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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 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asignación de presupuestos de publicidad.\u003C/p\u003E\n","\u003Cp\u003EA continuación, encontrarás un resumen de lo que podrás aprender en cada paso siguiendo esta quickstart guide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConfiguración del entorno\u003C/strong\u003E: usa las fases y las tablas para la ingesta de datos sin procesar de S3 en Snowflake y su organización.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIngeniería de datos\u003C/strong\u003E: aprovecha DataFrames de Snowpark para Python para realizar transformaciones de datos como, por ejemplo, agruparlos, agregarlos, dinamizarlos y unirlos. Así, prepararás los datos para las aplicaciones downstream.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFlujos de datos\u003C/strong\u003E: utiliza Snowflake Tasks para convertir el código de los flujos de datos en flujos operativos con supervisión integrada.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAprendizaje automático\u003C/strong\u003E: prepara los datos y entrena los modelos de aprendizaje automático (machine learning, ML) en Snowflake con Snowpark ML e implementa el modelo como una función definida por el usuario (user-defined-function, UDF) de Snowpark.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAplicación de Streamlit\u003C/strong\u003E: crea una aplicación interactiva usando Python (sin necesidad de tener experiencia en desarrollo web) para visualizar el retorno de la inversión (ROI) de diferentes presupuestos de gasto en publicidad.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EEn caso de que algunas de las tecnologías mencionadas anteriormente sean nuevas para ti, hemos preparado un breve resumen con enlaces a la documentación.\u003C/p\u003E\n","\u003Ch3\u003E¿Qué es Snowpark?\u003C/h3\u003E\n","\u003Cp\u003EEl conjunto de bibliotecas y tiempos de ejecución de Snowflake para implementar y procesar de forma segura código que no sea SQL, como Python, Java o Scala.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBibliotecas conocidas del cliente\u003C/strong\u003E: Snowpark ofrece una programación completamente integrada de estilo DataFrame y API compatibles con OSS para los lenguajes que los profesionales de los datos prefieran. También incluye la Snowpark ML API para conseguir un modelado de ML (en vista previa pública) y unas operaciones de ML (en vista previa privada) más eficientes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEstructuras de tiempo de ejecución flexibles\u003C/strong\u003E: Snowpark proporciona constructos de tiempo de ejecución flexibles que permiten a los usuarios introducir y ejecutar la lógica personalizada. Los desarrolladores pueden crear flujos de datos, modelos de ML y aplicaciones de datos sin problemas gracias a las UDF y mediante procedimientos almacenados.\u003C/p\u003E\n","\u003Cp\u003EObtén más información sobre \u003Ca href=\"/es/data-cloud/snowpark/\"\u003ESnowpark\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E¿Qué es Snowpark ML?\u003C/h3\u003E\n","\u003Cp\u003ESnowpark ML es una nueva biblioteca para lograr un desarrollo integral más rápido e intuitivo de ML en Snowflake. Snowpark ML tiene 2 API: Snowpark ML Modeling (en vista previa pública) para desarrollar modelos y Snowpark ML Operations (en vista previa privada) para implementarlos.\u003C/p\u003E\n","\u003Cp\u003EEsta quickstart guide se centrará en la Snowpark ML Modeling API, que escala horizontalmente la ingeniería de funciones y simplifica la ejecución del entrenamiento de ML en Snowflake.\u003C/p\u003E\n","\u003Ch3\u003E¿Qué es Streamlit?\u003C/h3\u003E\n","\u003Cp\u003EStreamlit es un marco de aplicación de lenguaje Python puro \u003Ca href=\"https://github.com/streamlit/streamlit\"\u003Ede código abierto\u003C/a\u003E que permite a los desarrolladores escribir, compartir e implementar aplicaciones de datos de forma rápida y sencilla. Más información sobre \u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EDescubrirás\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003Ecómo analizar datos y realizar tareas de ingeniería de datos utilizando DataFrames y las API de Snowpark;\u003C/li\u003E\u003Cli\u003Ecómo utilizar bibliotecas de Python de código abierto del canal curado de Snowflake Anaconda;\u003C/li\u003E\u003Cli\u003Ecómo entrenar modelos de ML con Snowpark ML en Snowflake;\u003C/li\u003E\u003Cli\u003Ecómo crear UDF escalares y vectorizadas de Snowpark para Python para la inferencia en línea y sin conexión, respectivamente;\u003C/li\u003E\u003Cli\u003Ecómo crear Snowflake Tasks para automatizar flujos de datos; y\u003C/li\u003E\u003Cli\u003Ecómo crear una aplicación web con Streamlit que use UDF escalares para la inferencia en función de lo que introduzca el usuario.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERequisitos previos\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/es/v2/Inicio---Sobre-el-Control-de-Versiones-Instalaci%C3%B3n-de-Git\"\u003EGit\u003C/a\u003E debe estar instalado.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.9\u003C/a\u003E debe estar instalado.\n\u003Cul\u003E\u003Cli\u003ETen en cuenta que vas a crear un entorno Python con la versión 3.9 en el paso \u003Cstrong\u003EIntroducción\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EDebes disponer de una cuenta de Snowflake con \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003Epaquetes de Anaconda habilitados por ORGADMIN\u003C/a\u003E. Si no tienes una, puedes registrarte para obtener una \u003Ca href=\"https://signup.snowflake.com/\"\u003Ecuenta de prueba gratuita\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EDebes iniciar sesión en una cuenta de Snowflake con rol ACCOUNTADMIN. Si tienes este rol en tu entorno, selecciónalo para usarlo. En el caso contrario, deberás: 1) registrarte para obtener una prueba gratuita; 2) utilizar un rol diferente que permita crear bases de datos, esquemas, tablas, fases, tareas, funciones definidas por el usuario y procedimientos almacenados, o 3) utilizar una base de datos y un esquema existentes en los que puedas crear los objetos mencionados.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Antes de comenzar, asegúrate de tener una cuenta de Snowflake con paquetes de Anaconda habilitados por ORGADMIN, tal y como se describe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started\"\u003Eaquí\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfiguración del entorno\u003C/h2\u003E\n","\u003Ch3\u003ECreación de tablas, carga de datos y configuración de fases\u003C/h3\u003E\n","\u003Cp\u003EInicia sesión en \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E con tus credenciales para crear tablas, cargar datos de Amazon S3 y configurar las fases internas de Snowflake.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ESi utilizas nombres diferentes para los objetos creados en esta sección, asegúrate de adaptar las secuencias de comandos y el código de las siguientes secciones en consecuencia.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPara cada bloque de secuencias de comandos de SQL que se muestra a continuación, selecciona todas las sentencias del bloque y ejecútalas de arriba a abajo.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EEjecuta los siguientes comandos SQL para crear el \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html\"\u003Ealmacén\u003C/a\u003E, la \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-database.html\"\u003Ebase de datos\u003C/a\u003E y el \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-schema.html\"\u003Eesquema\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE DASH_L; \nCREATE OR REPLACE DATABASE DASH_DB; \nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\n\nUSE DASH_DB.DASH_SCHEMA; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEjecuta los siguientes comandos SQL para crear la tabla \u003Cstrong\u003ECAMPAIGN_SPEND\u003C/strong\u003E con datos alojados en un cubo de S3 de acceso público.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE file format csvformat \n  skip_header = 1 \n  type = 'CSV';\n\nCREATE or REPLACE stage campaign_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\n\nCREATE or REPLACE TABLE CAMPAIGN_SPEND ( \n  CAMPAIGN VARCHAR(60), \n  CHANNEL VARCHAR(60), \n  DATE DATE, \n  TOTAL_CLICKS NUMBER(38,0), \n  TOTAL_COST NUMBER(38,0), \n  ADS_SERVED NUMBER(38,0) \n);\n\nCOPY into CAMPAIGN_SPEND \n  from @campaign_data_stage; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEjecuta los siguientes comandos SQL para crear la tabla \u003Cstrong\u003EMONTHLY_REVENUE\u003C/strong\u003E con datos alojados en un cubo de S3 de acceso público.