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-->\n","\u003Ch2\u003EVisão geral\u003C/h2\u003E\n","\u003Cp\u003EAo completar este guia, você poderá criar uma aplicação interativa a partir de dados brutos para ajudar uma organização a otimizar a alocação de recursos para publicidade.\u003C/p\u003E\n","\u003Cp\u003EConfira aqui um resumo do que você vai aprender em cada etapa do quickstart:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConfiguração do ambiente\u003C/strong\u003E: usar estágios e tabelas para ingerir e organizar dados brutos do S3 dentro do Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEngenharia de dados\u003C/strong\u003E: usar os DataFrames do Snowpark para Python para executar transformações de dados, como agrupar, agregar, dinamizar e combinar, para preparar os dados para aplicações mais adiante no processo.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPipelines de dados\u003C/strong\u003E: usar o Snowflake Tasks para transformar o código do seu pipeline de dados em pipelines operacionais com monitoramento integrado.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAprendizado de máquina\u003C/strong\u003E: preparar dados e executar treinamento de aprendizado de máquina (machine learning, ML) no Snowflake com o Snowpark ML e implementar o modelo como uma função definida por usuário (user-defined-function, UDF) do Snowpark.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAplicação Streamlit\u003C/strong\u003E: desenvolver uma aplicação interativa usando Python (sem precisar de experiência com desenvolvimento web) para ajudar a visualizar o retorno do investimento (ROI) em diferentes orçamentos de publicidade.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECaso você não conheça algumas tecnologias mencionadas acima, confira a seguir um breve resumo com links para documentação.\u003C/p\u003E\n","\u003Ch3\u003EO que é o Snowpark?\u003C/h3\u003E\n","\u003Cp\u003ETrata-se do conjunto de bibliotecas e sistemas de runtime (tempo de execução) do Snowflake que implementam e processam códigos não SQL de forma segura, incluindo Python, Java e Scala.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBibliotecas conhecidas dos clientes\u003C/strong\u003E: o Snowpark oferece interfaces de programação de aplicativos (application programming interface, APIs) totalmente integradas, com programação no estilo DataFrame e compatíveis com sistemas de suporte operacional (operational support system, OSS) nas linguagens que os operadores de dados gostam de usar. Ele também conta com a API Snowpark ML, para uma modelagem de aprendizado de máquina (machine learning, ML) (em versão preliminar pública) e operações de ML (em versão preliminar privada) mais eficientes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEstrutura de runtime flexível\u003C/strong\u003E: o Snowpark oferece estruturas de runtime flexíveis que permitem aos usuários inserir e executar uma lógica personalizada. Os desenvolvedores podem criar pipelines de dados, modelos de ML e aplicações de dados com facilidade, utilizando funções definidas pelo usuário e procedimentos armazenados.\u003C/p\u003E\n","\u003Cp\u003ESaiba mais sobre o \u003Ca href=\"/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-ptr/snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EO que é o Snowpark ML?\u003C/h3\u003E\n","\u003Cp\u003EO Snowpark ML é uma nova biblioteca que permite um desenvolvimento de ML completo, mais ágil e intuitivo no Snowflake. Ele conta com duas APIs: Snowpark ML Modeling (em versão preliminar pública) para desenvolvimento de modelos e Snowpark ML Operations (em versão preliminar privada) para implementação de modelos.\u003C/p\u003E\n","\u003Cp\u003EEste quickstart é voltado para a API Snowpark ML Modeling, que expande a engenharia de recursos e simplifica a execução do treinamento de ML no Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EO que é o Streamlit?\u003C/h3\u003E\n","\u003Cp\u003EÉ uma estrutura de aplicação de \u003Ca href=\"https://github.com/streamlit/streamlit\"\u003Ecódigo aberto\u003C/a\u003E em Python que permite aos desenvolvedores criar, compartilhar e implementar aplicações de dados de forma rápida e simples. Saiba mais sobre o \u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EVocê vai aprender como\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAnalisar dados e executar tarefas de engenharia de dados usando DataFrames e APIs do Snowpark.\u003C/li\u003E\u003Cli\u003EUsar bibliotecas de código aberto em Python de um canal Anaconda selecionado do Snowflake.\u003C/li\u003E\u003Cli\u003ETreinar um modelo de ML usando o Snowpark ML no Snowflake.\u003C/li\u003E\u003Cli\u003ECriar funções definidas pelo usuário (user-defined functions, UDFs) em Python do tipo escalar e vetorizada no Snowpark, para inferência online e offline respectivamente.\u003C/li\u003E\u003Cli\u003ECriar Snowflake Tasks para automatizar pipelines de dados.\u003C/li\u003E\u003Cli\u003ECriar uma aplicação web Streamlit que usa UDF escalar para inferência baseada nos dados inseridos pelo usuário.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPré-requisitos\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ETer o \u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E instalado.\u003C/li\u003E\u003Cli\u003ETer o \u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.9\u003C/a\u003E instalado.\n\u003Cul\u003E\u003Cli\u003EVocê vai criar um ambiente Python com a versão 3.9 na etapa \u003Cstrong\u003EIntrodução\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EUma conta Snowflake com \u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/udf/python/udf-python-packages#using-third-party-packages-from-anaconda\"\u003Epacotes Anaconda habilitados pelo ORGADMIN\u003C/a\u003E. Caso você não possua uma conta Snowflake, inscreva-se em uma \u003Ca href=\"https://signup.snowflake.com/?lang=pt-br\"\u003Econta de avaliação gratuita\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EUm login da conta Snowflake com a função ACCOUNTADMIN. Se você tiver essa função no seu ambiente, pode optar por usá-la. Caso contrário, será necessário:\u003C/li\u003E\u003C/ul\u003E\n\u003Col\u003E\u003Cli\u003EInscrever-se em uma avaliação gratuita;\u003C/li\u003E\u003Cli\u003EUsar uma função diferente capaz de criar banco de dados, esquema, tabelas, estágios, tarefas, funções definidas pelo usuário e procedimentos armazenados; OU\u003C/li\u003E\u003Cli\u003EUsar um banco de dados e esquema existentes onde você possa criar os objetos mencionados.\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: antes de continuar, é preciso ter uma conta Snowflake com pacotes Anaconda habilitados pelo ORGADMIN como descrito \u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/udf/python/udf-python-packages#getting-started\"\u003Eaqui\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfiguração do ambiente\u003C/h2\u003E\n","\u003Ch3\u003ECriação de tabelas, carregamento de dados e configuração de estágios\u003C/h3\u003E\n","\u003Cp\u003EAcesse o \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003E com suas credenciais para criar tabelas, carregar dados do Amazon S3 e configurar estágios internos do Snowflake.