\u003E de Snowflake para ofrecernos el array que hemos solicitado: \u003Ccode\u003Eingredient\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFLATTEN:\u003C/strong\u003E es una función de tabla que selecciona una columna VARIANT, OBJECT o ARRAY y genera una vista lateral. Puede usarse para convertir datos semiestructurados en una representación relacional.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELATERAL JOIN:\u003C/strong\u003E a diferencia de los resultados de las uniones no laterales, el resultado de una unión lateral solo incluye las filas generadas desde la vista de línea. Las filas de la izquierda no necesitan unirse a las de la derecha, ya que las de la izquierda ya se han tenido en cuenta al pasarse a la vista de línea.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.1.lat_flat.png\" alt=\"assets/4.1.lat_flat.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: explorar la función ARRAY\u003C/h3\u003E\n","\u003Cp\u003EAntes de extraer los datos solicitados sobre ingredientes, completa la siguiente consulta que resalta funciones ARRAY de Snowflake y que explorará la columna \u003Ccode\u003Eingredients\u003C/code\u003E para cualquier \u003Ccode\u003Emenu_item_name\u003C/code\u003E que contenga “lechuga” (lettuce) al usar \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/array_contains\"\u003EARRAY_CONTAINS\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:"ingredients"::VARIANT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.2.array_contains.png\" alt=\"assets/4.2.array_contains.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESegún los resultados, vemos que muchos elementos de la carta incluyen lechuga. Este tipo de análisis resultaría extremadamente valioso para los responsables del abastecimiento de la cadena de suministro en caso de que hubiera que retirar alimentos en las ciudades o países donde ofrecemos nuestros productos.\u003C/p\u003E\n","\u003Ch3\u003EPaso 3: estructurar datos semiestructurados a escala\u003C/h3\u003E\n","\u003Cp\u003EAhora que ya hemos visto qué clase de valores podemos proporcionar a nuestra organización, vamos a ejecutar la última consulta de esta sección.\u003C/p\u003E\n","\u003Cp\u003EEsta consulta utiliza la notación de puntos y nuestras uniones laterales combinadas con la función de tabla FLATTEN para proporcionar el conjunto de resultados que debíamos generar inicialmente.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.3.lat_flat_2.png\" alt=\"assets/4.3.lat_flat_2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E¡Genial!\u003C/strong\u003E El resultado parece cumplir con los requisitos exactos de las partes interesadas. En la siguiente sección veremos cómo llevar estos resultados a nuestra capa de analíticas, donde las partes interesadas podrán acceder a ellos.\u003C/p\u003E\n","\u003Ch3\u003EPaso 4: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECreación de vistas estructuradas a partir de datos semiestructurados\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003EEn la sección anterior, hemos utilizado un conjunto de funcionalidades de datos semiestructurados de Snowflake para desarrollar una consulta que proporciona el resultado exacto que necesitan nuestros usuarios finales. A continuación, vamos a seguir el proceso de promoción de esta consulta en nuestra capa sin procesar a través de la capa armonizada y, finalmente, hasta la analítica. Nuestros usuarios poseen privilegios para poder leer esta última capa.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENota:\u003C/strong\u003E Si conoces los modelos de datos bronce, plata y oro, la capa sin procesar sería bronce, la armonizada sería plata y la analítica, oro.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: crear una vista armonizada utilizando nuestro SQL sin formato semiestructurado\u003C/h3\u003E\n","\u003Cp\u003EVamos a utilizar la consulta con la que hemos finalizado la última sección. Ejecuta la siguiente consulta, que contiene tanto este SQL como todas las demás columnas ya estructuradas de la tabla \u003Ccode\u003Emenu\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EEn esta consulta utilizaremos \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E en nuestro esquema armonizado para incluir en una tabla la lógica de procesamiento semiestructurada y las columnas adicionales.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EUna vista permite acceder al resultado de una consulta como si fuera una tabla. Las vistas pueden utilizarse para muchos fines, como combinar, segregar o proteger datos.