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE stage monthly_revenue_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\n\nCREATE or REPLACE TABLE MONTHLY_REVENUE ( \n  YEAR NUMBER(38,0), \n  MONTH NUMBER(38,0), \n  REVENUE FLOAT \n);\n\nCOPY into MONTHLY_REVENUE \n  from @monthly_revenue_data_stage; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEjecuta los siguientes comandos SQL para crear la tabla \u003Cstrong\u003EBUDGET_ALLOCATIONS_AND_ROI\u003C/strong\u003E, que aloja las asignaciones de presupuesto y ROI de los últimos seis meses.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI ( \n  MONTH varchar(30), \n  SEARCHENGINE integer, \n  SOCIALMEDIA integer, \n  VIDEO integer, \n  EMAIL integer, \n  ROI float \n)\nCOMMENT = '{"origin":"sf_sit-is", "name":"aiml_notebooks_ad_spend_roi", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"streamlit"}}';\n\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI) VALUES \n('January',35,50,35,85,8.22), \n('February',75,50,35,85,13.90), \n('March',15,50,35,15,7.34), \n('April',25,80,40,90,13.23), \n('May',95,95,10,95,6.246), \n('June',35,50,35,85,8.22); \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEjecuta los siguientes comandos para crear \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage\"\u003Efases internas\u003C/a\u003E de Snowflake y almacenar procedimientos almacenados, UDF y archivos de modelos de ML.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE dash_sprocs;\nCREATE OR REPLACE STAGE dash_models;\nCREATE OR REPLACE STAGE dash_udfs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDe manera opcional, también puedes abrir \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql\"\u003Esetup.sql\u003C/a\u003E en Snowsight y ejecutar todas las sentencias de SQL para crear los objetos y cargar los datos de AWS S3.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Si utilizas nombres diferentes para los objetos creados en esta sección, asegúrate de adaptar las secuencias de comandos y el código de las siguientes secciones en consecuencia.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntroducción\u003C/h2\u003E\n","\u003Cp\u003EEsta sección incluye cómo clonar un repositorio de GitHub y cómo configurar tu entorno de Snowpark para Python.\u003C/p\u003E\n","\u003Ch3\u003EClonación del repositorio de GitHub\u003C/h3\u003E\n","\u003Cp\u003EEl primer paso es clonar el \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003Erepositorio de GitHub\u003C/a\u003E, que contiene todo el código que necesitarás para completar esta quickstart guide.\u003C/p\u003E\n","\u003Cp\u003ECon HTTPS:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EO con SSH:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowpark para Python\u003C/h3\u003E\n","\u003Cp\u003EPara completar los pasos \u003Cstrong\u003EIngeniería de datos\u003C/strong\u003E y \u003Cstrong\u003EAprendizaje automático\u003C/strong\u003E, puedes instalar todo en el entorno local (opción 1) o utilizar Hex (opción 2), tal y como se describe a continuación.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Para ejecutar la \u003Cstrong\u003Eaplicación de Streamlit\u003C/strong\u003E, tendrás que crear un entorno de Python e instalar Snowpark para Python en el entorno local junto con otras bibliotecas, tal y como se describe en \u003Cstrong\u003EInstalación local\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOpción 1: Instalación local\u003C/h4\u003E\n","\u003Cp\u003ECon esta opción podrás completar todos los pasos de la quickstart guide.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 1:\u003C/strong\u003E Descargar e instalar el instalador de Miniconda de \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003E. \u003Cem\u003E(O, si lo prefieres, puedes usar cualquier otro entorno de Python con Python 3.9, como \u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003E)\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 2:\u003C/strong\u003E Abrir una nueva ventana del terminal y ejecutar los siguientes comandos en ella.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 3:\u003C/strong\u003E Crear un entorno conda de Python 3.9 con el nombre \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E mediante la ejecución del siguiente comando en la misma ventana del terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 4:\u003C/strong\u003E Activar el entorno conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E ejecutando el siguiente comando en la misma ventana del terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda activate snowpark-de-ml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 5:\u003C/strong\u003E Instalar Snowpark para Python junto con las demás librerías en el entorno conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E del \u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake/\"\u003Ecanal de Snowflake Anaconda\u003C/a\u003E ejecutando el siguiente comando en la ventana del terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 6:\u003C/strong\u003E Instalar la biblioteca Streamlit en el entorno conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E ejecutando el siguiente comando en la misma ventana del terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install streamlit\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 7:\u003C/strong\u003E Instalar la biblioteca Snowpark ML en el entorno conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E ejecutando el siguiente comando en la misma ventana del terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install snowflake-ml-python\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPaso 9:\u003C/strong\u003E Actualizar \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E con los detalles y las credenciales de tu cuenta de Snowflake.\u003C/p\u003E\n","\u003Cp\u003EA continuación, puedes ver un ejemplo de \u003Cem\u003E\u003Cstrong\u003Econnection.json\u003C/strong\u003E\u003C/em\u003E basado en los nombres de los objetos mencionados en el paso \u003Cstrong\u003EEntorno de configuración\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "account"   : "<your_account_identifier_goes_here>",\n  "user"      : "<your_username_goes_here>",\n  "password"  : "<your_password_goes_here>",\n  "role"      : "ACCOUNTADMIN",\n  "warehouse" : "DASH_L",\n  "database"  : "DASH_DB",\n  "schema"    : "DASH_SCHEMA"\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Nota: Para el parámetro \u003Cstrong\u003Eaccount\u003C/strong\u003E que se menciona arriba, especifica tu \u003Cstrong\u003Eidentificador de cuenta\u003C/strong\u003E y no incluyas el nombre del dominio snowflakecomputing.com. Snowflake lo añade de forma automática al crear la conexión. Para obtener más información al respecto, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html\"\u003Econsulta la documentación\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOpción 2: Hex\u003C/h4\u003E\n","\u003Cp\u003ESi decides usar tu cuenta de \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E o \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecrear una cuenta de prueba gratuita de 30 días\u003C/a\u003E, Snowpark para Python está integrado para que no tengas que crear un entorno de Python ni instalarlo localmente en tu equipo junto con el resto de las bibliotecas. De esta forma, podrás completar los pasos \u003Cstrong\u003EIngeniería de datos\u003C/strong\u003E y \u003Cstrong\u003EAprendizaje automático\u003C/strong\u003E de esta quickstart guide directamente en Hex. (Consulta los pasos correspondientes para obtener más información sobre cómo cargar los cuadernos de ingeniería de datos y ML en Hex).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Para ejecutar la \u003Cstrong\u003Eaplicación de Streamlit\u003C/strong\u003E, tendrás que crear un entorno Python e instalar Snowpark para Python en el entorno local junto con otras bibliotecas, tal y como se ha descrito en \u003Cstrong\u003EInstalación local\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIngeniería de datos\u003C/h2\u003E\n","\u003Cp\u003EEncontrarás a continuación el enlace a este cuaderno que incluye las siguientes tareas de ingeniería de datos:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEstablecer una conexión segura entre Snowpark para Python y Snowflake.\u003C/li\u003E\u003Cli\u003ECargar datos de tablas de Snowflake en DataFrames de Snowpark.\u003C/li\u003E\u003Cli\u003ERealizar análisis de datos de exploración en DataFrames de Snowpark.\u003C/li\u003E\u003Cli\u003EDinamizar y unir datos de varias tablas con DataFrames de Snowpark.\u003C/li\u003E\u003Cli\u003EAutomatizar tareas de flujos de datos con Snowflake Tasks.