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ESe você usar nomes diferentes para os objetos criados nesta seção, atualize os scripts e o código nas seções a seguir conforme necessário.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPara cada bloco de script SQL abaixo, escolha as instruções no bloco e execute-as do início ao fim.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExecute os comandos SQL a seguir para criar um \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/create-warehouse\"\u003Earmazenamento\u003C/a\u003E, um \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/create-database\"\u003Ebanco de dados\u003C/a\u003E e um \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/create-schema\"\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\u003EExecute os comandos SQL a seguir para criar a tabela \u003Cstrong\u003ECAMPAIGN_SPEND\u003C/strong\u003E a partir dos dados hospedados no compartimento do S3 acessível ao 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\u003EExecute os comandos SQL a seguir para criar a tabela \u003Cstrong\u003EMONTHLY_REVENUE\u003C/strong\u003E a partir dos dados no hospedados no compartimento do S3 acessível ao 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\u003EExecute os comandos SQL a seguir para criar a tabela \u003Cstrong\u003EBUDGET_ALLOCATIONS_AND_ROI\u003C/strong\u003E que contém os orçamentos alocados e o retorno do investimento (ROI) dos 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)\nVALUES \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\u003EExecute os comandos a seguir para criar os \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/data-load-local-file-system-create-stage\"\u003Eestágios internos\u003C/a\u003E do Snowflake para armazenar os procedimentos armazenados, as UDFs e os arquivos de modelo 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\u003ESe preferir, você pode abrir o \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 no Snowsight e executar todas as instruções SQL para criar os objetos e carregar os dados do AWS S3.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: se você usar nomes diferentes para os objetos criados nesta seção, atualize os scripts e o código nas seções a seguir conforme necessário.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntrodução\u003C/h2\u003E\n","\u003Cp\u003EEsta seção aborda a clonagem do repositório do GitHub e a configuração do ambiente Snowpark para Python.\u003C/p\u003E\n","\u003Ch3\u003EClonagem do repositório do GitHub\u003C/h3\u003E\n","\u003Cp\u003EO primeiro passo é clonar o \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003Erepositório do GitHub\u003C/a\u003E. Esse repositório contém todo o código necessário para completar este quickstart guide com sucesso.\u003C/p\u003E\n","\u003Cp\u003EUsando 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\u003EOU usando 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 concluir as etapas de \u003Cstrong\u003EEngenharia de dados\u003C/strong\u003E e \u003Cstrong\u003EAprendizado de máquina\u003C/strong\u003E, você pode instalar tudo localmente (opção 1) ou usar o Hex (opção 2) como descrito a seguir.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: para executar a \u003Cstrong\u003Eaplicação Streamlit\u003C/strong\u003E, você terá que criar um ambiente Python e instalar o Snowpark para Python junto a outras bibliotecas localmente, como descrito em \u003Cstrong\u003EInstalação local\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOpção 1 – Instalação local\u003C/h4\u003E\n","\u003Cp\u003EEsta opção permite completar todas as etapas deste quickstart guide.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEtapa 1:\u003C/strong\u003E fazer download e executar o programa de instalação miniconda de \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003E. \u003Cem\u003E(Se preferir, utilize qualquer outro ambiente Python com Python 3.9, por exemplo, \u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003E)\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEtapa 2:\u003C/strong\u003E abrir uma nova janela do terminal e executar os seguintes comandos nela.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEtapa 3:\u003C/strong\u003E criar um ambiente conda em Python 3.9 chamado \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E, executando o seguinte comando na janela do 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\u003EEtapa 4:\u003C/strong\u003E ativar o ambiente conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E executando o seguinte comando na janela do 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\u003EEtapa 5:\u003C/strong\u003E instalar o Snowpark Python e as demais bibliotecas no ambiente conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E a partir do \u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake/\"\u003Ecanal Snowflake Anaconda\u003C/a\u003E, executando o seguinte comando na janela do 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\u003EEtapa 6:\u003C/strong\u003E instalar a biblioteca Streamlit no ambiente conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E executando o seguinte comando na janela do 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\u003EEtapa 7:\u003C/strong\u003E instalar a biblioteca Snowpark ML no ambiente conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E executando o seguinte comando na janela do 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\u003EEtapa 9:\u003C/strong\u003E atualizar o \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E com as informações e as credenciais da sua conta Snowflake.\u003C/p\u003E\n","\u003Cp\u003EAqui temos um \u003Cem\u003E\u003Cstrong\u003Econnection.json\u003C/strong\u003E\u003C/em\u003E de amostra baseado nos nomes de objeto mencionados na etapa \u003Cstrong\u003EConfiguração do ambiente\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 Observação: no parâmetro \u003Cstrong\u003Eaccount\u003C/strong\u003E acima, especifique seu \u003Cstrong\u003Eidentificador de conta\u003C/strong\u003E, sem incluir o domínio snowflakecomputing.com. O Snowflake o acrescenta automaticamente ao criar a conexão. Para obter mais informações, \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/admin-account-identifier\"\u003Econsulte a documentação\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOpção 2 – Utilização do Hex\u003C/h4\u003E\n","\u003Cp\u003ECaso opte por usar sua conta \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E ou \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecriar uma conta de avaliação gratuita de 30 dias\u003C/a\u003E, então o Snowpark para Python já estará integrado, eliminando a necessidade de criar um ambiente Python e instalar o Snowpark para Python junto das demais bibliotecas no seu notebook. Com isso, você poderá concluir as etapas de \u003Cstrong\u003EEngenharia de dados\u003C/strong\u003E e \u003Cstrong\u003EAprendizado de máquina\u003C/strong\u003E deste quickstart guide direto no Hex. Consulte as respectivas etapas para obter mais detalhes sobre o carregamento de notebooks de engenharia de dados e aprendizado de máquina no Hex.