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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-a4500fd76d","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Tasty Bytes: do zero ao Snowflake - dados semiestruturados"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Jacob 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------------------------ -->\n","\u003Ch2\u003EProcessamento de dados semiestruturados no Snowflake\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003EDamos as boas-vindas ao quickstart “Powered by Tasty Bytes: do zero ao Snowflake”, voltado para o processamento de dados semiestruturados!\u003C/p\u003E\n","\u003Cp\u003ENeste quickstart, vamos aprender sobre o processamento de dados semiestruturados no Snowflake. Vamos aprofundar o conhecimento sobre o tipo de dados VARIANT, o processamento de dados semiestruturados em combinação de notação de pontos com nivelamento lateral, bem como a criação de exibições e gráficos no Snowsight.\u003C/p\u003E\n","\u003Cp\u003EPara obter mais informações sobre dados semiestruturados no Snowflake, consulte a documentação \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/semistructured-concepts\"\u003Evisão geral de dados semiestruturados\u003C/a\u003E.\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\u003EO que é o tipo de dados VARIANT.\u003C/li\u003E\u003Cli\u003EComo usar notação de pontos para consultar dados semiestruturados.\u003C/li\u003E\u003Cli\u003EComo nivelar dados semiestruturados.\u003C/li\u003E\u003Cli\u003EComo analisar matrizes.\u003C/li\u003E\u003Cli\u003EComo criar um modo de exibição.\u003C/li\u003E\u003Cli\u003EComo criar um gráfico a partir de um conjunto de resultados no Snowsight.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EO que será desenvolvido\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUm processamento de dados semiestruturados de ponta a ponta.\u003C/li\u003E\u003Cli\u003EUma modo de exibição harmonizado (Silver) e outro de análise de dados (Gold).\u003C/li\u003E\u003Cli\u003EUm gráfico de barras de nível executivo.\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-semi-structured-data-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-semi-structured-data-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-semi-structured-data-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, dados semiestruturados”.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-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. <button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql\"\u003Etb_zts_semi_structured_data.sql\u003C/a\u003E</button>\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\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-semi-structured-data-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\u003ECriação dos perfis de dados semiestruturados do menu\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003EEnquanto engenheiros de dados da Tasty Bytes, recebemos a tarefa de criar o perfil dos nossos dados de menu, que incluem uma coluna de dados semiestruturados. A partir desta tabela de menu, precisamos criar uma exibição da camada de análise de dados que apresente as restrições alimentares e os ingredientes aos usuários finais.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Definir o contexto e consultar a tabela\u003C/h3\u003E\n","\u003Cp\u003EPara começar, vamos executar as três primeiras consultas juntas, que vão:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDefinir o contexto de função para \u003Ccode\u003Etasty_data_engineer\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EDefinir o contexto de armazenamento para \u003Ccode\u003Etasty_de_wh\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EGerar um conjunto de resultados do tipo \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/constructs/top_n\"\u003ETOP\u003C/a\u003E 10 da nossa tabela. \u003Ccode\u003Eraw_pos.menu\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tasty_data_engineer; \nUSE WAREHOUSE tasty_de_wh;\n\nSELECT TOP 10 \n    m.truck_brand_name, \n    m.menu_type, \n    m.menu_item_name, \n    m.menu_item_health_metrics_obj \nFROM frostbyte_tasty_bytes.raw_pos.menu m; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.1.menu.png\" alt=\"assets/3.1.menu.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENo resultado, podemos ver que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E deve mostrar os dados semiestruturados que contêm as métricas que precisamos fornecer mais adiante no processo.\u003C/p\u003E\n","\u003Cp\u003EAo clicar em uma das células nesta coluna, o Snowsight vai expandir automaticamente o painel de estatísticas para que vejamos melhor o conteúdo.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.1.2.stats.png\" alt=\"assets/3.1.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEtapa 2 - Explorar a coluna de dados semiestruturados\u003C/h3\u003E\n","\u003Cp\u003EPara analisar em mais detalhes como esta coluna é definida no Snowflake, execute a próxima consulta que usa \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/show-columns\"\u003ESHOW COLUMNS\u003C/a\u003E para descobrir os tipos de dados presentes na tabela \u003Ccode\u003Emenu\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.2.show_columns.png\" alt=\"assets/3.2.show_columns.