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECuaderno de ingeniería de datos en Jupyter o en Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPara comenzar, sigue estos pasos:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EEn una ventana del terminal, navega hasta esta carpeta y ejecuta \u003Ccode\u003Ejupyter notebook\u003C/code\u003E en la línea de comandos. (También puedes usar otras herramientas y entornos de desarrollo integrado[integrated development environment, IDE] como Visual Studio Code).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAbre y ejecuta las celdas de \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Asegúrate de que el kernel (de Python) del cuaderno de Jupyter está configurado como \u003Cem\u003E\u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E\u003C/em\u003E, es decir, con el mismo nombre del entorno creado en el paso \u003Cstrong\u003EClonación del repositorio de GitHub\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECuaderno de ingeniería de datos en Hex\u003C/h3\u003E\n","\u003Cp\u003ESi decides usar tu cuenta de \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E o \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecrear una cuenta de prueba gratuita de 30 días\u003C/a\u003E, sigue estos pasos para cargar el cuaderno y crear una conexión de datos con el fin de conectarte a Snowflake desde Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImporta \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003E como un proyecto en tu cuenta. Para obtener más información sobre la importación, consulta la \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentación\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA continuación, en lugar de usar \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E para conectarte a Snowflake, crea una \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econexión de datos\u003C/a\u003E y utilízala en el cuaderno de ingeniería de datos tal y como se muestra a continuación:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/hex_data_connection.png\" alt=\"Conexión de datos de HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Nota: También puedes crear conexiones de datos compartidos para tus proyectos y usuarios en tu espacio de trabajo. Para obtener más información al respecto, consulta la \u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Edocumentación\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ESustituye el siguiente fragmento de código en el cuaderno\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Epor…\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFlujos de datos\u003C/h2\u003E\n","\u003Cp\u003ETambién puedes hacer que las transformaciones de datos estén operativas en forma de flujos de datos automatizados que se ejecutan en Snowflake.\u003C/p\u003E\n","\u003Cp\u003EEn concreto, en el \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003Ecuaderno de ingeniería de datos\u003C/a\u003E, hay una sección en la que se muestra cómo se pueden crear y ejecutar las transformaciones de datos como con \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflake Tasks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EA modo de referencia, echa un vistazo a los fragmentos de código que se muestran a continuación.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ETarea raíz/principal\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EEsta tarea automatiza la carga de datos de los gastos de la campaña y la realización de varias transformaciones.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef campaign_spend_data_pipeline(session: Session) -> str: \n  # DATA TRANSFORMATIONS \n  # Perform the following actions to transform the data\n\n  # Load the campaign spend data \n  snow_df_spend_t = session.table('campaign_spend')\n\n  # Transform the data so we can see total cost per year/month per channel using group_by() and agg() Snowpark DataFrame functions\n   snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\n      with_column_renamed('"YEAR(DATE)"',"YEAR").with_column_renamed('"MONTH(DATE)"',"MONTH").sort('YEAR','MONTH')\n\n  # Transform the data so that each row will represent total cost across all channels per year/month using pivot() and sum() Snowpark DataFrame functions \n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH') \n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select( \n      col("YEAR"), \n      col("MONTH"), \n      col("'search_engine'").as_("SEARCH_ENGINE"), \n      col("'social_media'").as_("SOCIAL_MEDIA"), \n      col("'video'").as_("VIDEO"), \n      col("'email'").as_("EMAIL") \n  )\n\n  # Save transformed data \n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\n\n# Register data pipelining function as a Stored Procedure so it can be run as a task\nsession.sproc.register( \n  func=campaign_spend_data_pipeline, \n  name="campaign_spend_data_pipeline", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location="@dash_sprocs", \n  replace=True)\n\ncampaign_spend_data_pipeline_task = """ \nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task \n    WAREHOUSE = 'DASH_L' \n    SCHEDULE = '3 MINUTE' \nAS \n    CALL campaign_spend_data_pipeline() \n""" \nsession.sql(campaign_spend_data_pipeline_task).collect() \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ETarea secundaria/dependiente\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EEsta tarea automatiza la carga de datos de los ingresos mensuales, la realización de varias transformaciones y su unión con los datos de los gastos de la campaña.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef monthly_revenue_data_pipeline(session: Session) -> str: \n  # Load revenue table and transform the data into revenue per year/month using group_by and agg() functions \n  snow_df_spend_per_month_t = session.table('spend_per_month') \n  snow_df_revenue_t = session.table('monthly_revenue') \n  snow_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\n\n  # Join revenue data with the transformed campaign spend data so that our input features (i.e. cost per channel) and target variable (i.e. revenue) can be loaded into a single table for model training \n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, ["YEAR","MONTH"])\n\n  # SAVE in a new table for the next task \n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\n\n# Register data pipelining function as a Stored Procedure so it can be run as a task\nsession.sproc.register( \n  func=monthly_revenue_data_pipeline, \n  name="monthly_revenue_data_pipeline", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location="@dash_sprocs", \n  replace=True)\n\nmonthly_revenue_data_pipeline_task = """ \n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task \n      WAREHOUSE = 'DASH_L' \n      AFTER campaign_spend_data_pipeline_task \n  AS \n      CALL monthly_revenue_data_pipeline() \n  """ \nsession.sql(monthly_revenue_data_pipeline_task).collect() \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Nota: Observa arriba, en \u003Cem\u003E\u003Cstrong\u003Emonthly_revenue_data_pipeline_task\u003C/strong\u003E\u003C/em\u003E, que la cláusula \u003Cstrong\u003EAFTER campaign_spend_data_pipeline_task\u003C/strong\u003E hace que sea una tarea dependiente.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EInicio de Tasks\u003C/h4\u003E\n","\u003Cp\u003ESnowflake Tasks no se inicia por defecto. Debes ejecutar la siguiente sentencia para iniciar la función o reanudarla.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task monthly_revenue_data_pipeline_task resume").collect()\nsession.sql("alter task campaign_spend_data_pipeline_task resume").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ESuspender tareas\u003C/h4\u003E\n","\u003Cp\u003ESi reanudas las tareas anteriores, suspéndelas para evitar que se usen recursos innecesariamente. Para ello, ejecuta los siguientes comandos.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EObservabilidad de tareas\u003C/h3\u003E\n","\u003Cp\u003EEstas tareas y sus \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro#label-task-dag\"\u003Egrafos acíclicos dirigidos (directed acyclic graphs, DAG)\u003C/a\u003E se pueden ver en \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs\"\u003ESnowsight\u003C/a\u003E, como se muestra a continuación.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowflake_tasks.png\" alt=\"Observabilidad de tareas\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ENotificaciones de error de las tareas\u003C/h3\u003E\n","\u003Cp\u003ETambién puedes habilitar el envío de notificaciones push a un servicio de mensajería en la nube cuando se produzca algún error en la ejecución de las tareas. Para obtener más información, consulta la \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-errors\"\u003Edocumentación\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAprendizaje automático\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative SE REQUIERE haber completado correctamente los pasos de ingeniería de datos que se describen en \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EEn el enlace del cuaderno que encontrarás a continuación incluye las siguientes tareas de aprendizaje automático:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEstablecer una conexión segura entre Snowpark para Python y Snowflake.