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive IMPORTANTE: para executar a \u003Cstrong\u003Eaplicação Streamlit\u003C/strong\u003E, você terá que criar um ambiente Python e instalar o Snowpark para Python junto a outras bibliotecas localmente, conforme descrito acima em \u003Cstrong\u003EInstalação local\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEngenharia de dados\u003C/h2\u003E\n","\u003Cp\u003EO notebook do link abaixo aborda as seguintes tarefas de engenharia de dados.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEstabelecer uma conexão segura entre o Snowpark Python e o Snowflake.\u003C/li\u003E\u003Cli\u003ECarregar dados de tabelas do Snowflake nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EExecutar uma análise de dados exploratória nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EDinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EAutomatizar as tarefas de pipeline de dados com o Snowflake Tasks.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ENotebook de engenharia de dados no Jupyter ou Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPara começar, siga estas etapas:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EEm uma janela do terminal, acesse a seguinte pasta e execute \u003Ccode\u003Ejupyter notebook\u003C/code\u003E na linha de comando. (Também é possível usar outras ferramentas e ambientes de desenvolvimento integrado [integrated development environment, IDEs], como o Visual Studio Code.)\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAbra e execute as células em \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: verifique se o kernel (Python) do notebok Jupyter está definido como \u003Cem\u003E\u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E\u003C/em\u003E, que é o mesmo nome do ambiente criado na etapa \u003Cstrong\u003EClonagem do repositório do GitHub\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENotebook de engenharia de dados no Hex\u003C/h3\u003E\n","\u003Cp\u003ECaso opte por usar sua conta do \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E ou \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecriar uma conta de avaliação gratuita de 30 dias\u003C/a\u003E, siga estas etapas para carregar o notebook e criar uma conexão de dados com o Snowflake a partir do Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImporte \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 um projeto na sua conta. Para obter mais informações sobre importação, consulte a \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentação\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA seguir, em vez 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 se conectar ao Snowflake, crie uma \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econexão de dados\u003C/a\u003E e use-a no notebook de engenharia de dados conforme demonstrado abaixo.\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-ptr/hex_data_connection.png\" alt=\"Conexão de dados do HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Observação: também é possível criar conexões compartilhadas de dados com projetos e usuários no seu espaço de trabalho. Par obter mais informações, consulte a \u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Edocumentação\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ESubstitua o próximo snippet de código no notebook.\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\u003EPipelines de dados\u003C/h2\u003E\n","\u003Cp\u003ETambém é possível operacionalizar as transformações de dados na forma de pipelines de dados automatizados executados no Snowflake.\u003C/p\u003E\n","\u003Cp\u003EEm particular, no \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003Enotebook de engenharia de dados\u003C/a\u003E, há uma seção que mostra como criar e executar transformações de dados de modo opcional como \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflake Tasks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EPara fins de referência, aqui estão os snippets de código.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ETarefa raiz / pai (primária)\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EAutomatiza o carregamento de dados de despesas da campanha e a execução de diversas transformações.\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\u003ETarefa filho (secundária) / dependente\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003EAutomatiza o carregamento de dados de receita mensal, a execução de diversas transformações e a combinação com dados transformados de despesas da campanha.\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 Observação: em \u003Cem\u003E\u003Cstrong\u003Emonthly_revenue_data_pipeline_task\u003C/strong\u003E\u003C/em\u003E acima, observe a cláusula \u003Cstrong\u003EAFTER campaign_spend_data_pipeline_task\u003C/strong\u003E que faz dela uma tarefa dependente.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EIniciar tarefas\u003C/h4\u003E\n","\u003Cp\u003EO Snowflake Tasks não é iniciado por padrão, então é preciso executar as seguintes instruções para iniciá-lo/retomá-lo.\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 tarefas\u003C/h4\u003E\n","\u003Cp\u003ESe você retomar as tarefas acima, suspenda-as para evitar o uso desnecessário de recursos, executando os seguintes 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\u003EObservabilidade das tarefas\u003C/h3\u003E\n","\u003Cp\u003EAs tarefas e seus \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/tasks-intro#dag-of-tasks\"\u003Egráficos acíclicos dirigidos (directed acyclic graphs, DAGs)\u003C/a\u003E podem ser visualizados no \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs\"\u003ESnowsight\u003C/a\u003E, conforme mostrado abaixo.\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-ptr/snowflake_tasks.png\" alt=\"Observabilidade de tarefas\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ENotificações de erros para as tarefas\u003C/h3\u003E\n","\u003Cp\u003ETambém é possível habilitar notificações por push para um serviço de mensagens na nuvem em caso de erros durante a execução das tarefas. Para obter mais informações, consulte a \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/tasks-errors\"\u003Edocumentação\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAprendizado de máquina\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative PRÉ-REQUISITO: concluir as etapas descritas em \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\u003EO notebook do link abaixo aborda as seguintes tarefas de aprendizado de máquina.