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\n    AS\nSELECT \n    m.menu_id,\n    m.menu_type_id,\n    m.menu_type,\n    m.truck_brand_name,\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    m.item_category,\n    m.item_subcategory,\n    m.cost_of_goods_usd,\n    m.sale_price_usd,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/5.1.harmonized_view.png\" alt=\"assets/5.1.harmonized_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUna vez creada la vista \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E, podemos realizar una consulta directamente en ella, sin tener que utilizar el SQL más complejo que utilizamos en su momento. Sin embargo, teniendo en cuenta que las partes interesadas acceden a los datos desde la capa de analíticas, hagamos lo siguiente para llegar ahí.\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: promoción de la vista armonizada a la analítica de forma sencilla\u003C/h3\u003E\n","\u003Cp\u003EEjecuta la siguiente consulta haciendo referencia a nuestra \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E. Se creará \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E, que comenzará a aportar valor downstream de inmediato.\u003C/p\u003E\n","\u003Cp\u003EPodemos ver que en esta consulta hay un par de funciones nuevas que todavía no hemos tratado. Por un lado, hemos añadido el comando \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E que se puede ver tanto en \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/show\"\u003ESHOW VIEWS\u003C/a\u003E como dentro de la interfaz de Snowsight para documentar lo que un usuario puede ver al hacer una consulta en esta vista.\u003C/p\u003E\n","\u003Cp\u003EPor otro lado, estamos utilizando los parámetros \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE y RENAME\u003C/a\u003E, que pueden facilitarles mucho el trabajo a los desarrolladores de SQL al reducir la complejidad de las consultas o de la definición de la vista.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEXCLUDE:\u003C/strong\u003E al seleccionar todas las columnas (con SELECT * o SELECT table_name.*), EXCLUDE especifica qué columnas deben ser excluidas de los resultados.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERENAME:\u003C/strong\u003E al seleccionar todas las columnas (con SELECT * o SELECT table_name.*), se especifican los alias de las columnas que deben utilizarse en los resultados.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/5.2.analytics_view.png\" alt=\"assets/5.2.analytics_view.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPaso 3: hacer clic en Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAnálisis de los datos semiestructurados procesados en Snowsight\u003C/h2\u003E\n","\u003Ch3\u003EDescripción general\u003C/h3\u003E\n","\u003Cp\u003ECuando nuestra vista de la carta esté lista en nuestra capa de analíticas, vamos a ejecutar un par de consultas en ella que proporcionaremos a nuestros usuarios finales para mostrar cómo Snowflake impulsa una experiencia de consulta relacional con datos semiestructurados sin tener que hacer copias adicionales ni realizar ningún procesamiento complejo.\u003C/p\u003E\n","\u003Ch3\u003EPaso 1: analizar arrays\u003C/h3\u003E\n","\u003Cp\u003EEn la sección anterior, hemos visto la columna \u003Ccode\u003Eingredients\u003C/code\u003E como resultado de una consulta, pero ahora está disponible sin ningún requisito de notación de puntos en nuestra vista \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ECon ella, ejecuta ahora la siguiente consulta, que utiliza dos funciones de array adicionales de Snowflake, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/array_intersection\"\u003EARRAY_INTERSECTION\u003C/a\u003E y \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/arrays_overlap\"\u003EARRAYS_OVERLAP\u003C/a\u003E, para ver qué elementos de las cartas que no sean bebidas coinciden con nuestras cartas de la línea de food trucks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m1.menu_type,\n    m1.menu_item_name,\n    m2.menu_type AS overlap_menu_type,\n    m2.menu_item_name AS overlap_menu_item_name,\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\n    ON m1.menu_item_id <> m2.menu_item_id -- avoid joining the same menu item to itself\n    AND m1.menu_type <> m2.menu_type \nWHERE 1=1\n    AND m1.item_category <> 'Beverage' -- remove beverages\n    AND m2.item_category <> 'Beverage' -- remove beverages\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\nORDER BY m1.menu_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.1.arrays.png\" alt=\"assets/6.1.arrays.