\u003C/li\u003E\u003Cli\u003ECargar funciones y destinos de Snowflake en DataFrames de Snowpark.\u003C/li\u003E\u003Cli\u003EPreparar las funciones para el entrenamiento de modelos.\u003C/li\u003E\u003Cli\u003EEntrenar modelos de ML con Snowpark ML en Snowflake.\u003C/li\u003E\u003Cli\u003ECrear \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs\"\u003EUDF\u003C/a\u003E escalares y vectorizadas (por lotes) de Python para inferir nuevos puntos de datos tanto en línea como sin conexión, respectivamente.\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowpark_e2e_ml.png\" alt=\"ML integral\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECuaderno de aprendizaje automático en Jupyter o Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPara comenzar, sigue estos pasos:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EEn una ventana del terminal, navega hasta esta carpeta y ejecuta \u003Ccode\u003Ejupyter notebook\u003C/code\u003E en la línea de comandos. (También puedes usar otras herramientas e IDE como Visual Studio Code).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAbre y ejecuta\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: Asegúrate de que el kernel (de Python) del cuaderno de Jupyter está configurado como \u003Cem\u003E\u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E\u003C/em\u003E, es decir, con el mismo nombre del entorno creado en el paso \u003Cstrong\u003EClonación del repositorio de GitHub\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECuaderno de aprendizaje automático en Hex\u003C/h3\u003E\n","\u003Cp\u003ESi decides usar tu cuenta de \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E o \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecrear una cuenta de prueba gratuita de 30 días\u003C/a\u003E, sigue estos pasos para cargar el cuaderno y crear una conexión de datos con el fin de conectarte a Snowflake desde Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImporta \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003E como un proyecto en tu cuenta. Para obtener más información sobre la importación, consulta la \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentación\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA continuación, en lugar de usar \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E para conectarte a Snowflake, crea una \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econexión de datos\u003C/a\u003E y utilízala en el cuaderno de aprendizaje automático tal y como se muestra a continuación:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/hex_data_connection.png\" alt=\"Conexión de datos de HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Nota: También puedes crear conexiones de datos compartidos para tus proyectos y usuarios en tu espacio de trabajo. Para obtener más información al respecto, consulta la \u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Edocumentación\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ESustituye el siguiente fragmento de código en el cuaderno\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Epor…\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAplicación de Streamlit\u003C/h2\u003E\n","\u003Ch3\u003EEjecución de la aplicación de Streamlit en el entorno local\u003C/h3\u003E\n","\u003Cp\u003EEn una ventana del terminal, ve a esta carpeta y ejecuta el siguiente comando para ejecutar la aplicación de Streamlit \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py\"\u003ESnowpark_Streamlit_Revenue_Prediction.py\u003C/a\u003E localmente en tu ordenador.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Estreamlit run Snowpark_Streamlit_Revenue_Prediction.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESi todo ha salido bien, deberías ver cómo se abre una ventana del navegador con la aplicación ya cargada, tal y como se muestra a continuación.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/app.png\" alt=\"Aplicación de Streamlit\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EEjecución de la aplicación de Streamlit en Snowflake: Streamlit en Snowflake\u003C/h3\u003E\n","\u003Cp\u003ESi has habilitado Streamlit en Snowflake (Streamlit-in-Snowflake, SiS) en tu cuenta, sigue estos pasos para ejecutar la aplicación en Snowsight en lugar de localmente en tu ordenador.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative IMPORTANTE: SiS se encuentra en vista previa privada a fecha de junio de 2023.***\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003EHaz clic en \u003Cstrong\u003EStreamlit Apps\u003C/strong\u003E en el menú de navegación de la izquierda.\u003C/li\u003E\u003Cli\u003EHaz clic en \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E en la parte superior derecha.\u003C/li\u003E\u003Cli\u003EIntroduce el nombre de la aplicación en \u003Cstrong\u003EApp name\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelecciona \u003Cstrong\u003EWarehouse\u003C/strong\u003E y \u003Cstrong\u003EApp location\u003C/strong\u003E (base de datos y esquema) donde desees crear la aplicación de Streamlit.\u003C/li\u003E\u003Cli\u003EHaz clic en \u003Cstrong\u003ECreate\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ELlegado a este punto, se te proporcionará el código para crear una aplicación de Streamlit de ejemplo. Abre \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py\"\u003ESnowpark_Streamlit_Revenue_Prediction_SiS.py\u003C/a\u003E y copia el código. Pégalo a continuación en la aplicación de Streamlit de ejemplo.\u003C/li\u003E\u003Cli\u003EHaz clic en \u003Cstrong\u003ERun\u003C/strong\u003E en la parte superior derecha para ejecutar la aplicación.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ESi todo ha salido bien, deberías ver la aplicación en Snowsight, tal y como se muestra a continuación.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/app_sis.png\" alt=\"Streamlit en Snowflake\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EGuardar datos en Snowflake\u003C/h3\u003E\n","\u003Cp\u003EAjusta el control deslizante del presupuesto de publicidad en las dos aplicaciones para ver el ROI previsto para esas asignaciones. También puedes hacer clic en el botón \u003Cstrong\u003ESave to Snowflake\u003C/strong\u003E para guardar las asignaciones de ROI actuales y previstas en la tabla de Snowflake BUDGET_ALLOCATIONS_AND_ROI.\u003C/p\u003E\n","\u003Ch3\u003EDiferencias entre las dos aplicaciones de Streamlit\u003C/h3\u003E\n","\u003Cp\u003ELa principal diferencia entre la ejecución de la aplicación de Streamlit en un entorno local y en Snowflake (SiS) es la manera en la que se crea y se accede al objeto de sesión.\u003C/p\u003E\n","\u003Cp\u003EAl ejecutarla en un entorno local, se crearía y se accedería al nuevo objeto de sesión de la siguiente manera:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Function to create Snowflake Session to connect to Snowflake\ndef create_session(): \n    if "snowpark_session" not in st.session_state: \n        session = Session.builder.configs(json.load(open("connection.json"))).create() \n        st.session_state['snowpark_session'] = session \n    else: \n        session = st.session_state['snowpark_session'] \n    return session \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAl ejecutarla en Snowflake (SiS), se crearía el acceso al objeto de sesión actual de la siguiente manera:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession = snowpark.session._get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELimpieza\u003C/h2\u003E\n","\u003Cp\u003ESi has empezado o reanudado las tareas \u003Ccode\u003Emonthly_revenue_data_pipeline_task\u003C/code\u003E y \u003Ccode\u003Ecampaign_spend_data_pipeline_task\u003C/code\u003E como parte de las secciones \u003Cstrong\u003EIngeniería de datos\u003C/strong\u003E o \u003Cstrong\u003EFlujos de datos\u003C/strong\u003E, es importante que ejecutes los siguientes comandos para suspender las tareas y, así, evitar que se usen recursos innecesariamente.\u003C/p\u003E\n","\u003Cp\u003EEn el cuaderno con la Snowpark Python API\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEn Snowsight\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter task campaign_spend_data_pipeline_task suspend;\nalter task monthly_revenue_data_pipeline_task suspend;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusiones y recursos\u003C/h2\u003E\n","\u003Cp\u003E¡Enhorabuena! Has realizado las tareas de ingeniería de datos y entrenado un modelo de regresión lineal para predecir el futuro ROI de los presupuestos de gasto en publicidad variable en varios canales, como búsqueda, vídeo, redes sociales y correo electrónico con Snowpark para Python y scikit-learn. Además, has creado una aplicación de Streamlit que utiliza ese modelo para generar predicciones en nuevas asignaciones de presupuesto en función de lo que introduzca el usuario.