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEstabelecer uma conexão segura entre o Snowpark Python e o Snowflake.\u003C/li\u003E\u003Cli\u003ECarregar recursos e destinos da tabela do Snowflake no DataFrame do Snowpark.\u003C/li\u003E\u003Cli\u003EPreparar os recursos para treinamento de modelos.\u003C/li\u003E\u003Cli\u003ETreinar o modelo de ML com Snowpark ML no Snowflake.\u003C/li\u003E\u003Cli\u003ECriar \u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/python/creating-udfs\"\u003Efunções definidas pelo usuário (UDFs)\u003C/a\u003E escalares e vetorizadas (também conhecidas como “em lote”) para inferência de novos pontos de dados e inferência online e offline, 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-ptr/snowpark_e2e_ml.png\" alt=\"Aprendizado de máquina completo\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ENotebook de aprendizado de máquina no Jupyter ou Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPara começar, siga estas etapas:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EEm uma janela do terminal, acesse a seguinte pasta e execute \u003Ccode\u003Ejupyter notebook\u003C/code\u003E na linha de comando. (Também é possível usar outras ferramentas e ambientes de desenvolvimento integrado [integrated development environment, IDEs], como o Visual Studio Code.)\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAbra e execute o \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: verifique se o kernel (Python) do notebok Jupyter está definido como \u003Cem\u003E\u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E\u003C/em\u003E, que é o mesmo nome do ambiente criado na etapa \u003Cstrong\u003EClonagem do repositório do GitHub\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENotebook de aprendizado de máquina no Hex\u003C/h3\u003E\n","\u003Cp\u003ECaso opte por usar sua conta do \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E ou \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecriar uma conta de avaliação gratuita de 30 dias\u003C/a\u003E, siga estas etapas para carregar o notebook e criar uma conexão de dados com o Snowflake a partir do Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImporte \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 um projeto na sua conta. Para obter mais informações sobre importação, consulte a \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentação\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA seguir, em vez 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 se conectar ao Snowflake, crie uma \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econexão de dados\u003C/a\u003E e use-a no notebook de aprendizado de máquina, como demonstrado abaixo.\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-ptr/hex_data_connection.png\" alt=\"Conexão de dados do HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative Observação: também é possível criar conexões compartilhadas de dados com projetos e usuários no seu espaço de trabalho. Par obter mais informações, consulte a \u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Edocumentação\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ESubstitua o próximo snippet de código no notebook.\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\u003EAplicação Streamlit\u003C/h2\u003E\n","\u003Ch3\u003EExecução local da aplicação Streamlit\u003C/h3\u003E\n","\u003Cp\u003EEm uma janela do terminal, acesse esta pasta e use o próximo comando para executar a aplicação 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 em sua máquina.\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\u003ESe tudo correr bem, deve surgir uma janela de navegador com a aplicação carregada, conforme abaixo.\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-ptr/app.png\" alt=\"Streamlit-App\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExecução da aplicação Streamlit no Snowflake – Streamlit-in-Snowflake (SiS)\u003C/h3\u003E\n","\u003Cp\u003ECaso você tenha o SiS habilitado em sua conta, siga estas etapas para executar a aplicação no Snowsight, em vez de localmente em sua máquina.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative IMPORTANTE: o SiS está em versão preliminar privada em junho de 2023.***\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003EClique em \u003Cstrong\u003EStreamlit Apps\u003C/strong\u003E no menu de navegação à esquerda.\u003C/li\u003E\u003Cli\u003EClique em \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E no canto superior direito.\u003C/li\u003E\u003Cli\u003EInsira o \u003Cstrong\u003Enome da aplicação\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelecione \u003Cstrong\u003EWarehouse\u003C/strong\u003E e o \u003Cstrong\u003Elocal da aplicação\u003C/strong\u003E (banco de dados e esquema) onde você quer criar a aplicação Streamlit.\u003C/li\u003E\u003Cli\u003EClique em \u003Cstrong\u003ECreate\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EVocê receberá um código de uma aplicação Streamlit de exemplo. Agora, abra \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 e copie e cole o código na aplicação Streamlit de exemplo.\u003C/li\u003E\u003Cli\u003EClique em \u003Cstrong\u003ERun\u003C/strong\u003E no canto superior direito.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ESe tudo correr bem, você deverá ver a aplicação no Snowsight conforme abaixo.\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-ptr/app_sis.png\" alt=\"Streamlit-in-Snowflake\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESalvar dados no Snowflake\u003C/h3\u003E\n","\u003Cp\u003EEm ambas as aplicações, ajuste os controles deslizantes de orçamento de publicidade para ver o retorno do investimento (ROI) previsto para essas alocações. Também é possível clicar no botão \u003Cstrong\u003ESave to Snowflake\u003C/strong\u003E para salvar as alocações atuais e o retorno do investimento previsto na tabela BUDGET_ALLOCATIONS_AND_ROI do Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EDiferenças entre as duas aplicações Streamlit\u003C/h3\u003E\n","\u003Cp\u003EA principal diferença entre executar a aplicação Streamlit localmente e no Snowflake (SiS) é a forma como você cria e acessa o objeto da sessão.\u003C/p\u003E\n","\u003Cp\u003ENa execução local, você cria e acessa o novo objeto da sessão da seguinte forma:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Função para criar uma sessão Snowflake para conectar ao 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\u003EAo executar no Snowflake (SiS), você acessa o objeto da sessão atual da seguinte forma:\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\u003ELimpeza\u003C/h2\u003E\n","\u003Cp\u003ECaso você tenha iniciado/retomado as duas tarefas \u003Ccode\u003Emonthly_revenue_data_pipeline_task\u003C/code\u003E e \u003Ccode\u003Ecampaign_spend_data_pipeline_task\u003C/code\u003E durante as seções \u003Cstrong\u003EEngenharia de dados\u003C/strong\u003E ou \u003Cstrong\u003EPipelines de dados\u003C/strong\u003E, é importante executar os seguintes comandos para suspender essas tarefas e evitar o uso desnecessário de recursos.