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------------------------ -->\n","\u003Ch2\u003EAcesso direto a dados de terceiros via Snowflake Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003EDamos as boas-vindas ao quickstart “Tasty Bytes:do zero ao Snowflake”, voltado para colaboração! Neste quickstart, destacaremos como você pode enriquecer seus próprios dados com dados meteorológicos por meio do Snowflake Marketplace. O Snowflake Marketplace é o lugar onde você pode encontrar, testar e comprar dados e aplicações de terceiros sem ter que configurar processos de extração, transformação e carga (extract, transform, load; ETL), pipelines de dados ou integrações.\u003C/p\u003E\n","\u003Ch3\u003EPré-requisitos\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAntes de começar, é necessário ter concluído o \u003Ca href=\"/pt_br/developers/guides/tasty-bytes-introduction-ptbr/\"\u003E\u003Cstrong\u003EQuickstart “Introdução à Tasty Bytes”\u003C/strong\u003E\u003C/a\u003E, que oferece um passo a passo para criar uma conta de avaliação gratuita e implementar os elementos básicos da Tasty Bytes necessários para completar este módulo.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVocê vai aprender\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAcessar o Snowflake Marketplace.\u003C/li\u003E\u003Cli\u003EObter dados da Weather Source na sua conta, em tempo real.\u003C/li\u003E\u003Cli\u003EComo criar um modo de exibição.\u003C/li\u003E\u003Cli\u003ECriar uma função SQL.\u003C/li\u003E\u003Cli\u003EUsar gráficos do Snowsight para explorar insights visuais.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EO que será desenvolvido\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA harmonização de dados próprios de vendas e dados meteorológicos de terceiros.\u003C/li\u003E\u003Cli\u003EConversão simplificada de Fahrenheit para Celsius.\u003C/li\u003E\u003Cli\u003EConversão simplificada de polegadas para milímetros.\u003C/li\u003E\u003Cli\u003EUma introdução sobre como descobrir insights adicionais por meio do Snowflake Marketplace.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECriação de uma planilha e cópia no código SQL\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003ENeste quickstart, vamos acompanhar a história da Tasty Bytes por meio de uma planilha SQL do Snowsight. Esta página servirá como um guia de referência completo com comentários adicionais, imagens e links para a documentação.\u003C/p\u003E\n","\u003Cp\u003ENesta seção vamos mostrar como acessar o Snowflake, criar uma planilha nova, renomeá-la, copiar o código SQL do GitHub e colar o código SQL que usaremos neste quickstart.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Acessar o Snowflake via URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAbra o navegador e insira a URL da sua conta Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 2 - Fazer login no Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAcesse sua conta Snowflake.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/log_into_snowflake.gif\" alt=\"assets/log_into_snowflake.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 3 - Acessar as planilhas\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClique na guia Worksheets na barra de navegação à esquerda.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/worksheet_tab.png\" alt=\"assets/worksheet_tab.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 4 - Criar uma planilha\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDentro da guia Worksheets, clique no botão “+” no canto superior direito do Snowsight e escolha “SQL Worksheet”.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/+_sqlworksheet.png\" alt=\"assets/+_sqlworksheet.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 5 - Renomear uma planilha\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERenomeie a planilha clicando no carimbo de data/hora gerado automaticamente e digite “Tasty Bytes, colaboração”.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/rename_worksheet_tasty_bytes_setup.gif\" alt=\"assets/rename_worksheet_tasty_bytes_setup.gif\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 6 - Acessar o código SQL do quickstart no GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClique no botão abaixo para acessar o arquivo SQL da Tasty Bytes hospedado no GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.sql\"\u003Etb_zts_collaboration.sql\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Ch3\u003EEtapa 7 - Copiar o Setup SQL do GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ENo GitHub, navegue para a direita e clique em “Copy raw contents”. Todas as informações SQL necessárias serão copiadas para sua área de transferência.