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe nuevo, utilizando la misma vista, hemos desarrollado este tipo de consulta que puede proporcionar valor empresarial real a nuestros operadores de food trucks, mientras trabajan con los responsables de abastecimiento para predecir y pedir los ingredientes que necesiten cada semana.\u003C/p\u003E\n","\u003Ch3\u003EPaso 2: proporcionar métricas a ejecutivos\u003C/h3\u003E\n","\u003Cp\u003EAhora veamos cómo ayudar a los ejecutivos de Tasty Bytes a tomar decisiones sobre la carta basadas en datos. Para ello, proporcionaremos métricas de alto nivel en relación con los ingredientes y alérgenos que estamos teniendo en cuenta en nuestras marcas.\u003C/p\u003E\n","\u003Cp\u003EEjecuta la siguiente consulta, que utiliza las sentencias \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/count\"\u003ECOUNT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/sum\"\u003ESUM\u003C/a\u003E y la condicional \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/case\"\u003ECASE\u003C/a\u003E para agregar las métricas requeridas de \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.2.metrics.png\" alt=\"assets/6.2.metrics.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EY, con los resultados que acabamos de recibir, ya hemos pasado de una tabla sin procesar con datos semiestructurados a una única fila agregada a la que cualquier persona de nuestra organización puede acceder fácilmente para hacer que Tasty Bytes se base más en los datos.\u003C/p\u003E\n","\u003Ch3\u003EPaso 3: convertir los resultados en gráficos\u003C/h3\u003E\n","\u003Cp\u003EAlgunos de los ejecutivos de Tasty Bytes prefieren las representaciones visuales de datos. Por ello, vamos a ver lo sencillo que es convertir los resultados de las tablas en gráficos fáciles de asimilar dentro de Snowsight.\u003C/p\u003E\n","\u003Cp\u003EEjecuta la siguiente consulta, que añade filtros en tres de los nombres de las marcas de food trucks al SQL que hemos ejecutado anteriormente.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    m.brand_name,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\nGROUP BY m.brand_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.3.results.png\" alt=\"assets/6.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPor defecto, Snowsight devuelve los resultados de la consulta en tablas. Sin embargo, hay una potente función de Snowsight que aún no hemos visto: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-visualizations#using-charts\"\u003Eel uso de gráficos\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESigue las flechas de la siguiente captura de pantalla para crear tu propio gráfico de barras que muestre una comparativa de estas tres marcas de food trucks que muestre los elementos de su carta que tienen en cuenta los ingredientes y alérgenos.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.3.chart.png\" alt=\"assets/6.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPara terminar con esta quickstart guide, ya solo nos falta recalcar lo fácil que sería para un ejecutivo de Tasty Bytes hacer esta clase de análisis por su cuenta, sin tener que saber nada sobre el procesamiento de datos semiestructurados que hemos incluido en las vistas desarrolladas. Ahora ya podemos descansar sabiendo que estamos fomentando la democratización de los datos en la organización Tasty Bytes.\u003C/p\u003E\n","\u003Ch3\u003EPaso 4: 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 Tasty Bytes - De cero a Snowflake sobre datos semiestructurados.\u003C/p\u003E\n","\u003Cp\u003ELo que quiere decir que habrás: -aprendido qué es un tipo de datos VARIANT; - usado las notaciones de puntos para consultar datos semiestructurados; -eliminado el formato de los datos semiestructurados; -analizado arrays; -creado dos vistas; -creado un gráfico a partir de un conjunto de resultados en Snowsight.\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 - Datos semiestructurados","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Procesamiento de datos semiestructurados en Snowflake\r\n\r\n### Descripción general\r\nTe damos la bienvenida a la quickstart guide Tasty Bytes - De cero a Snowflake centrada en el procesamiento de datos semiestructurados.\r\n\r\nEn esta quickstart guide veremos cómo procesar datos semiestructurados en Snowflake analizando en detalle el tipo de datos VARIANT, el procesamiento de datos semiestructurados con notación de puntos y cómo quitar el formato lateral, así como la creación de vistas y los gráficos de Snowsight.