\u003C/p\u003E\n","\u003Cp\u003ENo notebook que usa a API Snowpark Python:\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\u003ENo 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\u003EConclusão e recursos\u003C/h2\u003E\n","\u003Cp\u003EParabéns! Você executou com sucesso tarefas de engenharia de dados e treinou um modelo de regressão linear para prever o retorno do investimento (ROI) futuro de diversos orçamentos de publicidade em diferentes canais, incluindo pesquisa, vídeo, redes sociais e email usando o Snowpark para Python e scikit-learn. Depois, você criou uma aplicação Streamlit que usa esse modelo para gerar previsões de novas alocações de orçamento com base nos dados inseridos pelo usuário.\u003C/p\u003E\n","\u003Cp\u003EAdoraríamos saber sua opinião sobre este quickstart guide! Preencha este \u003Ca href=\"https://forms.gle/XKd8rXPUNs2G1yM28\"\u003Eformulário de feedback\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EVocê aprendeu a\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAnalisar dados e executar tarefas de engenharia de dados usando DataFrames e APIs do Snowpark.\u003C/li\u003E\u003Cli\u003EUsar bibliotecas de código aberto em Python de um canal Anaconda selecionado do Snowflake.\u003C/li\u003E\u003Cli\u003ETreinar um modelo de ML usando o Snowpark ML no Snowflake.\u003C/li\u003E\u003Cli\u003ECriar funções definidas pelo usuário (UDFs) em Python do tipo escalar e vetorizada no Snowpark, para inferência online e offline respectivamente.\u003C/li\u003E\u003Cli\u003ECriar Snowflake Tasks para automatizar pipelines de dados e (re)treinar modelos.\u003C/li\u003E\u003Cli\u003ECriar uma aplicação web Streamlit que usa a UDF escalar para inferência.\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 fonte no GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAvançado: guia de engenharia de dados com Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAvançado: guia de aprendizado de máquina com 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\"\u003EDemonstrações do Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/python/index\"\u003EGuia do desenvolvedor de Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/\"\u003EDocumentação Streamlit\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Introdução à engenharia de dados e ML com Snowpark para Python","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Visão geral\r\n\r\n\r\nAo completar este guia, você poderá criar uma aplicação interativa a partir de dados brutos para ajudar uma organização a otimizar a alocação de recursos para publicidade.\r\n\r\nConfira aqui um resumo do que você vai aprender em cada etapa do quickstart:\r\n\r\n- **Configuração do ambiente**: usar estágios e tabelas para ingerir e organizar dados brutos do S3 dentro do Snowflake.\r\n- **Engenharia de dados**: usar os DataFrames do Snowpark para Python para executar transformações de dados, como agrupar, agregar, dinamizar e combinar, para preparar os dados para aplicações mais adiante no processo.\r\n- **Pipelines de dados**: usar o Snowflake Tasks para transformar o código do seu pipeline de dados em pipelines operacionais com monitoramento integrado.  \r\n- **Aprendizado de máquina**: preparar dados e executar treinamento de aprendizado de máquina (machine learning, ML) no Snowflake com o Snowpark ML e implementar o modelo como uma função definida por usuário (user-defined-function, UDF) do Snowpark.\r\n- **Aplicação Streamlit**: desenvolver uma aplicação interativa usando Python (sem precisar de experiência com desenvolvimento web) para ajudar a visualizar o retorno do investimento (ROI) em diferentes orçamentos de publicidade.\r\n\r\nCaso você não conheça algumas tecnologias mencionadas acima, confira a seguir um breve resumo com links para documentação.\r\n\r\n### O que é o Snowpark?\r\n\r\nTrata-se do conjunto de bibliotecas e sistemas de runtime (tempo de execução) do Snowflake que implementam e processam códigos não SQL de forma segura, incluindo Python, Java e Scala.\r\n\r\n**Bibliotecas conhecidas dos clientes**: o Snowpark oferece interfaces de programação de aplicativos (application programming interface, APIs) totalmente integradas, com programação no estilo DataFrame e compatíveis com sistemas de suporte operacional (operational support system, OSS) nas linguagens que os operadores de dados gostam de usar. Ele também conta com a API Snowpark ML, para uma modelagem de aprendizado de máquina (machine learning, ML) (em versão preliminar pública) e operações de ML (em versão preliminar privada) mais eficientes.\r\n\r\n**Estrutura de runtime flexível**: o Snowpark oferece estruturas de runtime flexíveis que permitem aos usuários inserir e executar uma lógica personalizada. Os desenvolvedores podem criar pipelines de dados, modelos de ML e aplicações de dados com facilidade, utilizando funções definidas pelo usuário e procedimentos armazenados.\r\n\r\nSaiba mais sobre o [Snowpark](/snowpark/).\r\n\r\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/snowpark.png)\r\n\r\n### O que é o Snowpark ML?\r\n\r\nO Snowpark ML é uma nova biblioteca que permite um desenvolvimento de ML completo, mais ágil e intuitivo no Snowflake. Ele conta com duas APIs: Snowpark ML Modeling (em versão preliminar pública) para desenvolvimento de modelos e Snowpark ML Operations (em versão preliminar privada) para implementação de modelos.\r\n\r\nEste quickstart é voltado para a API Snowpark ML Modeling, que expande a engenharia de recursos e simplifica a execução do treinamento de ML no Snowflake.\r\n\r\n### O que é o Streamlit?\r\n\r\nÉ uma estrutura de aplicação de [código aberto](https://github.com/streamlit/streamlit) em Python que permite aos desenvolvedores criar, compartilhar e implementar aplicações de dados de forma rápida e simples. Saiba mais sobre o [Streamlit](https://streamlit.io/).\r\n\r\n### Você vai aprender como\r\n\r\n- Analisar dados e executar tarefas de engenharia de dados usando DataFrames e APIs do Snowpark.\r\n- Usar bibliotecas de código aberto em Python de um canal Anaconda selecionado do Snowflake.\r\n- Treinar um modelo de ML usando o Snowpark ML no Snowflake.\r\n- Criar funções definidas pelo usuário (user-defined functions, UDFs) em Python do tipo escalar e vetorizada no Snowpark, para inferência online e offline respectivamente.\r\n- Criar Snowflake Tasks para automatizar pipelines de dados.\r\n- Criar uma aplicação web Streamlit que usa UDF escalar para inferência baseada nos dados inseridos pelo usuário.\r\n\r\n### Pré-requisitos\r\n\r\n- Ter o [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) instalado.