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 8 - Colar o Setup SQL do GitHub para a sua planilha do Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EVolte para o Snowsight e sua planilha recém-criada e cole (\u003Cem\u003ECMD + V no Mac ou CTRL + V no Windows\u003C/em\u003E) o conteúdo copiado do GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EEtapa 9 - Clicar em Next -->\u003C/h3\u003E\n","\u003Ch2\u003EEstudo dos dias sem nenhuma venda nos dados próprios\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003EOs analistas financeiros da Tasty Bytes alertaram que, ao realizar análises ano a ano, há dias inexplicáveis em diversas cidades em que não houve nenhuma venda nos caminhões. Um exemplo citado foi o de Hamburgo, na Alemanha, em fevereiro de 2022.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Consultar os dados do ponto de venda para observar tendências\u003C/h3\u003E\n","\u003Cp\u003EVamos começar realizando três consultas para definir o contexto de função e armazenamento para \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E e \u003Ccode\u003Etasty_de_wh\u003C/code\u003E. Com o contexto definido, vamos consultar a exibição de análise de dados \u003Ccode\u003Eorders_v\u003C/code\u003E para obter um conjunto de resultados de vendas em Hamburgo, Alemanha, em 2022.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-USE\"\u003E\nSELECT o.date, SUM(o.price) AS daily_sales FROM frostbyte_tasty_bytes.analytics.orders_v o WHERE 1=1 AND o.country = 'Germany' AND o.primary_city = 'Hamburg' AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28' GROUP BY o.date ORDER BY o.date ASC; ```\n\n![assets/3.1.orders_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/3.1.orders_v.png)\n\nCom base no que vemos acima, podemos concordar com os analistas de que não há registros de vendas em alguns dias de fevereiro, então eles realmente identificaram algo. Na próxima seção, veremos se conseguimos analisar melhor o que pode ter acontecido.\n\n### Etapa 2 - Clicar em Next -->\n\n## Obtenção de dados da Weather Source no Snowflake Marketplace\n\n### Visão geral\nCom base no que vimos na seção anterior, parece que não temos dados de vendas de 16 a 21 de fevereiro em Hamburgo, Alemanha. Não há muito que possamos fazer para investigar com os dados próprios, mas provavelmente algo ocorreu aqui. \n        \nUma ideia que podemos explorar imediatamente usando o [Snowflake Marketplace](/pt_br/data-cloud/marketplace/) é verificar se houve alguma condição meteorológica extrema, empregando a oferta de dados pública e gratuita da Weather Source.\n\n### Etapa 1 - Adquirir a oferta da Weather Source LLC: frostbyte no Snowflake Marketplace\nO Snowflake Marketplace é o melhor lugar para procurar, testar e comprar dados e aplicações necessários para desenvolver soluções de negócios inovadoras. Nesta etapa, vamos acessar a ofertar da [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) para ajudar a orientar as análises adicionais sobre a queda nas vendas em Hamburgo.\n\nSiga as etapas e o vídeo abaixo para adquirir a oferta na sua conta Snowflake.\n\n- Clique -> Home\n- Clique -> Marketplace\n- Pesquise -> frostbyte\n- Clique -> Weather Source LLC: frostbyte\n- Clique -> Get\n- Renomeie o banco de dados -> FROSTBYTE_WEATHERSOURCE (tudo em maiúsculas)\n- Permissão para funções adicionais -> PUBLIC\n\n![assets/4.1.acquire_weather_source.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.1.acquire_weather_source.gif)\n\n>aside positive A Weather Source é uma fornecedora líder global de dados de meteorologia, e seu OnPoint Product Suite oferece às empresas os dados meteorológicos necessários para geração de insights relevantes e úteis para diversos casos de uso em diversos setores.\n>\n\n### Etapa 2 - Harmonizar os dados próprios e de terceiros\nCom o banco de dados `frostbyte_weathersource` já criado, execute a consulta desta etapa para criar uma exibição `harmonized.daily_weather_v` que junte duas tabelas da Weather Source à nossa tabela de países e cidades onde os food trucks da Tasty Bytes operam.\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\nAS\nSELECT\nhd.*,\nTO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\npc.city_name AS city,\nc.country AS country_desc\nFROM frostbyte_weathersource.onpoint_id.history_day hd\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\nON pc.postal_code = hd.postal_code\nAND pc.country = hd.country\nJOIN frostbyte_tasty_bytes.raw_pos.country c\nON c.iso_country = hd.country\nAND c.city = hd.city_name;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/4.2.daily_weather_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.2.daily_weather_v.png)\n\nComo podemos ver na definição do modo de exibição acima, estamos juntando duas das tabelas `frostbyte_weathersource` dentro do esquema `onpoint_id` e harmonizando-as com a nossa tabela `country` do banco de dados `frostbyte_tasty_bytes` e o esquema `raw_pos`. \n\nEsse é o tipo de operação que encontramos normalmente na camada harmonizada, ou que poderia ser descrita como zona Silver (Prata).