\r\n\r\nPara obtener más información sobre los datos semiestructurados en Snowflake, consulta la [descripción general de los datos semiestructurados.](https://docs.snowflake.com/en/user-guide/semistructured-concepts)\r\n\r\n### Requisitos previos\r\n- Antes de empezar, asegúrate de haber completado la [**quickstart guide Introducción a Tasty Bytes**](/es/developers/guides/tasty-bytes-introduction-es/), 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- Qué es el tipo de datos VARIANT\r\n- Cómo usar las notación de puntos para consultar datos semiestructurados\r\n- Cómo quitar el formato de los datos semiestructurados\r\n- Cómo analizar arrays\r\n- Cómo crear una vista\r\n- Cómo crear un gráfico a partir de un conjunto de resultados en Snowsight\r\n\r\n### Cosas que podrás conseguir\r\n- Un proceso integral de datos semiestructurados\r\n- Una vista armonizada (plata) y analítica (oro)\r\n- Un gráfico de barras útil a nivel ejecutivo\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-semi-structured-data-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-semi-structured-data-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-semi-structured-data-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 con (marca de tiempo) y escribe “Tasty Bytes - Datos semiestructurados\"\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-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. \u003Cbutton\u003E[tb_zts_semi_structured_data.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql)\u003C/button\u003E\r\n\r\n### 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-semi-structured-data-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## Elaboración de perfiles de datos semiestructurados de la carta\r\n\r\n### Descripción general\r\nComo ingeniero de datos de Tasty Bytes, debes elaborar un perfil para los datos de nuestra carta, lo que incluye una columna para datos semiestructurados. A partir de esta tabla con la carta tendrás que generar la vista de una capa analítica que incluya los datos de los ingredientes y alérgenos para los usuarios finales.\r\n\r\n### Paso 1: configurar el contexto y la consulta en la tabla\r\nPara empezar, ejecutaremos las tres primeras consultas juntas. De esta forma: - Se definirá el contexto del rol como `tasty_data_engineer`. - Se definirá el contexto del almacén como `tasty_de_wh`. - Se generará un conjunto con los diez resultados principales mediante el comando [TOP](https://docs.snowflake.com/en/sql-reference/constructs/top_n) para nuestra tabla `raw_pos.menu`.\r\n\r\n``` \r\nUSE ROLE tasty_data_engineer; \r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT TOP 10 \r\n    m.truck_brand_name, \r\n    m.menu_type, \r\n    m.menu_item_name, \r\n    m.menu_item_health_metrics_obj \r\nFROM frostbyte_tasty_bytes.raw_pos.menu m; \r\n```\r\n\r\n![assets/3.1.menu.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.1.menu.png)\r\n\r\nEn el resultado, podemos ver que `menu_item_health_metrics_obj` debe ser el dato semiestructurado que nos han dicho que contiene las métricas que necesitamos proporcionar posteriormente. \r\n\r\nAl hacer clic en una de las celdas de esta columna, veremos que Snowsight amplía automáticamente los paneles de estadísticas para que veamos mejor los contenidos.\r\n\r\n![assets/3.1.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.1.2.stats.png)\r\n\r\n### Paso 2: explorar la columna de datos semiestructurados\r\nPara examinar en más profundidad cómo se define esta columna en Snowflake, ejecuta la siguiente consulta en la que usaremos [SHOW COLUMNS](https://docs.snowflake.com/en/sql-reference/sql/show-columns) para explorar los tipos de datos presentes en nuestra tabla `menu`.\r\n\r\n```\r\nSHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\r\n```\r\n\r\n![assets/3.2.show_columns.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.2.show_columns.png)\r\n\r\nSi observamos el conjunto de resultados, podemos ver que `menu_item_health_metrics_obj` es del tipo de datos [VARIANT](https://docs.snowflake.com/en/sql-reference/data-types-semistructured).\r\n\r\n\u003E En el caso de datos mayormente regulares que solo usan tipos nativos del formato semiestructurado en cuestión (como cadenas o enteros en formato JSON), los requisitos de almacenamiento y rendimiento de consultas para las operaciones en datos relacionales y en una columna VARIANT son muy similares.\r\n\u003E\r\n\r\n### Paso 3: recorrer datos semiestructurados mediante la notación de puntos\r\nEn la columna `menu_item_health_metrics_obj`, vemos que se incluyó `menu_item_id` junto con los datos más anidados sobre ingredientes y alérgenos a los que necesitamos acceder. \r\n\r\nEjecuta la siguiente consulta en la que usamos la [notación de puntos](https://docs.snowflake.com/en/user-guide/querying-semistructured#dot-notation) para recorrer nuestros datos semiestructurados.