\r\n- Ter o [Python 3.9](https://www.python.org/downloads/) instalado.\r\n  - Você vai criar um ambiente Python com a versão 3.9 na etapa **Introdução**.\r\n- Uma conta Snowflake com [pacotes Anaconda habilitados pelo ORGADMIN](https://docs.snowflake.com/pt/developer-guide/udf/python/udf-python-packages#using-third-party-packages-from-anaconda). Caso você não possua uma conta Snowflake, inscreva-se em uma [conta de avaliação gratuita](https://signup.snowflake.com/?lang=pt-br).\r\n- Um login da conta Snowflake com a função ACCOUNTADMIN. Se você tiver essa função no seu ambiente, pode optar por usá-la. Caso contrário, será necessário: \r\n1) Inscrever-se em uma avaliação gratuita; \r\n2) Usar uma função diferente capaz de criar banco de dados, esquema, tabelas, estágios, tarefas, funções definidas pelo usuário e procedimentos armazenados; OU \r\n3) Usar um banco de dados e esquema existentes onde você possa criar os objetos mencionados.\r\n\r\n\u003E aside positive IMPORTANTE: antes de continuar, é preciso ter uma conta Snowflake com pacotes Anaconda habilitados pelo ORGADMIN como descrito [aqui](https://docs.snowflake.com/pt/developer-guide/udf/python/udf-python-packages#getting-started).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Configuração do ambiente\r\n\r\n\r\n### Criação de tabelas, carregamento de dados e configuração de estágios\r\n\r\nAcesse o [Snowsight](https://docs.snowflake.com/pt/user-guide/ui-snowsight.html#) com suas credenciais para criar tabelas, carregar dados do Amazon S3 e configurar estágios internos do Snowflake.\r\n\r\n\u003E aside positive IMPORTANTE:\r\n\u003E\r\n\u003E - Se você usar nomes diferentes para os objetos criados nesta seção, atualize os scripts e o código nas seções a seguir conforme necessário.\r\n\u003E\r\n\u003E - Para cada bloco de script SQL abaixo, escolha as instruções no bloco e execute-as do início ao fim.\r\n\r\nExecute os comandos SQL a seguir para criar um [armazenamento](https://docs.snowflake.com/pt/sql-reference/sql/create-warehouse), um [banco de dados](https://docs.snowflake.com/pt/sql-reference/sql/create-database) e um [esquema](https://docs.snowflake.com/pt/sql-reference/sql/create-schema).\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\nExecute os comandos SQL a seguir para criar a tabela **CAMPAIGN_SPEND** a partir dos dados hospedados no compartimento do S3 acessível ao 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\nExecute os comandos SQL a seguir para criar a tabela **MONTHLY_REVENUE** a partir dos dados no hospedados no compartimento do S3 acessível ao 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\nExecute os comandos SQL a seguir para criar a tabela **BUDGET_ALLOCATIONS_AND_ROI** que contém os orçamentos alocados e o retorno do investimento (ROI) dos 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)\r\nVALUES \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\nExecute os comandos a seguir para criar os [estágios internos](https://docs.snowflake.com/pt/user-guide/data-load-local-file-system-create-stage) do Snowflake para armazenar os procedimentos armazenados, as UDFs e os arquivos de modelo 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\nSe preferir, você pode abrir o [setup.sql](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql) no Snowsight e executar todas as instruções SQL para criar os objetos e carregar os dados do AWS S3.\r\n\r\n\u003E aside positive IMPORTANTE: se você usar nomes diferentes para os objetos criados nesta seção, atualize os scripts e o código nas seções a seguir conforme necessário.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Introdução\r\n\r\n\r\nEsta seção aborda a clonagem do repositório do GitHub e a configuração do ambiente Snowpark para Python.\r\n\r\n### Clonagem do repositório do GitHub\r\n\r\nO primeiro passo é clonar o [repositório do GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn). Esse repositório contém todo o código necessário para completar este quickstart guide com sucesso.\r\n\r\nUsando 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\nOU usando 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 concluir as etapas de **Engenharia de dados** e **Aprendizado de máquina**, você pode instalar tudo localmente (opção 1) ou usar o Hex (opção 2) como descrito a seguir.\r\n\r\n\u003E aside positive IMPORTANTE: para executar a **aplicação Streamlit**, você terá que criar um ambiente Python e instalar o Snowpark para Python junto a outras bibliotecas localmente, como descrito em **Instalação local**.\r\n\r\n#### Opção 1 – Instalação local\r\n\r\nEsta opção permite completar todas as etapas deste quickstart guide.\r\n\r\n**Etapa 1:** fazer download e executar o programa de instalação miniconda de [https://conda.io/miniconda.html](https://conda.io/miniconda.html). *(Se preferir, utilize qualquer outro ambiente Python com Python 3.9, por exemplo, [virtualenv](https://virtualenv.pypa.io/en/latest/))*.\r\n\r\n**Etapa 2:** abrir uma nova janela do terminal e executar os seguintes comandos nela.\r\n\r\n**Etapa 3:** criar um ambiente conda em Python 3.9 chamado **snowpark-de-ml**, executando o seguinte comando na janela do 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**Etapa 4:** ativar o ambiente conda **snowpark-de-ml** executando o seguinte comando na janela do terminal.\r\n\r\n```python\r\nconda activate snowpark-de-ml\r\n```\r\n\r\n**Etapa 5:** instalar o Snowpark Python e as demais bibliotecas no ambiente conda **snowpark-de-ml** a partir do [canal Snowflake Anaconda](https://repo.anaconda.com/pkgs/snowflake/), executando o seguinte comando na janela do 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**Etapa 6:** instalar a biblioteca Streamlit no ambiente conda **snowpark-de-ml** executando o seguinte comando na janela do terminal.\r\n\r\n```python\r\npip install streamlit\r\n```\r\n\r\n**Etapa 7:** instalar a biblioteca Snowpark ML no ambiente conda **snowpark-de-ml** executando o seguinte comando na janela do terminal.\r\n\r\n```python\r\npip install snowflake-ml-python\r\n```\r\n\r\n**Etapa 9:** atualizar o [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) com as informações e as credenciais da sua conta Snowflake.\r\n\r\nAqui temos um ***connection.json*** de amostra baseado nos nomes de objeto mencionados na etapa **Configuração do ambiente**.\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 Observação: no parâmetro **account** acima, especifique seu **identificador de conta**, sem incluir o domínio snowflakecomputing.com. O Snowflake o acrescenta automaticamente ao criar a conexão. Para obter mais informações, [consulte a documentação](https://docs.snowflake.com/pt/user-guide/admin-account-identifier).