\n\n### Etapa 3 - Visualizar as temperaturas diárias\nCom a exibição `daily_weather_v` do nosso esquema harmonizado ativa, vamos observar a temperatura diária média de Hamburgo em fevereiro de 2022 executando a seguinte consulta.\n\nAo fazer isso, usaremos as funções [AVG](https://docs.snowflake.com/pt/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/pt/sql-reference/functions/year) e [MONTH](https://docs.snowflake.com/pt/sql-reference/functions/year).\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESELECT\ndw.country_desc,\ndw.city_name,\ndw.date_valid_std,\nAVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\nAND dw.country_desc = 'Germany'\nAND dw.city_name = 'Hamburg'\nAND YEAR(date_valid_std) = '2022'\nAND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/4.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.3.results.png)\n\nPara investigar melhor as tendências, vamos usar os gráficos do Snowsight para criar um gráfico de linha com a temperatura média no período de tempo.\n\n![assets/4.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.3.chart.png)\n\nCom base no que vimos acima, não há nada que se destaque como causa dos dias sem vendas dos nossos food trucks. Veremos na próxima etapa o que mais podemos descobrir para explicar o que aconteceu.\n\n### Etapa 4 - Adicionar dados sobre as condições de vento\nComo visto na etapa anterior, não parece que a temperatura média diária tenha sido o motivo para os dias sem vendas em Hamburgo. Felizmente, a Weather Source fornece outras métricas de clima que podemos analisar também. \n\nExecute a próxima consulta, onde usaremos a exibição harmonizada para agregar métricas com as condições de vento. Para esta consulta, usaremos a função [MAX](https://docs.snowflake.com/pt/sql-reference/functions/min).\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESELECT\ndw.country_desc,\ndw.city_name,\ndw.date_valid_std,\nMAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\nWHERE 1=1\nAND dw.country_desc IN ('Germany')\nAND dw.city_name = 'Hamburg'\nAND YEAR(date_valid_std) = '2022'\nAND MONTH(date_valid_std) = '2'\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/4.4.result.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.4.result.png)\n\nNovamente, as tendências desse tipo de dados podem ser melhor apresentadas por meio de um gráfico rápido do Snowsight. Na captura de tela abaixo, siga as setas para ir de Results para Charts.\n\n![assets/4.4.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.4.chart.png)\n\n**Veja só!** As condições de vento nos dias sem vendas estavam em níveis de furacão. Esse parece ser um bom motivo por que nossos food trucks não realizaram nenhuma venda nesses dias. Como executamos essa análise na seção harmonizada, agora começaremos o processo de disponibilizar na parte de análise de dados, onde os analistas poderão acessar esses insights por conta própria.\n\n### Etapa 5 - Clicar em Next -->\n\n## Democratização dos insights de dados\n\n### Visão geral\nJá determinamos que os ventos de nível de furacão foram, provavelmente, a causa para não haver nenhuma venda nos dias indicados pelos analistas financeiros.\n\nAgora, vamos disponibilizar esse tipo de pesquisa a todos os membros da organização, implementando uma exibição de análise de dados que todos os colaboradores da Tasty Bytes podem acessar.\n\n### Etapa 1 - Criar as funções SQL\nComo somos uma empresa global, vamos começar nosso processo criando duas funções SQL para converter Fahrenheit em Celsius e polegadas em milímetros. \n\nExecute as duas consultas desta etapa, uma de cada vez, para criar as funções `fahrenheit_to_celsius` e `inch_to_millimeter`, que empregam o comando [CREATE FUNCTION](https://docs.snowflake.com/pt/sql-reference/sql/create-function).\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\nRETURNS NUMBER(35,4)\nAS\n$$\n(temp_f - 32) * (5/9)\n$$;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/5.1.f_to_c.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.1.f_to_c.png)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\nRETURNS NUMBER(35,4)\nAS\n$$\ninch * 25.4\n$$;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/5.1.inch_to_mm.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.1.inch_to_mm.png)\n\n>aside positive Ao criar uma função definida pelo usuário (user-defined function, UDF), você especifica um manipulador cujo código é escrito em uma das linguagens compatíveis. De acordo com a linguagem do manipulador, você pode incluir o código fonte dele em linha com a instrução CREATE FUNCTION ou fazer referência à localização do manipulador a partir de CREATE FUNCTION, onde o manipulador é pré-compilado ou o código fonte é extraído de um estágio.