\u003C/p\u003E\n","\u003Cp\u003ENos encantaría conocer tu opinión sobre esta quickstart guide. Puedes enviárnosla a través de este \u003Ca href=\"https://forms.gle/XKd8rXPUNs2G1yM28\"\u003Eformulario\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EQué has aprendido\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003Ecómo analizar datos y realizar tareas de ingeniería de datos utilizando DataFrames y las API de Snowpark;\u003C/li\u003E\u003Cli\u003Ecómo utilizar bibliotecas de Python de código abierto del canal curado de Snowflake Anaconda;\u003C/li\u003E\u003Cli\u003Ecómo entrenar modelos de ML con Snowpark ML en Snowflake;\u003C/li\u003E\u003Cli\u003Ecómo crear UDF escalares y vectorizadas de Snowpark para Python para la inferencia en línea y sin conexión, respectivamente;\u003C/li\u003E\u003Cli\u003ECómo crear tareas de Snowflake Tasks para automatizar flujos de datos y (re)entrenar el modelo\u003C/li\u003E\u003Cli\u003ECómo crear una aplicación web de Streamlit que utiliza UDF escalares para la inferencia\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERecursos relacionados\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003ECódigo fuente en GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAvanzado: Guía de ingeniería de datos de Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAvanzado: Guía de ML de Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md\"\u003EDemos de Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html\"\u003EGuía de Snowpark para Python para desarrolladores\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/\"\u003EStreamlit Docs\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Introducción a la ingeniería de datos y al ML con Snowpark para Python","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Descripción general\r\n\r\n\r\nTras completar esta guía, serás capaz de pasar de datos sin procesar a una aplicación interactiva que podrá ayudar a tu organización a optimizar la asignación de presupuestos de publicidad.\r\n\r\nA continuación, encontrarás un resumen de lo que podrás aprender en cada paso siguiendo esta quickstart guide:\r\n\r\n- **Configuración del entorno**: usa las fases y las tablas para la ingesta de datos sin procesar de S3 en Snowflake y su organización.\r\n- **Ingeniería de datos**: aprovecha DataFrames de Snowpark para Python para realizar transformaciones de datos como, por ejemplo, agruparlos, agregarlos, dinamizarlos y unirlos. Así, prepararás los datos para las aplicaciones downstream.\r\n- **Flujos de datos**: utiliza Snowflake Tasks para convertir el código de los flujos de datos en flujos operativos con supervisión integrada.  \r\n- **Aprendizaje automático**: prepara los datos y entrena los modelos de aprendizaje automático (machine learning, ML) en Snowflake con Snowpark ML e implementa el modelo como una función definida por el usuario (user-defined-function, UDF) de Snowpark.\r\n- **Aplicación de Streamlit**: crea una aplicación interactiva usando Python (sin necesidad de tener experiencia en desarrollo web) para visualizar el retorno de la inversión (ROI) de diferentes presupuestos de gasto en publicidad.\r\n\r\nEn caso de que algunas de las tecnologías mencionadas anteriormente sean nuevas para ti, hemos preparado un breve resumen con enlaces a la documentación.\r\n\r\n### ¿Qué es Snowpark?\r\n\r\nEl conjunto de bibliotecas y tiempos de ejecución de Snowflake para implementar y procesar de forma segura código que no sea SQL, como Python, Java o Scala.\r\n\r\n**Bibliotecas conocidas del cliente**: Snowpark ofrece una programación completamente integrada de estilo DataFrame y API compatibles con OSS para los lenguajes que los profesionales de los datos prefieran. También incluye la Snowpark ML API para conseguir un modelado de ML (en vista previa pública) y unas operaciones de ML (en vista previa privada) más eficientes.\r\n\r\n**Estructuras de tiempo de ejecución flexibles**: Snowpark proporciona constructos de tiempo de ejecución flexibles que permiten a los usuarios introducir y ejecutar la lógica personalizada. Los desarrolladores pueden crear flujos de datos, modelos de ML y aplicaciones de datos sin problemas gracias a las UDF y mediante procedimientos almacenados.\r\n\r\nObtén más información sobre [Snowpark](/es/data-cloud/snowpark/).\r\n\r\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowpark.png)\r\n\r\n### ¿Qué es Snowpark ML?\r\n\r\nSnowpark ML es una nueva biblioteca para lograr un desarrollo integral más rápido e intuitivo de ML en Snowflake. Snowpark ML tiene 2 API: Snowpark ML Modeling (en vista previa pública) para desarrollar modelos y Snowpark ML Operations (en vista previa privada) para implementarlos.\r\n\r\nEsta quickstart guide se centrará en la Snowpark ML Modeling API, que escala horizontalmente la ingeniería de funciones y simplifica la ejecución del entrenamiento de ML en Snowflake.\r\n\r\n### ¿Qué es Streamlit?\r\n\r\nStreamlit es un marco de aplicación de lenguaje Python puro [de código abierto](https://github.com/streamlit/streamlit) que permite a los desarrolladores escribir, compartir e implementar aplicaciones de datos de forma rápida y sencilla. Más información sobre [Streamlit](https://streamlit.io/).\r\n\r\n### Descubrirás\r\n\r\n- cómo analizar datos y realizar tareas de ingeniería de datos utilizando DataFrames y las API de Snowpark;\r\n- cómo utilizar bibliotecas de Python de código abierto del canal curado de Snowflake Anaconda;\r\n- cómo entrenar modelos de ML con Snowpark ML en Snowflake;\r\n- cómo crear UDF escalares y vectorizadas de Snowpark para Python para la inferencia en línea y sin conexión, respectivamente;\r\n- cómo crear Snowflake Tasks para automatizar flujos de datos; y\r\n- cómo crear una aplicación web con Streamlit que use UDF escalares para la inferencia en función de lo que introduzca el usuario.\r\n\r\n### Requisitos previos\r\n\r\n- [Git](https://git-scm.com/book/es/v2/Inicio---Sobre-el-Control-de-Versiones-Instalaci%C3%B3n-de-Git) debe estar instalado.\r\n- [Python 3.9](https://www.python.org/downloads/) debe estar instalado.\r\n  - Ten en cuenta que vas a crear un entorno Python con la versión 3.9 en el paso **Introducción**.\r\n- Debes disponer de una cuenta de Snowflake con [paquetes de Anaconda habilitados por ORGADMIN](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda). Si no tienes una, puedes registrarte para obtener una [cuenta de prueba gratuita](https://signup.snowflake.com/).\r\n- Debes iniciar sesión en una cuenta de Snowflake con rol ACCOUNTADMIN. Si tienes este rol en tu entorno, selecciónalo para usarlo. En el caso contrario, deberás: 1) registrarte para obtener una prueba gratuita; 2) utilizar un rol diferente que permita crear bases de datos, esquemas, tablas, fases, tareas, funciones definidas por el usuario y procedimientos almacenados, o 3) utilizar una base de datos y un esquema existentes en los que puedas crear los objetos mencionados.\r\n\r\n\u003E aside positive IMPORTANTE: Antes de comenzar, asegúrate de tener una cuenta de Snowflake con paquetes de Anaconda habilitados por ORGADMIN, tal y como se describe [aquí](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Configuración del entorno\r\n\r\n\r\n### Creación de tablas, carga de datos y configuración de fases\r\n\r\nInicia sesión en [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#) con tus credenciales para crear tablas, cargar datos de Amazon S3 y configurar las fases internas de Snowflake.\r\n\r\n\u003E aside positive IMPORTANTE:\r\n\u003E\r\n\u003E - Si utilizas nombres diferentes para los objetos creados en esta sección, asegúrate de adaptar las secuencias de comandos y el código de las siguientes secciones en consecuencia.\r\n\u003E\r\n\u003E - Para cada bloque de secuencias de comandos de SQL que se muestra a continuación, selecciona todas las sentencias del bloque y ejecútalas de arriba a abajo.\r\n\r\nEjecuta los siguientes comandos SQL para crear el [almacén](https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html), la [base de datos](https://docs.snowflake.com/en/sql-reference/sql/create-database.html) y el [esquema](https://docs.snowflake.com/en/sql-reference/sql/create-schema.html).\r\n\r\n```sql \r\nUSE ROLE ACCOUNTADMIN;\r\n\r\nCREATE OR REPLACE WAREHOUSE DASH_L; \r\nCREATE OR REPLACE DATABASE DASH_DB; \r\nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\r\n\r\nUSE DASH_DB.DASH_SCHEMA; \r\n```\r\n\r\nEjecuta los siguientes comandos SQL para crear la tabla **CAMPAIGN_SPEND** con datos alojados en un cubo de S3 de acceso público.