\r\n\r\n#### Opção 2 – Utilização do Hex\r\n\r\nCaso opte por usar sua conta [Hex](https://app.hex.tech/login) ou [criar uma conta de avaliação gratuita de 30 dias](https://app.hex.tech/signup/quickstart-30), então o Snowpark para Python já estará integrado, eliminando a necessidade de criar um ambiente Python e instalar o Snowpark para Python junto das demais bibliotecas no seu notebook. Com isso, você poderá concluir as etapas de **Engenharia de dados** e **Aprendizado de máquina** deste quickstart guide direto no Hex. Consulte as respectivas etapas para obter mais detalhes sobre o carregamento de notebooks de engenharia de dados e aprendizado de máquina no Hex.\r\n\r\n\u003E aside positive IMPORTANTE: para executar a **aplicação Streamlit**, você terá que criar um ambiente Python e instalar o Snowpark para Python junto a outras bibliotecas localmente, conforme descrito acima em **Instalação local**.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Engenharia de dados\r\n\r\n\r\nO notebook do link abaixo aborda as seguintes tarefas de engenharia de dados.\r\n\r\n1) Estabelecer uma conexão segura entre o Snowpark Python e o Snowflake. \r\n2) Carregar dados de tabelas do Snowflake nos DataFrames do Snowpark. \r\n3) Executar uma análise de dados exploratória nos DataFrames do Snowpark. \r\n4) Dinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark. \r\n5) Automatizar as tarefas de pipeline de dados com o Snowflake Tasks.\r\n\r\n### Notebook de engenharia de dados no Jupyter ou Visual Studio Code\r\n\r\nPara começar, siga estas etapas:\r\n\r\n1) Em uma janela do terminal, acesse a seguinte pasta e execute `jupyter notebook` na linha de comando. (Também é possível usar outras ferramentas e ambientes de desenvolvimento integrado [integrated development environment, IDEs], como o Visual Studio Code.)\r\n\r\n2) Abra e execute as células em [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: verifique se o kernel (Python) do notebok Jupyter está definido como ***snowpark-de-ml***, que é o mesmo nome do ambiente criado na etapa **Clonagem do repositório do GitHub**.\r\n\r\n### Notebook de engenharia de dados no Hex\r\n\r\nCaso opte por usar sua conta do [Hex](https://app.hex.tech/login) ou [criar uma conta de avaliação gratuita de 30 dias](https://app.hex.tech/signup/quickstart-30), siga estas etapas para carregar o notebook e criar uma conexão de dados com o Snowflake a partir do Hex.\r\n\r\n1) Importe [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 um projeto na sua conta. Para obter mais informações sobre importação, consulte a [documentação](https://learn.hex.tech/docs/versioning/import-export).\r\n\r\n2) A seguir, em vez de usar [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) para se conectar ao Snowflake, crie uma [conexão de dados](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) e use-a no notebook de engenharia de dados conforme demonstrado abaixo.\r\n\r\n![Conexão de dados do HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/hex_data_connection.png)\r\n\r\n\u003E aside negative Observação: também é possível criar conexões compartilhadas de dados com projetos e usuários no seu espaço de trabalho. Par obter mais informações, consulte a [documentação](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections).\r\n\r\n3) Substitua o próximo snippet de código no notebook.\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## Pipelines de dados\r\n\r\nTambém é possível operacionalizar as transformações de dados na forma de pipelines de dados automatizados executados no Snowflake.\r\n\r\nEm particular, no [notebook de engenharia de dados](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb), há uma seção que mostra como criar e executar transformações de dados de modo opcional como [Snowflake Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro).\r\n\r\nPara fins de referência, aqui estão os snippets de código.\r\n\r\n### **Tarefa raiz / pai (primária)**\r\n\r\nAutomatiza o carregamento de dados de despesas da campanha e a execução de diversas transformações.\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### **Tarefa filho (secundária) / dependente**\r\n\r\nAutomatiza o carregamento de dados de receita mensal, a execução de diversas transformações e a combinação com dados transformados de despesas da campanha.\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 Observação: em ***monthly_revenue_data_pipeline_task*** acima, observe a cláusula **AFTER campaign_spend_data_pipeline_task** que faz dela uma tarefa dependente.\r\n\r\n#### Iniciar tarefas\r\n\r\nO Snowflake Tasks não é iniciado por padrão, então é preciso executar as seguintes instruções para iniciá-lo/retomá-lo.\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 tarefas\r\n\r\nSe você retomar as tarefas acima, suspenda-as para evitar o uso desnecessário de recursos, executando os seguintes 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### Observabilidade das tarefas\r\n\r\nAs tarefas e seus [gráficos acíclicos dirigidos (directed acyclic graphs, DAGs)](https://docs.snowflake.com/pt/user-guide/tasks-intro#dag-of-tasks) podem ser visualizados no [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs), conforme mostrado abaixo.\r\n\r\n---\r\n\r\n![Observabilidade de tarefas](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/snowflake_tasks.png)\r\n\r\n---\r\n\r\n### Notificações de erros para as tarefas\r\n\r\nTambém é possível habilitar notificações por push para um serviço de mensagens na nuvem em caso de erros durante a execução das tarefas. Para obter mais informações, consulte a [documentação](https://docs.snowflake.com/pt/user-guide/tasks-errors).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Aprendizado de máquina\r\n\r\n\r\n\u003E aside negative PRÉ-REQUISITO: concluir as etapas descritas em [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\nO notebook do link abaixo aborda as seguintes tarefas de aprendizado de máquina.\r\n\r\n1) Estabelecer uma conexão segura entre o Snowpark Python e o Snowflake. \r\n2) Carregar recursos e destinos da tabela do Snowflake no DataFrame do Snowpark. \r\n3) Preparar os recursos para treinamento de modelos. \r\n4) Treinar o modelo de ML com Snowpark ML no Snowflake. \r\n5) Criar [funções definidas pelo usuário (UDFs)](https://docs.snowflake.com/pt/developer-guide/snowpark/python/creating-udfs) escalares e vetorizadas (também conhecidas como “em lote”) para inferência de novos pontos de dados e inferência online e offline, respectivamente.\r\n\r\n---\r\n\r\n![Aprendizado de máquina completo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/snowpark_e2e_ml.png)\r\n\r\n---\r\n\r\n### Notebook de aprendizado de máquina no Jupyter ou Visual Studio Code\r\n\r\nPara começar, siga estas etapas:\r\n\r\n1) Em uma janela do terminal, acesse a seguinte pasta e execute `jupyter notebook` na linha de comando. (Também é possível usar outras ferramentas e ambientes de desenvolvimento integrado [integrated development environment, IDEs], como o Visual Studio Code.)\r\n\r\n2) Abra e execute o [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: verifique se o kernel (Python) do notebok Jupyter está definido como ***snowpark-de-ml***, que é o mesmo nome do ambiente criado na etapa **Clonagem do repositório do GitHub**.