\n>\n\n### Etapa 2 - Criar o SQL para nosso modo de exibição\nAntes de implementar o modo de exibição de análise de dados, vamos criar o SQL que usaremos na exibição para combinar os dados de vendas diárias e clima, bem como aproveitar as funções de conversão do SQL. \n\nExecute a próxima consulta, com aplicação do filtro para Hamburgo, Alemanha, onde usamos algumas funções que ainda não vimos, como [ZEROIFNULL](https://docs.snowflake.com/pt/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/pt/sql-reference/functions/round) e [DATE](https://docs.snowflake.com/pt/sql-reference/functions/to_date).\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESELECT\nfd.date_valid_std AS date,\nfd.city_name,\nfd.country_desc,\nZEROIFNULL(SUM(odv.price)) AS daily_sales,\nROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\nROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\nROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\nROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\nMAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\nON fd.date_valid_std = DATE(odv.order_ts)\nAND fd.city_name = odv.primary_city\nAND fd.country_desc = odv.country\nWHERE 1=1\nAND fd.country_desc = 'Germany'\nAND fd.city = 'Hamburg'\nAND fd.yyyy_mm = '2022-02'\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\nORDER BY fd.date_valid_std ASC;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/5.2.SQL.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.2.SQL.png)\n\n\nOs resultados parecem ótimos. Agora podemos colocar esse SQL em uma exibição na próxima etapa.\n\n### Etapa 3 - Implementar o modo de exibição de análise de dados\nUsando a mesma consulta que acabamos de estudar, vamos precisar remover os filtros da cláusula WHERE, adicionar [COMMENT](https://docs.snowflake.com/pt/sql-reference/sql/comment) e promovê-la em nosso esquema `analytics` como a exibição `daily_city_metrics_v`.\n\nPara isso, execute a última consulta desta seção.\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\nAS\nSELECT\nfd.date_valid_std AS date,\nfd.city_name,\nfd.country_desc,\nZEROIFNULL(SUM(odv.price)) AS daily_sales,\nROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\nROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\nROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\nROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\nMAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\nON fd.date_valid_std = DATE(odv.order_ts)\nAND fd.city_name = odv.primary_city\nAND fd.country_desc = odv.country\nWHERE 1=1\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/5.3.view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.3.view.png)\n\nÓtimo, acabamos de democratizar esses insights para toda a organização Tasty Bytes. Na próxima seção, vamos combinar todo o conhecimento aprendido e validar nosso trabalho.\n\n### Etapa 4 - Clicar em Next -->\n\n## Obtenção de insights a partir de dados de vendas e clima do marketplace\n\n### Visão geral\nCom os dados disponíveis de vendas e clima de todas as cidades atendidas pelos nossos food trucks, vamos observar como reduzimos o tempo que os analistas financeiros levam para obter os insights.\n\n### Etapa 1 - Simplificar a análise\nAnteriormente, tivemos que combinar manualmente os dados de pontos de venda com os da Weather Source para investigar os problemas nas vendas em Hamburgo, mas o processo foi simplificado com a exibição `analytics.daily_city_metrics_v`. \n\nExecute a próxima consulta, que mostra como simplificamos essa análise ao fazer dela uma simples instrução Select a partir de uma única exibição.\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESELECT\ndcm.date,\ndcm.city_name,\ndcm.country_desc,\ndcm.daily_sales,\ndcm.avg_temperature_fahrenheit,\ndcm.avg_temperature_celsius,\ndcm.avg_precipitation_inches,\ndcm.avg_precipitation_millimeters,\ndcm.max_wind_speed_100m_mph\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\nWHERE 1=1\nAND dcm.country_desc = 'Germany'\nAND dcm.city_name = 'Hamburg'\nAND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\nORDER BY date DESC;\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E\n![assets/6.1.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/6.1.results.png)\n\n**Viva!** Se isso já estivesse disponível quando os analistas financeiros estavam conduzindo a pesquisa inicial, eles nem precisariam ter falado com a equipe de dados, pois os insights já estariam ali. \n\nAo finalizar este quickstart, vimos como é simples derivar o valor comercial real com nosso trabalho e como é fácil usar o Snowflake Marketplace para descobrir insights adicionais de dados.\n\n### Etapa 2 - Clicar em Next -->\n\n## Conclusão e próximas etapas\n\n### Conclusão\nÓtimo trabalho! Você concluiu com sucesso o quickstart “Tasty Bytes: do zero ao Snowflake - colaboração”. \n\nNele, você: \n- Acessou o Snowflake Marketplace. \n- Obteve dados da Weather Source na sua conta, em tempo real. \n- Criou um modo de exibição. \n- Criou uma função SQL. \n- Usou os gráficos do Snowsight para explorar insights visuais.\n\nCaso queira refazer este quickstart, use os scripts de Reset que aparecem no final da planilha associada.