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\r\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.3.dot.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.3.dot.png)\r\n\r\nAl usar la notación de puntos podemos extraer `menu_item_id` al completo, pero parece que aún quedan objetos semiestructurados adicionales en los resultados de la columna `menu_item_health_metrics`. \r\n\r\nUna vez más, vamos a hacer clic en una de las celdas de esta columna para analizarla en detalle.\r\n\r\n![assets/3.3.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/3.3.2.stats.png)\r\n\r\n**¡Estamos avanzando!** Veamos cómo podemos procesar más `menu_item_health_metrics` en la siguiente sección, con funciones adicionales de Snowflake.\r\n\r\n### Paso 4: hacer clic en Next --\u003E\r\n\r\n## Cómo quitar el formato de datos semiestructurados\r\n\r\n### Descripción general\r\nDespués de haber visto cómo podemos consultar fácilmente los datos semiestructurados tal y como aparecen en una columna Variant mediante la notación de puntos, nuestro ingeniero de datos de Tasty ya puede proporcionar los datos solicitados a los colaboradores internos.\r\n\r\nEn esta sección, realizaremos procesamientos adicionales de datos semiestructurados para cumplir algunos de los requisitos.\r\n\r\n### Paso 1: familiarizarse con la función FLATTEN\r\nPara seguir extrayendo datos, los usuarios posteriores hacen consultas relativas a la columna `menu_item_health_metrics_obj`. Ejecuta la siguiente consulta, que usa la funcionalidad de la notación de puntos que acabamos de ver, junto con la función [FLATTEN](https://docs.snowflake.com/en/sql-reference/functions/flatten) y la capacidad [LATERAL JOIN](https://docs.snowflake.com/en/sql-reference/constructs/join-lateral) de Snowflake para ofrecernos el array que hemos solicitado: `ingredient`.\r\n\r\n\u003E **FLATTEN:** es una función de tabla que selecciona una columna VARIANT, OBJECT o ARRAY y genera una vista lateral. Puede usarse para convertir datos semiestructurados en una representación relacional.\r\n\u003E\r\n\u003E**LATERAL JOIN:** a diferencia de los resultados de las uniones no laterales, el resultado de una unión lateral solo incluye las filas generadas desde la vista de línea. Las filas de la izquierda no necesitan unirse a las de la derecha, ya que las de la izquierda ya se han tenido en cuenta al pasarse a la vista de línea. \r\n\u003E\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.1.lat_flat.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.1.lat_flat.png)\r\n\r\n### Paso 2: explorar la función ARRAY\r\nAntes de extraer los datos solicitados sobre ingredientes, completa la siguiente consulta que resalta funciones ARRAY de Snowflake y que explorará la columna `ingredients` para cualquier `menu_item_name` que contenga “lechuga” (lettuce) al usar [ARRAY_CONTAINS](https://docs.snowflake.com/en/sql-reference/functions/array_contains).\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\r\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:\"ingredients\"::VARIANT);\r\n```\r\n\r\n![assets/4.2.array_contains.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.2.array_contains.png)\r\n\r\nSegún los resultados, vemos que muchos elementos de la carta incluyen lechuga. Este tipo de análisis resultaría extremadamente valioso para los responsables del abastecimiento de la cadena de suministro en caso de que hubiera que retirar alimentos en las ciudades o países donde ofrecemos nuestros productos.\r\n\r\n### Paso 3: estructurar datos semiestructurados a escala\r\nAhora que ya hemos visto qué clase de valores podemos proporcionar a nuestra organización, vamos a ejecutar la última consulta de esta sección. \r\n\r\nEsta consulta utiliza la notación de puntos y nuestras uniones laterales combinadas con la función de tabla FLATTEN para proporcionar el conjunto de resultados que debíamos generar inicialmente.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.3.lat_flat_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/4.3.lat_flat_2.png)\r\n\r\n**¡Genial!** El resultado parece cumplir con los requisitos exactos de las partes interesadas. En la siguiente sección veremos cómo llevar estos resultados a nuestra capa de analíticas, donde las partes interesadas podrán acceder a ellos.\r\n\r\n### Paso 4: hacer clic en Next --\u003E\r\n\r\n## Creación de vistas estructuradas a partir de datos semiestructurados\r\n\r\n### Descripción general\r\nEn la sección anterior, hemos utilizado un conjunto de funcionalidades de datos semiestructurados de Snowflake para desarrollar una consulta que proporciona el resultado exacto que necesitan nuestros usuarios finales. A continuación, vamos a seguir el proceso de promoción de esta consulta en nuestra capa sin procesar a través de la capa armonizada y, finalmente, hasta la analítica. Nuestros usuarios poseen privilegios para poder leer esta última capa.