\r\n\r\n```sql \r\nCREATE or REPLACE file format csvformat \r\n  skip_header = 1 \r\n  type = 'CSV';\r\n\r\nCREATE or REPLACE stage campaign_data_stage \r\n  file_format = csvformat \r\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\r\n\r\nCREATE or REPLACE TABLE CAMPAIGN_SPEND ( \r\n  CAMPAIGN VARCHAR(60), \r\n  CHANNEL VARCHAR(60), \r\n  DATE DATE, \r\n  TOTAL_CLICKS NUMBER(38,0), \r\n  TOTAL_COST NUMBER(38,0), \r\n  ADS_SERVED NUMBER(38,0) \r\n);\r\n\r\nCOPY into CAMPAIGN_SPEND \r\n  from @campaign_data_stage; \r\n```\r\n\r\nEjecuta los siguientes comandos SQL para crear la tabla **MONTHLY_REVENUE** con datos alojados en un cubo de S3 de acceso público.\r\n\r\n```sql \r\nCREATE or REPLACE stage monthly_revenue_data_stage \r\n  file_format = csvformat \r\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\r\n\r\nCREATE or REPLACE TABLE MONTHLY_REVENUE ( \r\n  YEAR NUMBER(38,0), \r\n  MONTH NUMBER(38,0), \r\n  REVENUE FLOAT \r\n);\r\n\r\nCOPY into MONTHLY_REVENUE \r\n  from @monthly_revenue_data_stage; \r\n```\r\n\r\nEjecuta los siguientes comandos SQL para crear la tabla **BUDGET_ALLOCATIONS_AND_ROI**, que aloja las asignaciones de presupuesto y ROI de los últimos seis meses.\r\n\r\n```sql \r\nCREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI ( \r\n  MONTH varchar(30), \r\n  SEARCHENGINE integer, \r\n  SOCIALMEDIA integer, \r\n  VIDEO integer, \r\n  EMAIL integer, \r\n  ROI float \r\n)\r\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"aiml_notebooks_ad_spend_roi\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"streamlit\"}}';\r\n\r\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI) VALUES \r\n('January',35,50,35,85,8.22), \r\n('February',75,50,35,85,13.90), \r\n('March',15,50,35,15,7.34), \r\n('April',25,80,40,90,13.23), \r\n('May',95,95,10,95,6.246), \r\n('June',35,50,35,85,8.22); \r\n```\r\n\r\nEjecuta los siguientes comandos para crear [fases internas](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage) de Snowflake y almacenar procedimientos almacenados, UDF y archivos de modelos de ML.\r\n\r\n```sql\r\nCREATE OR REPLACE STAGE dash_sprocs;\r\nCREATE OR REPLACE STAGE dash_models;\r\nCREATE OR REPLACE STAGE dash_udfs;\r\n```\r\n\r\nDe manera opcional, también puedes abrir [setup.sql](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql) en Snowsight y ejecutar todas las sentencias de SQL para crear los objetos y cargar los datos de AWS S3.\r\n\r\n\u003E aside positive IMPORTANTE: Si utilizas nombres diferentes para los objetos creados en esta sección, asegúrate de adaptar las secuencias de comandos y el código de las siguientes secciones en consecuencia.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Introducción\r\n\r\n\r\nEsta sección incluye cómo clonar un repositorio de GitHub y cómo configurar tu entorno de Snowpark para Python.\r\n\r\n### Clonación del repositorio de GitHub\r\n\r\nEl primer paso es clonar el [repositorio de GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn), que contiene todo el código que necesitarás para completar esta quickstart guide.\r\n\r\nCon HTTPS:\r\n\r\n```shell\r\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\r\n```\r\n\r\nO con SSH:\r\n\r\n```shell\r\ngit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\r\n```\r\n\r\n### Snowpark para Python\r\n\r\nPara completar los pasos **Ingeniería de datos** y **Aprendizaje automático**, puedes instalar todo en el entorno local (opción 1) o utilizar Hex (opción 2), tal y como se describe a continuación.\r\n\r\n\u003E aside positive IMPORTANTE: Para ejecutar la **aplicación de Streamlit**, tendrás que crear un entorno de Python e instalar Snowpark para Python en el entorno local junto con otras bibliotecas, tal y como se describe en **Instalación local**.\r\n\r\n#### Opción 1: Instalación local\r\n\r\nCon esta opción podrás completar todos los pasos de la quickstart guide.\r\n\r\n**Paso 1:** Descargar e instalar el instalador de Miniconda de [https://conda.io/miniconda.html](https://conda.io/miniconda.html). *(O, si lo prefieres, puedes usar cualquier otro entorno de Python con Python 3.9, como [virtualenv](https://virtualenv.pypa.io/en/latest/))*.\r\n\r\n**Paso 2:** Abrir una nueva ventana del terminal y ejecutar los siguientes comandos en ella.\r\n\r\n**Paso 3:** Crear un entorno conda de Python 3.9 con el nombre **snowpark-de-ml** mediante la ejecución del siguiente comando en la misma ventana del terminal.\r\n\r\n```python\r\nconda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\r\n```\r\n\r\n**Paso 4:** Activar el entorno conda **snowpark-de-ml** ejecutando el siguiente comando en la misma ventana del terminal.\r\n\r\n```python\r\nconda activate snowpark-de-ml\r\n```\r\n\r\n**Paso 5:** Instalar Snowpark para Python junto con las demás librerías en el entorno conda **snowpark-de-ml** del [canal de Snowflake Anaconda](https://repo.anaconda.com/pkgs/snowflake/) ejecutando el siguiente comando en la ventana del terminal.\r\n\r\n```python\r\nconda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\r\n```\r\n\r\n**Paso 6:** Instalar la biblioteca Streamlit en el entorno conda **snowpark-de-ml** ejecutando el siguiente comando en la misma ventana del terminal.\r\n\r\n```python\r\npip install streamlit\r\n```\r\n\r\n**Paso 7:** Instalar la biblioteca Snowpark ML en el entorno conda **snowpark-de-ml** ejecutando el siguiente comando en la misma ventana del terminal.\r\n\r\n```python\r\npip install snowflake-ml-python\r\n```\r\n\r\n**Paso 9:** Actualizar [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) con los detalles y las credenciales de tu cuenta de Snowflake.\r\n\r\nA continuación, puedes ver un ejemplo de ***connection.json*** basado en los nombres de los objetos mencionados en el paso **Entorno de configuración**.\r\n\r\n```json\r\n{\r\n  \"account\"   : \"\u003Cyour_account_identifier_goes_here\u003E\",\r\n  \"user\"      : \"\u003Cyour_username_goes_here\u003E\",\r\n  \"password\"  : \"\u003Cyour_password_goes_here\u003E\",\r\n  \"role\"      : \"ACCOUNTADMIN\",\r\n  \"warehouse\" : \"DASH_L\",\r\n  \"database\"  : \"DASH_DB\",\r\n  \"schema\"    : \"DASH_SCHEMA\"\r\n}\r\n```\r\n\r\n\u003E aside negative Nota: Para el parámetro **account** que se menciona arriba, especifica tu **identificador de cuenta** y no incluyas el nombre del dominio snowflakecomputing.com. Snowflake lo añade de forma automática al crear la conexión. Para obtener más información al respecto, [consulta la documentación](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html).\r\n\r\n#### Opción 2: Hex\r\n\r\nSi decides usar tu cuenta de [Hex](https://app.hex.tech/login) o [crear una cuenta de prueba gratuita de 30 días](https://app.hex.tech/signup/quickstart-30), Snowpark para Python está integrado para que no tengas que crear un entorno de Python ni instalarlo localmente en tu equipo junto con el resto de las bibliotecas. De esta forma, podrás completar los pasos **Ingeniería de datos** y **Aprendizaje automático** de esta quickstart guide directamente en Hex. (Consulta los pasos correspondientes para obtener más información sobre cómo cargar los cuadernos de ingeniería de datos y ML en Hex).\r\n\r\n\u003E aside positive IMPORTANTE: Para ejecutar la **aplicación de Streamlit**, tendrás que crear un entorno Python e instalar Snowpark para Python en el entorno local junto con otras bibliotecas, tal y como se ha descrito en **Instalación local**.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Ingeniería de datos\r\n\r\n\r\nEncontrarás a continuación el enlace a este cuaderno que incluye las siguientes tareas de ingeniería de datos:\r\n\r\n1) Establecer una conexión segura entre Snowpark para Python y Snowflake. \r\n2) Cargar datos de tablas de Snowflake en DataFrames de Snowpark. \r\n3) Realizar análisis de datos de exploración en DataFrames de Snowpark. \r\n4) Dinamizar y unir datos de varias tablas con DataFrames de Snowpark. \r\n5) Automatizar tareas de flujos de datos con Snowflake Tasks.\r\n\r\n### Cuaderno de ingeniería de datos en Jupyter o en Visual Studio Code\r\n\r\nPara comenzar, sigue estos pasos:\r\n\r\n1) En una ventana del terminal, navega hasta esta carpeta y ejecuta `jupyter notebook` en la línea de comandos. (También puedes usar otras herramientas y entornos de desarrollo integrado[integrated development environment, IDE] como Visual Studio Code).\r\n\r\n2) Abre y ejecuta las celdas de [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb)\r\n\r\n\u003E aside positive IMPORTANTE: Asegúrate de que el kernel (de Python) del cuaderno de Jupyter está configurado como ***snowpark-de-ml***, es decir, con el mismo nombre del entorno creado en el paso **Clonación del repositorio de GitHub**.