\n\n### Próximas etapas\nPara continuar sua jornada no Snowflake Data Cloud, clique no link abaixo para ver mais quickstarts Powered By Tasty Bytes disponíveis para você.\n\n- ### [Powered by Tasty Bytes - Índice de quickstarts](/pt_br/developers/guides/tasty-bytes-introduction-ptbr/)\u003C/code\u003E\u003C/pre\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Acesso direto a dados de terceiros via Snowflake Marketplace\r\n\r\n### Visão geral\r\nDamos as boas-vindas ao quickstart “Tasty Bytes:do zero ao Snowflake”, voltado para colaboração! Neste quickstart, destacaremos como você pode enriquecer seus próprios dados com dados meteorológicos por meio do Snowflake Marketplace. O Snowflake Marketplace é o lugar onde você pode encontrar, testar e comprar dados e aplicações de terceiros sem ter que configurar processos de extração, transformação e carga (extract, transform, load; ETL), pipelines de dados ou integrações.\r\n\r\n\r\n### Pré-requisitos\r\n- Antes de começar, é necessário ter concluído o [**Quickstart “Introdução à Tasty Bytes”**](/pt_br/developers/guides/tasty-bytes-introduction-ptbr/), que oferece um passo a passo para criar uma conta de avaliação gratuita e implementar os elementos básicos da Tasty Bytes necessários para completar este módulo.\r\n\r\n### Você vai aprender\r\n- Acessar o Snowflake Marketplace.\r\n- Obter dados da Weather Source na sua conta, em tempo real.\r\n- Como criar um modo de exibição.\r\n- Criar uma função SQL.\r\n- Usar gráficos do Snowsight para explorar insights visuais.\r\n\r\n### O que será desenvolvido\r\n- A harmonização de dados próprios de vendas e dados meteorológicos de terceiros.\r\n- Conversão simplificada de Fahrenheit para Celsius.\r\n- Conversão simplificada de polegadas para milímetros.\r\n- Uma introdução sobre como descobrir insights adicionais por meio do Snowflake Marketplace.\r\n\r\n## Criação de uma planilha e cópia no código SQL\r\n\r\n### Visão geral\r\nNeste quickstart, vamos acompanhar a história da Tasty Bytes por meio de uma planilha SQL do Snowsight. Esta página servirá como um guia de referência completo com comentários adicionais, imagens e links para a documentação.\r\n\r\nNesta seção vamos mostrar como acessar o Snowflake, criar uma planilha nova, renomeá-la, copiar o código SQL do GitHub e colar o código SQL que usaremos neste quickstart.\r\n\r\n### Etapa 1 - Acessar o Snowflake via URL\r\n- Abra o navegador e insira a URL da sua conta Snowflake. \r\n\r\n### Etapa 2 - Fazer login no Snowflake\r\n- Acesse sua conta Snowflake.\r\n    - ![assets/log_into_snowflake.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/log_into_snowflake.gif)\r\n\r\n### Etapa 3 - Acessar as planilhas\r\n- Clique na guia Worksheets na barra de navegação à esquerda.\r\n    - ![assets/worksheet_tab.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/worksheet_tab.png)\r\n\r\n### Etapa 4 - Criar uma planilha\r\n- Dentro da guia Worksheets, clique no botão “+” no canto superior direito do Snowsight e escolha “SQL Worksheet”.\r\n    - ![assets/+_sqlworksheet.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/+_sqlworksheet.png)\r\n\r\n### Etapa 5 - Renomear uma planilha\r\n- Renomeie a planilha clicando no carimbo de data/hora gerado automaticamente e digite “Tasty Bytes, colaboração”.\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/rename_worksheet_tasty_bytes_setup.gif)\r\n\r\n### Etapa 6 - Acessar o código SQL do quickstart no GitHub\r\n- Clique no botão abaixo para acessar o arquivo SQL da Tasty Bytes hospedado no GitHub.\r\n\r\n\u003Cbutton\u003E[tb_zts_collaboration.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_collaboration.sql)\u003C/button\u003E\r\n\r\n### Etapa 7 - Copiar o Setup SQL do GitHub\r\n- No GitHub, navegue para a direita e clique em “Copy raw contents”. Todas as informações SQL necessárias serão copiadas para sua área de transferência.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/github_copy_raw_contents.png)\r\n\r\n### Etapa 8 - Colar o Setup SQL do GitHub para a sua planilha do Snowflake\r\n- Volte para o Snowsight e sua planilha recém-criada e cole (*CMD + V no Mac ou CTRL + V no Windows*) o conteúdo copiado do GitHub.\r\n\r\n### Etapa 9 - Clicar em Next --\u003E\r\n\r\n## Estudo dos dias sem nenhuma venda nos dados próprios\r\n\r\n### Visão geral\r\nOs analistas financeiros da Tasty Bytes alertaram que, ao realizar análises ano a ano, há dias inexplicáveis em diversas cidades em que não houve nenhuma venda nos caminhões. Um exemplo citado foi o de Hamburgo, na Alemanha, em fevereiro de 2022.\r\n\r\n\r\n### Etapa 1 - Consultar os dados do ponto de venda para observar tendências\r\nVamos começar realizando três consultas para definir o contexto de função e armazenamento para `tasty_data_engineer` e `tasty_de_wh`. Com o contexto definido, vamos consultar a exibição de análise de dados `orders_v` para obter um conjunto de resultados de vendas em Hamburgo, Alemanha, em 2022.