\r\n\r\n**Nota:** Si conoces los modelos de datos bronce, plata y oro, la capa sin procesar sería bronce, la armonizada sería plata y la analítica, oro.\r\n\r\n### Paso 1: crear una vista armonizada utilizando nuestro SQL sin formato semiestructurado\r\nVamos a utilizar la consulta con la que hemos finalizado la última sección. Ejecuta la siguiente consulta, que contiene tanto este SQL como todas las demás columnas ya estructuradas de la tabla `menu`. \r\n\r\nEn esta consulta utilizaremos [CREATE VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-view) en nuestro esquema armonizado para incluir en una tabla la lógica de procesamiento semiestructurada y las columnas adicionales.\r\n\r\n\u003E Una vista permite acceder al resultado de una consulta como si fuera una tabla. Las vistas pueden utilizarse para muchos fines, como combinar, segregar o proteger datos. \r\n\u003E\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\r\n    AS\r\nSELECT \r\n    m.menu_id,\r\n    m.menu_type_id,\r\n    m.menu_type,\r\n    m.truck_brand_name,\r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    m.item_category,\r\n    m.item_subcategory,\r\n    m.cost_of_goods_usd,\r\n    m.sale_price_usd,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/5.1.harmonized_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/5.1.harmonized_view.png)\r\n\r\nUna vez creada la vista `harmonized.menu_v`, podemos realizar una consulta directamente en ella, sin tener que utilizar el SQL más complejo que utilizamos en su momento. Sin embargo, teniendo en cuenta que las partes interesadas acceden a los datos desde la capa de analíticas, hagamos lo siguiente para llegar ahí.\r\n\r\n### Paso 2: promoción de la vista armonizada a la analítica de forma sencilla\r\nEjecuta la siguiente consulta haciendo referencia a nuestra `harmonized.menu_v`. Se creará `analytics.menu_v`, que comenzará a aportar valor downstream de inmediato. \r\n\r\nPodemos ver que en esta consulta hay un par de funciones nuevas que todavía no hemos tratado. Por un lado, hemos añadido el comando [COMMENT](https://docs.snowflake.com/en/sql-reference/sql/comment) que se puede ver tanto en [SHOW VIEWS](https://docs.snowflake.com/en/sql-reference/sql/show) como dentro de la interfaz de Snowsight para documentar lo que un usuario puede ver al hacer una consulta en esta vista.\r\n\r\nPor otro lado, estamos utilizando los parámetros [SELECT * EXCLUDE y RENAME](https://docs.snowflake.com/en/sql-reference/sql/select#parameters), que pueden facilitarles mucho el trabajo a los desarrolladores de SQL al reducir la complejidad de las consultas o de la definición de la vista.\r\n\r\n\u003E **EXCLUDE:** al seleccionar todas las columnas (con SELECT * o SELECT table_name.*), EXCLUDE especifica qué columnas deben ser excluidas de los resultados.\r\n\u003E\r\n\u003E **RENAME:** al seleccionar todas las columnas (con SELECT * o SELECT table_name.*), se especifican los alias de las columnas que deben utilizarse en los resultados.\r\n\u003E\r\n\r\n![assets/5.2.analytics_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/5.2.analytics_view.png)\r\n\r\n### Paso 3: hacer clic en Next --\u003E\r\n\r\n## Análisis de los datos semiestructurados procesados en Snowsight\r\n\r\n### Descripción general\r\nCuando nuestra vista de la carta esté lista en nuestra capa de analíticas, vamos a ejecutar un par de consultas en ella que proporcionaremos a nuestros usuarios finales para mostrar cómo Snowflake impulsa una experiencia de consulta relacional con datos semiestructurados sin tener que hacer copias adicionales ni realizar ningún procesamiento complejo.\r\n\r\n### Paso 1: analizar arrays\r\nEn la sección anterior, hemos visto la columna `ingredients` como resultado de una consulta, pero ahora está disponible sin ningún requisito de notación de puntos en nuestra vista `analytics.menu_v`. \r\n\r\nCon ella, ejecuta ahora la siguiente consulta, que utiliza dos funciones de array adicionales de Snowflake, [ARRAY_INTERSECTION](https://docs.snowflake.com/en/sql-reference/functions/array_intersection) y [ARRAYS_OVERLAP](https://docs.snowflake.com/en/sql-reference/functions/arrays_overlap), para ver qué elementos de las cartas que no sean bebidas coinciden con nuestras cartas de la línea de food trucks.