\r\n\r\n### Cuaderno de ingeniería de datos en Hex\r\n\r\nSi decides usar tu cuenta de [Hex](https://app.hex.tech/login) o [crear una cuenta de prueba gratuita de 30 días](https://app.hex.tech/signup/quickstart-30), sigue estos pasos para cargar el cuaderno y crear una conexión de datos con el fin de conectarte a Snowflake desde Hex.\r\n\r\n1) Importa [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb) como un proyecto en tu cuenta. Para obtener más información sobre la importación, consulta la [documentación](https://learn.hex.tech/docs/versioning/import-export).\r\n\r\n2) A continuación, en lugar de usar [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) para conectarte a Snowflake, crea una [conexión de datos](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) y utilízala en el cuaderno de ingeniería de datos tal y como se muestra a continuación:\r\n\r\n![Conexión de datos de HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/hex_data_connection.png)\r\n\r\n\u003E aside negative Nota: También puedes crear conexiones de datos compartidos para tus proyectos y usuarios en tu espacio de trabajo. Para obtener más información al respecto, consulta la [documentación](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections).\r\n\r\n3) Sustituye el siguiente fragmento de código en el cuaderno\r\n\r\n```python\r\nconnection_parameters = json.load(open('connection.json'))\r\nsession = Session.builder.configs(connection_parameters).create()\r\n```\r\n\r\n**por…**\r\n\r\n```python\r\nimport hextoolkit\r\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\r\nsession = hex_snowflake_conn.get_snowpark_session()\r\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Flujos de datos\r\n\r\nTambién puedes hacer que las transformaciones de datos estén operativas en forma de flujos de datos automatizados que se ejecutan en Snowflake.\r\n\r\nEn concreto, en el [cuaderno de ingeniería de datos](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb), hay una sección en la que se muestra cómo se pueden crear y ejecutar las transformaciones de datos como con [Snowflake Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro).\r\n\r\nA modo de referencia, echa un vistazo a los fragmentos de código que se muestran a continuación.\r\n\r\n### **Tarea raíz/principal**\r\n\r\nEsta tarea automatiza la carga de datos de los gastos de la campaña y la realización de varias transformaciones.\r\n\r\n```python \r\ndef campaign_spend_data_pipeline(session: Session) -\u003E str: \r\n  # DATA TRANSFORMATIONS \r\n  # Perform the following actions to transform the data\r\n\r\n  # Load the campaign spend data \r\n  snow_df_spend_t = session.table('campaign_spend')\r\n\r\n  # Transform the data so we can see total cost per year/month per channel using group_by() and agg() Snowpark DataFrame functions\r\n   snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\r\n      with_column_renamed('\"YEAR(DATE)\"',\"YEAR\").with_column_renamed('\"MONTH(DATE)\"',\"MONTH\").sort('YEAR','MONTH')\r\n\r\n  # Transform the data so that each row will represent total cost across all channels per year/month using pivot() and sum() Snowpark DataFrame functions \r\n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH') \r\n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select( \r\n      col(\"YEAR\"), \r\n      col(\"MONTH\"), \r\n      col(\"'search_engine'\").as_(\"SEARCH_ENGINE\"), \r\n      col(\"'social_media'\").as_(\"SOCIAL_MEDIA\"), \r\n      col(\"'video'\").as_(\"VIDEO\"), \r\n      col(\"'email'\").as_(\"EMAIL\") \r\n  )\r\n\r\n  # Save transformed data \r\n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\r\n\r\n# Register data pipelining function as a Stored Procedure so it can be run as a task\r\nsession.sproc.register( \r\n  func=campaign_spend_data_pipeline, \r\n  name=\"campaign_spend_data_pipeline\", \r\n  packages=['snowflake-snowpark-python'], \r\n  is_permanent=True, \r\n  stage_location=\"@dash_sprocs\", \r\n  replace=True)\r\n\r\ncampaign_spend_data_pipeline_task = \"\"\" \r\nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task \r\n    WAREHOUSE = 'DASH_L' \r\n    SCHEDULE = '3 MINUTE' \r\nAS \r\n    CALL campaign_spend_data_pipeline() \r\n\"\"\" \r\nsession.sql(campaign_spend_data_pipeline_task).collect() \r\n```\r\n\r\n### **Tarea secundaria/dependiente**\r\n\r\nEsta tarea automatiza la carga de datos de los ingresos mensuales, la realización de varias transformaciones y su unión con los datos de los gastos de la campaña.\r\n\r\n```python \r\ndef monthly_revenue_data_pipeline(session: Session) -\u003E str: \r\n  # Load revenue table and transform the data into revenue per year/month using group_by and agg() functions \r\n  snow_df_spend_per_month_t = session.table('spend_per_month') \r\n  snow_df_revenue_t = session.table('monthly_revenue') \r\n  snow_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\r\n\r\n  # Join revenue data with the transformed campaign spend data so that our input features (i.e. cost per channel) and target variable (i.e. revenue) can be loaded into a single table for model training \r\n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, [\"YEAR\",\"MONTH\"])\r\n\r\n  # SAVE in a new table for the next task \r\n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\r\n\r\n# Register data pipelining function as a Stored Procedure so it can be run as a task\r\nsession.sproc.register( \r\n  func=monthly_revenue_data_pipeline, \r\n  name=\"monthly_revenue_data_pipeline\", \r\n  packages=['snowflake-snowpark-python'], \r\n  is_permanent=True, \r\n  stage_location=\"@dash_sprocs\", \r\n  replace=True)\r\n\r\nmonthly_revenue_data_pipeline_task = \"\"\" \r\n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task \r\n      WAREHOUSE = 'DASH_L' \r\n      AFTER campaign_spend_data_pipeline_task \r\n  AS \r\n      CALL monthly_revenue_data_pipeline() \r\n  \"\"\" \r\nsession.sql(monthly_revenue_data_pipeline_task).collect() \r\n```\r\n\r\n\u003E aside negative Nota: Observa arriba, en ***monthly_revenue_data_pipeline_task***, que la cláusula **AFTER campaign_spend_data_pipeline_task** hace que sea una tarea dependiente.\r\n\r\n#### Inicio de Tasks\r\n\r\nSnowflake Tasks no se inicia por defecto. Debes ejecutar la siguiente sentencia para iniciar la función o reanudarla.\r\n\r\n```sql\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task resume\").collect()\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task resume\").collect()\r\n```\r\n\r\n#### Suspender tareas\r\n\r\nSi reanudas las tareas anteriores, suspéndelas para evitar que se usen recursos innecesariamente. Para ello, ejecuta los siguientes comandos.\r\n\r\n```sql\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\r\n```\r\n\r\n### Observabilidad de tareas\r\n\r\nEstas tareas y sus [grafos acíclicos dirigidos (directed acyclic graphs, DAG)](https://docs.snowflake.com/en/user-guide/tasks-intro#label-task-dag) se pueden ver en [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs), como se muestra a continuación.\r\n\r\n---\r\n\r\n![Observabilidad de tareas](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowflake_tasks.png)\r\n\r\n---\r\n\r\n### Notificaciones de error de las tareas\r\n\r\nTambién puedes habilitar el envío de notificaciones push a un servicio de mensajería en la nube cuando se produzca algún error en la ejecución de las tareas. Para obtener más información, consulta la [documentación](https://docs.snowflake.com/en/user-guide/tasks-errors).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Aprendizaje automático\r\n\r\n\r\n\u003E aside negative SE REQUIERE haber completado correctamente los pasos de ingeniería de datos que se describen en [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb).\r\n\r\nEn el enlace del cuaderno que encontrarás a continuación incluye las siguientes tareas de aprendizaje automático:\r\n\r\n1) Establecer una conexión segura entre Snowpark para Python y Snowflake. \r\n2) Cargar funciones y destinos de Snowflake en DataFrames de Snowpark. \r\n3) Preparar las funciones para el entrenamiento de modelos. \r\n4) Entrenar modelos de ML con Snowpark ML en Snowflake. \r\n5) Crear [ UDF](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs) escalares y vectorizadas (por lotes) de Python para inferir nuevos puntos de datos tanto en línea como sin conexión, respectivamente.\r\n\r\n---\r\n\r\n![ML integral](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/snowpark_e2e_ml.png)\r\n\r\n---\r\n\r\n### Cuaderno de aprendizaje automático en Jupyter o Visual Studio Code\r\n\r\nPara comenzar, sigue estos pasos:\r\n\r\n1) En una ventana del terminal, navega hasta esta carpeta y ejecuta `jupyter notebook` en la línea de comandos. (También puedes usar otras herramientas e IDE como Visual Studio Code).