\r\n\r\n``` USE ROLE tasty_data_engineer; USE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT o.date, SUM(o.price) AS daily_sales FROM frostbyte_tasty_bytes.analytics.orders_v o WHERE 1=1 AND o.country = 'Germany' AND o.primary_city = 'Hamburg' AND DATE(o.order_ts) BETWEEN '2022-02-10' AND '2022-02-28' GROUP BY o.date ORDER BY o.date ASC; ```\r\n\r\n![assets/3.1.orders_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/3.1.orders_v.png)\r\n\r\nCom base no que vemos acima, podemos concordar com os analistas de que não há registros de vendas em alguns dias de fevereiro, então eles realmente identificaram algo. Na próxima seção, veremos se conseguimos analisar melhor o que pode ter acontecido.\r\n\r\n### Etapa 2 - Clicar em Next --\u003E\r\n\r\n## Obtenção de dados da Weather Source no Snowflake Marketplace\r\n\r\n### Visão geral\r\nCom base no que vimos na seção anterior, parece que não temos dados de vendas de 16 a 21 de fevereiro em Hamburgo, Alemanha. Não há muito que possamos fazer para investigar com os dados próprios, mas provavelmente algo ocorreu aqui. \r\n        \r\nUma ideia que podemos explorar imediatamente usando o [Snowflake Marketplace](/pt_br/data-cloud/marketplace/) é verificar se houve alguma condição meteorológica extrema, empregando a oferta de dados pública e gratuita da Weather Source.\r\n\r\n### Etapa 1 - Adquirir a oferta da Weather Source LLC: frostbyte no Snowflake Marketplace\r\nO Snowflake Marketplace é o melhor lugar para procurar, testar e comprar dados e aplicações necessários para desenvolver soluções de negócios inovadoras. Nesta etapa, vamos acessar a ofertar da [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) para ajudar a orientar as análises adicionais sobre a queda nas vendas em Hamburgo.\r\n\r\nSiga as etapas e o vídeo abaixo para adquirir a oferta na sua conta Snowflake.\r\n\r\n- Clique -\u003E Home\r\n- Clique -\u003E Marketplace\r\n- Pesquise -\u003E frostbyte\r\n- Clique -\u003E Weather Source LLC: frostbyte\r\n- Clique -\u003E Get\r\n- Renomeie o banco de dados -\u003E FROSTBYTE_WEATHERSOURCE (tudo em maiúsculas)\r\n- Permissão para funções adicionais -\u003E PUBLIC\r\n\r\n![assets/4.1.acquire_weather_source.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.1.acquire_weather_source.gif)\r\n\r\n\u003Easide positive A Weather Source é uma fornecedora líder global de dados de meteorologia, e seu OnPoint Product Suite oferece às empresas os dados meteorológicos necessários para geração de insights relevantes e úteis para diversos casos de uso em diversos setores.\r\n\u003E\r\n\r\n### Etapa 2 - Harmonizar os dados próprios e de terceiros\r\nCom o banco de dados `frostbyte_weathersource` já criado, execute a consulta desta etapa para criar uma exibição `harmonized.daily_weather_v` que junte duas tabelas da Weather Source à nossa tabela de países e cidades onde os food trucks da Tasty Bytes operam.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.daily_weather_v\r\n    AS\r\nSELECT \r\n    hd.*,\r\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\r\n    pc.city_name AS city,\r\n    c.country AS country_desc\r\nFROM frostbyte_weathersource.onpoint_id.history_day hd\r\nJOIN frostbyte_weathersource.onpoint_id.postal_codes pc\r\n    ON pc.postal_code = hd.postal_code\r\n    AND pc.country = hd.country\r\nJOIN frostbyte_tasty_bytes.raw_pos.country c\r\n    ON c.iso_country = hd.country\r\n    AND c.city = hd.city_name;\r\n```\r\n\r\n![assets/4.2.daily_weather_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.2.daily_weather_v.png)\r\n\r\nComo podemos ver na definição do modo de exibição acima, estamos juntando duas das tabelas `frostbyte_weathersource` dentro do esquema `onpoint_id` e harmonizando-as com a nossa tabela `country` do banco de dados `frostbyte_tasty_bytes` e o esquema `raw_pos`. \r\n\r\nEsse é o tipo de operação que encontramos normalmente na camada harmonizada, ou que poderia ser descrita como zona Silver (Prata).\r\n\r\n### Etapa 3 - Visualizar as temperaturas diárias\r\nCom a exibição `daily_weather_v` do nosso esquema harmonizado ativa, vamos observar a temperatura diária média de Hamburgo em fevereiro de 2022 executando a seguinte consulta.\r\n\r\nAo fazer isso, usaremos as funções [AVG](https://docs.snowflake.com/pt/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/pt/sql-reference/functions/year) e [MONTH](https://docs.snowflake.com/pt/sql-reference/functions/year).\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc = 'Germany'\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.3.results.png)\r\n\r\nPara investigar melhor as tendências, vamos usar os gráficos do Snowsight para criar um gráfico de linha com a temperatura média no período de tempo.\r\n\r\n![assets/4.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.3.chart.png)\r\n\r\nCom base no que vimos acima, não há nada que se destaque como causa dos dias sem vendas dos nossos food trucks. Veremos na próxima etapa o que mais podemos descobrir para explicar o que aconteceu.