\r\n\r\n```\r\nSELECT \r\n    m1.menu_type,\r\n    m1.menu_item_name,\r\n    m2.menu_type AS overlap_menu_type,\r\n    m2.menu_item_name AS overlap_menu_item_name,\r\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\r\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\r\n    ON m1.menu_item_id \u003C\u003E m2.menu_item_id -- avoid joining the same menu item to itself\r\n    AND m1.menu_type \u003C\u003E m2.menu_type \r\nWHERE 1=1\r\n    AND m1.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND m2.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\r\nORDER BY m1.menu_type;\r\n```\r\n\r\n![assets/6.1.arrays.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.1.arrays.png)\r\n\r\nDe nuevo, utilizando la misma vista, hemos desarrollado este tipo de consulta que puede proporcionar valor empresarial real a nuestros operadores de food trucks, mientras trabajan con los responsables de abastecimiento para predecir y pedir los ingredientes que necesiten cada semana.\r\n\r\n### Paso 2: proporcionar métricas a ejecutivos\r\nAhora veamos cómo ayudar a los ejecutivos de Tasty Bytes a tomar decisiones sobre la carta basadas en datos. Para ello, proporcionaremos métricas de alto nivel en relación con los ingredientes y alérgenos que estamos teniendo en cuenta en nuestras marcas.\r\n\r\nEjecuta la siguiente consulta, que utiliza las sentencias [COUNT](https://docs.snowflake.com/en/sql-reference/functions/count), [SUM](https://docs.snowflake.com/en/sql-reference/functions/sum) y la condicional [CASE](https://docs.snowflake.com/en/sql-reference/functions/case) para agregar las métricas requeridas de `analytics.menu_v`.\r\n\r\n```\r\nSELECT\r\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\r\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\r\n```\r\n\r\n![assets/6.2.metrics.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.2.metrics.png)\r\n\r\nY, con los resultados que acabamos de recibir, ya hemos pasado de una tabla sin procesar con datos semiestructurados a una única fila agregada a la que cualquier persona de nuestra organización puede acceder fácilmente para hacer que Tasty Bytes se base más en los datos. \r\n\r\n\r\n### Paso 3: convertir los resultados en gráficos\r\nAlgunos de los ejecutivos de Tasty Bytes prefieren las representaciones visuales de datos. Por ello, vamos a ver lo sencillo que es convertir los resultados de las tablas en gráficos fáciles de asimilar dentro de Snowsight. \r\n\r\nEjecuta la siguiente consulta, que añade filtros en tres de los nombres de las marcas de food trucks al SQL que hemos ejecutado anteriormente.\r\n\r\n```\r\nSELECT\r\n    m.brand_name,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m\r\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\r\nGROUP BY m.brand_name;\r\n```\r\n\r\n![assets/6.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.3.results.png)\r\n\r\nPor defecto, Snowsight devuelve los resultados de la consulta en tablas. Sin embargo, hay una potente función de Snowsight que aún no hemos visto: [el uso de gráficos](https://docs.snowflake.com/en/user-guide/ui-snowsight-visualizations#using-charts).\r\n\r\nSigue las flechas de la siguiente captura de pantalla para crear tu propio gráfico de barras que muestre una comparativa de estas tres marcas de food trucks que muestre los elementos de su carta que tienen en cuenta los ingredientes y alérgenos.\r\n\r\n![assets/6.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-es/6.3.chart.png)\r\n\r\nPara terminar con esta quickstart guide, ya solo nos falta recalcar lo fácil que sería para un ejecutivo de Tasty Bytes hacer esta clase de análisis por su cuenta, sin tener que saber nada sobre el procesamiento de datos semiestructurados que hemos incluido en las vistas desarrolladas. Ahora ya podemos descansar sabiendo que estamos fomentando la democratización de los datos en la organización Tasty Bytes.\r\n\r\n### Paso 4: 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 Tasty Bytes - De cero a Snowflake sobre datos semiestructurados. \r\n\r\nLo que quiere decir que habrás: -aprendido qué es un tipo de datos VARIANT; - usado las notaciones de puntos para consultar datos semiestructurados; -eliminado el formato de los datos semiestructurados; -analizado arrays; -creado dos vistas; -creado un gráfico a partir de un conjunto de resultados en Snowsight.\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"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-02374508ef","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-0af0412810",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c4beb607d2","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-fb47751504","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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