\r\n\r\n2) Abre y ejecuta[Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb)\r\n\r\n\u003E aside positive IMPORTANTE: Asegúrate de que el kernel (de Python) del cuaderno de Jupyter está configurado como ***snowpark-de-ml***, es decir, con el mismo nombre del entorno creado en el paso **Clonación del repositorio de GitHub**.\r\n\r\n### Cuaderno de aprendizaje automático en Hex\r\n\r\nSi decides usar tu cuenta de [Hex](https://app.hex.tech/login) o [crear una cuenta de prueba gratuita de 30 días](https://app.hex.tech/signup/quickstart-30), sigue estos pasos para cargar el cuaderno y crear una conexión de datos con el fin de conectarte a Snowflake desde Hex.\r\n\r\n1) Importa [Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb) como un proyecto en tu cuenta. Para obtener más información sobre la importación, consulta la [documentación](https://learn.hex.tech/docs/versioning/import-export).\r\n\r\n2) A continuación, en lugar de usar [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) para conectarte a Snowflake, crea una [conexión de datos](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) y utilízala en el cuaderno de aprendizaje automático tal y como se muestra a continuación:\r\n\r\n![Conexión de datos de HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/hex_data_connection.png)\r\n\r\n\u003E aside negative Nota: También puedes crear conexiones de datos compartidos para tus proyectos y usuarios en tu espacio de trabajo. Para obtener más información al respecto, consulta la [documentación](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections).\r\n\r\n3) Sustituye el siguiente fragmento de código en el cuaderno\r\n\r\n```python\r\nconnection_parameters = json.load(open('connection.json'))\r\nsession = Session.builder.configs(connection_parameters).create()\r\n```\r\n\r\n**por…**\r\n\r\n```python\r\nimport hextoolkit\r\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\r\nsession = hex_snowflake_conn.get_snowpark_session()\r\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Aplicación de Streamlit\r\n\r\n\r\n### Ejecución de la aplicación de Streamlit en el entorno local\r\n\r\nEn una ventana del terminal, ve a esta carpeta y ejecuta el siguiente comando para ejecutar la aplicación de Streamlit [Snowpark_Streamlit_Revenue_Prediction.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py) localmente en tu ordenador.\r\n\r\n```shell\r\nstreamlit run Snowpark_Streamlit_Revenue_Prediction.py\r\n```\r\n\r\nSi todo ha salido bien, deberías ver cómo se abre una ventana del navegador con la aplicación ya cargada, tal y como se muestra a continuación.\r\n\r\n---\r\n\r\n![Aplicación de Streamlit](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/app.png)\r\n\r\n---\r\n\r\n### Ejecución de la aplicación de Streamlit en Snowflake: Streamlit en Snowflake\r\n\r\nSi has habilitado Streamlit en Snowflake (Streamlit-in-Snowflake, SiS) en tu cuenta, sigue estos pasos para ejecutar la aplicación en Snowsight en lugar de localmente en tu ordenador.\r\n\r\n\u003E aside negative IMPORTANTE: SiS se encuentra en vista previa privada a fecha de junio de 2023.***\r\n\r\n  1) Haz clic en **Streamlit Apps** en el menú de navegación de la izquierda. \r\n  2) Haz clic en **+ Streamlit App** en la parte superior derecha. \r\n  3) Introduce el nombre de la aplicación en **App name**. \r\n  4) Selecciona **Warehouse** y **App location** (base de datos y esquema) donde desees crear la aplicación de Streamlit. \r\n  5) Haz clic en **Create**. \r\n  6) Llegado a este punto, se te proporcionará el código para crear una aplicación de Streamlit de ejemplo. Abre [Snowpark_Streamlit_Revenue_Prediction_SiS.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py) y copia el código. Pégalo a continuación en la aplicación de Streamlit de ejemplo. \r\n  7) Haz clic en **Run** en la parte superior derecha para ejecutar la aplicación.\r\n\r\nSi todo ha salido bien, deberías ver la aplicación en Snowsight, tal y como se muestra a continuación.\r\n\r\n---\r\n\r\n![Streamlit en Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-es/app_sis.png)\r\n\r\n---\r\n\r\n### Guardar datos en Snowflake\r\n\r\nAjusta el control deslizante del presupuesto de publicidad en las dos aplicaciones para ver el ROI previsto para esas asignaciones. También puedes hacer clic en el botón **Save to Snowflake** para guardar las asignaciones de ROI actuales y previstas en la tabla de Snowflake BUDGET_ALLOCATIONS_AND_ROI.\r\n\r\n### Diferencias entre las dos aplicaciones de Streamlit\r\n\r\nLa principal diferencia entre la ejecución de la aplicación de Streamlit en un entorno local y en Snowflake (SiS) es la manera en la que se crea y se accede al objeto de sesión.\r\n\r\nAl ejecutarla en un entorno local, se crearía y se accedería al nuevo objeto de sesión de la siguiente manera:\r\n\r\n```python\r\n# Function to create Snowflake Session to connect to Snowflake\r\ndef create_session(): \r\n    if \"snowpark_session\" not in st.session_state: \r\n        session = Session.builder.configs(json.load(open(\"connection.json\"))).create() \r\n        st.session_state['snowpark_session'] = session \r\n    else: \r\n        session = st.session_state['snowpark_session'] \r\n    return session \r\n```\r\n\r\nAl ejecutarla en Snowflake (SiS), se crearía el acceso al objeto de sesión actual de la siguiente manera:\r\n\r\n```python\r\nsession = snowpark.session._get_active_session()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Limpieza\r\n\r\nSi has empezado o reanudado las tareas `monthly_revenue_data_pipeline_task` y `campaign_spend_data_pipeline_task` como parte de las secciones **Ingeniería de datos** o **Flujos de datos**, es importante que ejecutes los siguientes comandos para suspender las tareas y, así, evitar que se usen recursos innecesariamente.\r\n\r\nEn el cuaderno con la Snowpark Python API\r\n\r\n```sql\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\r\n```\r\n\r\nEn Snowsight\r\n\r\n```sql\r\nalter task campaign_spend_data_pipeline_task suspend;\r\nalter task monthly_revenue_data_pipeline_task suspend;\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusiones y recursos\r\n\r\n\r\n¡Enhorabuena! Has realizado las tareas de ingeniería de datos y entrenado un modelo de regresión lineal para predecir el futuro ROI de los presupuestos de gasto en publicidad variable en varios canales, como búsqueda, vídeo, redes sociales y correo electrónico con Snowpark para Python y scikit-learn. Además, has creado una aplicación de Streamlit que utiliza ese modelo para generar predicciones en nuevas asignaciones de presupuesto en función de lo que introduzca el usuario.\r\n\r\nNos encantaría conocer tu opinión sobre esta quickstart guide. Puedes enviárnosla a través de este [formulario](https://forms.gle/XKd8rXPUNs2G1yM28).\r\n\r\n### Qué has aprendido\r\n\r\n- cómo analizar datos y realizar tareas de ingeniería de datos utilizando DataFrames y las API de Snowpark;\r\n- cómo utilizar bibliotecas de Python de código abierto del canal curado de Snowflake Anaconda;\r\n- cómo entrenar modelos de ML con Snowpark ML en Snowflake;\r\n- cómo crear UDF escalares y vectorizadas de Snowpark para Python para la inferencia en línea y sin conexión, respectivamente;\r\n- Cómo crear tareas de Snowflake Tasks para automatizar flujos de datos y (re)entrenar el modelo\r\n- Cómo crear una aplicación web de Streamlit que utiliza UDF escalares para la inferencia\r\n\r\n### Recursos relacionados\r\n\r\n- [Código fuente en GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn)\r\n- [Avanzado: Guía de ingeniería de datos de Snowpark para Python](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\r\n- [Avanzado: Guía de ML de Snowpark para Python](/en/developers/guides/getting-started-snowpark-machine-learning/)\r\n- [Demos de Snowpark para Python](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\r\n- [Guía de Snowpark para Python para desarrolladores](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html)\r\n- [Streamlit Docs](https://docs.streamlit.io/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-42d4da585a","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-e92c2f4aa2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-2479d3138e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-10-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-319b7a24b3","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-99d3ab13e8",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-55aed2b664",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-752789d5a8","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-452bd4ae44",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/getting-started-with-dataengineering-ml-using-snowpark-python-es","headings":["\u003Ch2\u003EDescripción 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