\r\n\r\n### Etapa 4 - Adicionar dados sobre as condições de vento\r\nComo visto na etapa anterior, não parece que a temperatura média diária tenha sido o motivo para os dias sem vendas em Hamburgo. Felizmente, a Weather Source fornece outras métricas de clima que podemos analisar também. \r\n\r\nExecute a próxima consulta, onde usaremos a exibição harmonizada para agregar métricas com as condições de vento. Para esta consulta, usaremos a função [MAX](https://docs.snowflake.com/pt/sql-reference/functions/min).\r\n\r\n```\r\nSELECT \r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc IN ('Germany')\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2'\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/4.4.result.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.4.result.png)\r\n\r\nNovamente, as tendências desse tipo de dados podem ser melhor apresentadas por meio de um gráfico rápido do Snowsight. Na captura de tela abaixo, siga as setas para ir de Results para Charts.\r\n\r\n![assets/4.4.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/4.4.chart.png)\r\n\r\n**Veja só!** As condições de vento nos dias sem vendas estavam em níveis de furacão. Esse parece ser um bom motivo por que nossos food trucks não realizaram nenhuma venda nesses dias. Como executamos essa análise na seção harmonizada, agora começaremos o processo de disponibilizar na parte de análise de dados, onde os analistas poderão acessar esses insights por conta própria.\r\n\r\n### Etapa 5 - Clicar em Next --\u003E\r\n\r\n## Democratização dos insights de dados\r\n\r\n### Visão geral\r\nJá determinamos que os ventos de nível de furacão foram, provavelmente, a causa para não haver nenhuma venda nos dias indicados pelos analistas financeiros.\r\n\r\nAgora, vamos disponibilizar esse tipo de pesquisa a todos os membros da organização, implementando uma exibição de análise de dados que todos os colaboradores da Tasty Bytes podem acessar.\r\n\r\n### Etapa 1 - Criar as funções SQL\r\nComo somos uma empresa global, vamos começar nosso processo criando duas funções SQL para converter Fahrenheit em Celsius e polegadas em milímetros. \r\n\r\nExecute as duas consultas desta etapa, uma de cada vez, para criar as funções `fahrenheit_to_celsius` e `inch_to_millimeter`, que empregam o comando [CREATE FUNCTION](https://docs.snowflake.com/pt/sql-reference/sql/create-function).\r\n\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\nAS\r\n$$\r\n    (temp_f - 32) * (5/9)\r\n$$;\r\n```\r\n\r\n![assets/5.1.f_to_c.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.1.f_to_c.png)\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION frostbyte_tasty_bytes.analytics.inch_to_millimeter(inch NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\n    AS\r\n$$\r\n    inch * 25.4\r\n$$;\r\n```\r\n\r\n![assets/5.1.inch_to_mm.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.1.inch_to_mm.png)\r\n\r\n\u003Easide positive Ao criar uma função definida pelo usuário (user-defined function, UDF), você especifica um manipulador cujo código é escrito em uma das linguagens compatíveis. De acordo com a linguagem do manipulador, você pode incluir o código fonte dele em linha com a instrução CREATE FUNCTION ou fazer referência à localização do manipulador a partir de CREATE FUNCTION, onde o manipulador é pré-compilado ou o código fonte é extraído de um estágio.\r\n\u003E\r\n\r\n### Etapa 2 - Criar o SQL para nosso modo de exibição\r\nAntes de implementar o modo de exibição de análise de dados, vamos criar o SQL que usaremos na exibição para combinar os dados de vendas diárias e clima, bem como aproveitar as funções de conversão do SQL. \r\n\r\nExecute a próxima consulta, com aplicação do filtro para Hamburgo, Alemanha, onde usamos algumas funções que ainda não vimos, como [ZEROIFNULL](https://docs.snowflake.com/pt/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/pt/sql-reference/functions/round) e [DATE](https://docs.snowflake.com/pt/sql-reference/functions/to_date).\r\n\r\n```\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\n    AND fd.country_desc = 'Germany'\r\n    AND fd.city = 'Hamburg'\r\n    AND fd.yyyy_mm = '2022-02'\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\r\nORDER BY fd.date_valid_std ASC;\r\n```\r\n\r\n![assets/5.2.SQL.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.2.SQL.png)\r\n\r\n\r\nOs resultados parecem ótimos. Agora podemos colocar esse SQL em uma exibição na próxima etapa.\r\n\r\n### Etapa 3 - Implementar o modo de exibição de análise de dados\r\nUsando a mesma consulta que acabamos de estudar, vamos precisar remover os filtros da cláusula WHERE, adicionar [COMMENT](https://docs.snowflake.com/pt/sql-reference/sql/comment) e promovê-la em nosso esquema `analytics` como a exibição `daily_city_metrics_v`.\r\n\r\nPara isso, execute a última consulta desta seção.