\r\n\r\n### Notebook de aprendizado de máquina no Hex\r\n\r\nCaso opte por usar sua conta do [Hex](https://app.hex.tech/login) ou [criar uma conta de avaliação gratuita de 30 dias](https://app.hex.tech/signup/quickstart-30), siga estas etapas para carregar o notebook e criar uma conexão de dados com o Snowflake a partir do Hex.\r\n\r\n1) Importe [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 um projeto na sua conta. Para obter mais informações sobre importação, consulte a [documentação](https://learn.hex.tech/docs/versioning/import-export).\r\n\r\n2) A seguir, em vez de usar [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) para se conectar ao Snowflake, crie uma [conexão de dados](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) e use-a no notebook de aprendizado de máquina, como demonstrado abaixo.\r\n\r\n![Conexão de dados do HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/hex_data_connection.png)\r\n\r\n\u003E aside negative Observação: também é possível criar conexões compartilhadas de dados com projetos e usuários no seu espaço de trabalho. Par obter mais informações, consulte a [documentação](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections).\r\n\r\n3) Substitua o próximo snippet de código no notebook.\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## Aplicação Streamlit\r\n\r\n\r\n### Execução local da aplicação Streamlit\r\n\r\nEm uma janela do terminal, acesse esta pasta e use o próximo comando para executar a aplicação 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 em sua máquina.\r\n\r\n```shell\r\nstreamlit run Snowpark_Streamlit_Revenue_Prediction.py\r\n```\r\n\r\nSe tudo correr bem, deve surgir uma janela de navegador com a aplicação carregada, conforme abaixo.\r\n\r\n---\r\n\r\n![Streamlit-App](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/app.png)\r\n\r\n---\r\n\r\n### Execução da aplicação Streamlit no Snowflake – Streamlit-in-Snowflake (SiS)\r\n\r\nCaso você tenha o SiS habilitado em sua conta, siga estas etapas para executar a aplicação no Snowsight, em vez de localmente em sua máquina.\r\n\r\n\u003E aside negative IMPORTANTE: o SiS está em versão preliminar privada em junho de 2023.***\r\n\r\n  1) Clique em **Streamlit Apps** no menu de navegação à esquerda. \r\n2) Clique em **+ Streamlit App** no canto superior direito. \r\n3) Insira o **nome da aplicação**. \r\n4) Selecione **Warehouse** e o **local da aplicação** (banco de dados e esquema) onde você quer criar a aplicação Streamlit. \r\n5) Clique em **Create**. \r\n6) Você receberá um código de uma aplicação Streamlit de exemplo. Agora, abra [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) e copie e cole o código na aplicação Streamlit de exemplo.\r\n 7) Clique em **Run** no canto superior direito.\r\n\r\nSe tudo correr bem, você deverá ver a aplicação no Snowsight conforme abaixo.\r\n\r\n---\r\n\r\n![Streamlit-in-Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ptr/app_sis.png)\r\n\r\n---\r\n\r\n### Salvar dados no Snowflake\r\n\r\nEm ambas as aplicações, ajuste os controles deslizantes de orçamento de publicidade para ver o retorno do investimento (ROI) previsto para essas alocações. Também é possível clicar no botão **Save to Snowflake** para salvar as alocações atuais e o retorno do investimento previsto na tabela BUDGET_ALLOCATIONS_AND_ROI do Snowflake.\r\n\r\n### Diferenças entre as duas aplicações Streamlit\r\n\r\nA principal diferença entre executar a aplicação Streamlit localmente e no Snowflake (SiS) é a forma como você cria e acessa o objeto da sessão.\r\n\r\nNa execução local, você cria e acessa o novo objeto da sessão da seguinte forma:\r\n\r\n```python\r\n# Função para criar uma sessão Snowflake para conectar ao 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\nAo executar no Snowflake (SiS), você acessa o objeto da sessão atual da seguinte forma:\r\n\r\n```python\r\nsession = snowpark.session._get_active_session()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Limpeza\r\n\r\nCaso você tenha iniciado/retomado as duas tarefas `monthly_revenue_data_pipeline_task` e `campaign_spend_data_pipeline_task` durante as seções **Engenharia de dados** ou **Pipelines de dados**, é importante executar os seguintes comandos para suspender essas tarefas e evitar o uso desnecessário de recursos.\r\n\r\nNo notebook que usa a API Snowpark Python:\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\nNo 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## Conclusão e recursos\r\n\r\n\r\nParabéns! Você executou com sucesso tarefas de engenharia de dados e treinou um modelo de regressão linear para prever o retorno do investimento (ROI) futuro de diversos orçamentos de publicidade em diferentes canais, incluindo pesquisa, vídeo, redes sociais e email usando o Snowpark para Python e scikit-learn. Depois, você criou uma aplicação Streamlit que usa esse modelo para gerar previsões de novas alocações de orçamento com base nos dados inseridos pelo usuário.\r\n\r\nAdoraríamos saber sua opinião sobre este quickstart guide! Preencha este [formulário de feedback](https://forms.gle/XKd8rXPUNs2G1yM28).\r\n\r\n### Você aprendeu a\r\n\r\n- Analisar dados e executar tarefas de engenharia de dados usando DataFrames e APIs do Snowpark.\r\n- Usar bibliotecas de código aberto em Python de um canal Anaconda selecionado do Snowflake.\r\n- Treinar um modelo de ML usando o Snowpark ML no Snowflake.\r\n- Criar funções definidas pelo usuário (UDFs) em Python do tipo escalar e vetorizada no Snowpark, para inferência online e offline respectivamente.\r\n- Criar Snowflake Tasks para automatizar pipelines de dados e (re)treinar modelos.\r\n- Criar uma aplicação web Streamlit que usa a UDF escalar para inferência.\r\n\r\n### Recursos relacionados\r\n\r\n- [Código fonte no GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn)\r\n- [Avançado: guia de engenharia de dados com Snowpark para Python](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\r\n- [Avançado: guia de aprendizado de máquina com Snowpark para Python](/en/developers/guides/getting-started-snowpark-machine-learning/)\r\n- [Demonstrações do Snowpark para Python](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\r\n- [Guia do desenvolvedor de Snowpark para Python](https://docs.snowflake.com/pt/developer-guide/snowpark/python/index)\r\n- [Documentação Streamlit](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"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-af4abbbb60","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-d9505dd279",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ad0c6938d3","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-10-07",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-84d889d83f","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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