\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.analytics.daily_city_metrics_v\r\nCOMMENT = 'Daily Weather Source Metrics and Orders Data for our Cities'\r\n    AS\r\nSELECT \r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(frostbyte_tasty_bytes.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.harmonized.daily_weather_v fd\r\nLEFT JOIN frostbyte_tasty_bytes.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\r\n```\r\n\r\n![assets/5.3.view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/5.3.view.png)\r\n\r\nÓtimo, acabamos de democratizar esses insights para toda a organização Tasty Bytes. Na próxima seção, vamos combinar todo o conhecimento aprendido e validar nosso trabalho.\r\n\r\n### Etapa 4 - Clicar em Next --\u003E\r\n\r\n## Obtenção de insights a partir de dados de vendas e clima do marketplace\r\n\r\n### Visão geral\r\nCom os dados disponíveis de vendas e clima de todas as cidades atendidas pelos nossos food trucks, vamos observar como reduzimos o tempo que os analistas financeiros levam para obter os insights.\r\n\r\n### Etapa 1 - Simplificar a análise\r\nAnteriormente, tivemos que combinar manualmente os dados de pontos de venda com os da Weather Source para investigar os problemas nas vendas em Hamburgo, mas o processo foi simplificado com a exibição `analytics.daily_city_metrics_v`. \r\n\r\nExecute a próxima consulta, que mostra como simplificamos essa análise ao fazer dela uma simples instrução Select a partir de uma única exibição.\r\n\r\n```\r\nSELECT \r\n    dcm.date,\r\n    dcm.city_name,\r\n    dcm.country_desc,\r\n    dcm.daily_sales,\r\n    dcm.avg_temperature_fahrenheit,\r\n    dcm.avg_temperature_celsius,\r\n    dcm.avg_precipitation_inches,\r\n    dcm.avg_precipitation_millimeters,\r\n    dcm.max_wind_speed_100m_mph\r\nFROM frostbyte_tasty_bytes.analytics.daily_city_metrics_v dcm\r\nWHERE 1=1\r\n    AND dcm.country_desc = 'Germany'\r\n    AND dcm.city_name = 'Hamburg'\r\n    AND dcm.date BETWEEN '2022-02-01' AND '2022-02-26'\r\nORDER BY date DESC;\r\n```\r\n\r\n![assets/6.1.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration-ptbr/6.1.results.png)\r\n\r\n**Viva!** Se isso já estivesse disponível quando os analistas financeiros estavam conduzindo a pesquisa inicial, eles nem precisariam ter falado com a equipe de dados, pois os insights já estariam ali. \r\n\r\nAo finalizar este quickstart, vimos como é simples derivar o valor comercial real com nosso trabalho e como é fácil usar o Snowflake Marketplace para descobrir insights adicionais de dados.\r\n\r\n### Etapa 2 - Clicar em Next --\u003E\r\n\r\n## Conclusão e próximas etapas\r\n\r\n### Conclusão\r\nÓtimo trabalho! Você concluiu com sucesso o quickstart “Tasty Bytes: do zero ao Snowflake - colaboração”. \r\n\r\nNele, você: \r\n- Acessou o Snowflake Marketplace. \r\n- Obteve dados da Weather Source na sua conta, em tempo real. \r\n- Criou um modo de exibição. \r\n- Criou uma função SQL. \r\n- Usou os gráficos do Snowsight para explorar insights visuais.\r\n\r\nCaso queira refazer este quickstart, use os scripts de Reset que aparecem no final da planilha associada.\r\n\r\n### Próximas etapas\r\nPara continuar sua jornada no Snowflake Data Cloud, clique no link abaixo para ver mais quickstarts Powered By Tasty Bytes disponíveis para você.\r\n\r\n- ### [Powered by Tasty Bytes - Índice de quickstarts](/pt_br/developers/guides/tasty-bytes-introduction-ptbr/)","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-9c1e2641f7","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":{"id":"container-f303562427","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-da775b190c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-05-30",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6348d98e90","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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direto a dados de terceiros via Snowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003ECriação de uma planilha e cópia no código SQL\u003C/h2\u003E","\u003Ch2\u003EEstudo dos dias sem nenhuma venda nos dados próprios\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-5d5c22ce42","fragmentPath":"/content/dam/snowflake-site/pt_br/content-fragments/quickstarts/tasty-bytes-zero-to-snowflake-collaboration-ptbr",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-c93a0bbbcf","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E 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aem-GridColumn--default--12"},"id":"container-8ce692ad76","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"text":{"id":"text-82aa54136e","additionalClasses":"sf-footer__newsletter-title","text":"\u003Cp\u003E\u003Cb\u003EInscreva-se em nosso boletim informativo\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EReceba as últimas informações da Snowflake sobre produtos, insights de especialistas, direto em sua caixa postal.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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