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAo observar o conjunto de resultados, vamos notar que \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E é do tipo de dados \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/data-types-semistructured\"\u003EVARIANT\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive Para dados que são em sua maioria regulares e usam apenas tipos de dados nativos do formato semiestruturado que você está usando (por exemplo, cadeias de caracteres e inteiros no formato JSON), os requisitos de armazenamento e o desempenho de consulta para operações em dados relacionais e dados em uma coluna VARIANT são muito semelhantes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EEtapa 3 - Percorrer dados semiestruturados usando a notação de pontos\u003C/h3\u003E\n","\u003Cp\u003ENa coluna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E, vimos que \u003Ccode\u003Emenu_item_id\u003C/code\u003E estava incluído entre os dados aninhados de ingredientes e restrições alimentares que precisamos acessar.\u003C/p\u003E\n","\u003Cp\u003EExecute a consulta a seguir, em que começamos a usar a \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/querying-semistructured#dot-notation\"\u003Enotação de pontos\u003C/a\u003E para percorrer os dados semiestruturados.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.3.dot.png\" alt=\"assets/3.3.dot.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAo usar a notação de pontos, podemos extrair os dados completamente \u003Ccode\u003Emenu_item_id\u003C/code\u003E, mas parece que ainda há objetos semiestruturados no resultado da coluna \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EVamos clicar novamente em uma das células da coluna para analisar melhor.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.3.2.stats.png\" alt=\"assets/3.3.2.stats.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEstamos progredindo!\u003C/strong\u003E Vamos ver como processar ainda mais a coluna \u003Ccode\u003Emenu_item_health_metrics\u003C/code\u003E na próxima seção usando funções adicionais do Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 4 - Clicar em Next -->\u003C/h3\u003E\n","\u003Ch2\u003ENivelamento de dados semiestruturados\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003EDepois de ver como é fácil consultar os dados semiestruturados em uma coluna Variant usando a notação de pontos, nosso engenheiro de dados da Tasty já pode fornecer os dados solicitados aos colaboradores internos.\u003C/p\u003E\n","\u003Cp\u003ENesta seção, vamos realizar um processamento adicional dos dados semiestruturados para atender aos requisitos.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Introdução ao nivelamento lateral\u003C/h3\u003E\n","\u003Cp\u003EPara extrair da coluna \u003Ccode\u003Emenu_item_health_metrics_obj\u003C/code\u003E dados mais detalhados solicitados pelos usuários, execute a consulta a seguir. Ela usa a notação de pontos junto da função \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/flatten\"\u003EFLATTEN\u003C/a\u003E do Snowflake e o recurso \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/constructs/join-lateral\"\u003ELATERAL JOIN\u003C/a\u003E para nos fornecer a primeira matriz \u003Ccode\u003Eingredient\u003C/code\u003E solicitada.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive \u003Cstrong\u003EFlatten:\u003C/strong\u003E é uma função de tabela que usa uma coluna VARIANT, OBJECT ou ARRAY para criar uma exibição lateral. Flatten pode ser usada para converter dados semiestruturados em uma representação relacional.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELateral Join:\u003C/strong\u003E diferente do resultado de uma junção não lateral, o resultado de uma junção lateral inclui apenas as linhas geradas a partir da exibição em linha. Não é necessário unir as linhas do lado esquerdo às do lado direito, pois as linhas da esquerda já foram incluídas ao serem passadas para a exibição em linha.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.1.lat_flat.png\" alt=\"assets/4.1.lat_flat.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEtapa 2 - Explorar de uma função de matriz\u003C/h3\u003E\n","\u003Cp\u003EAntes de extrair os dados de restrições alimentares solicitados, execute a próxima consulta, que destaca as funções de matriz do Snowflake, que explorará a coluna \u003Ccode\u003Eingredients\u003C/code\u003E em busca de qualquer \u003Ccode\u003Emenu_item_name\u003C/code\u003E que inclua “alface” (“lettuce”, se os dados estiverem em inglês) usando \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/array_contains\"\u003EARRAY_CONTAINS\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:"ingredients"::VARIANT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.2.array_contains.png\" alt=\"assets/4.2.array_contains.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECom base nos resultados, vemos que muitos dos itens do menu contêm “alface” (“lettuce”). Esse tipo de análise pode ser bem útil para os gerentes de compras de cadeia de suprimentos no caso de recalls alimentares realizados em cidades e países onde atuamos.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 3 - Estruturar dados semiestruturados em escala\u003C/h3\u003E\n","\u003Cp\u003ETendo acabado de ver o valor que podemos agregar à organização, é hora de executar a última consulta desta seção.\u003C/p\u003E\n","\u003Cp\u003EEsta consulta vai usar a notação de pontos e combinar as funções de tabela LATERAL JOIN e FLATTEN para gerar o conjunto de resultados solicitado inicialmente.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.3.lat_flat_2.png\" alt=\"assets/4.3.lat_flat_2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMaravilha!\u003C/strong\u003E O resultado parece atender exatamente aos requisitos solicitados por nossos colaboradores. Na seção a seguir veremos como levar os resultados à nossa camada de análise de dados, onde eles poderão ser acessados.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 4 - Clicar em Next -->\u003C/h3\u003E\n","\u003Ch2\u003ECriar modos de exibição estruturados com dados semiestruturados\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003ENa última seção, criamos uma consulta que fornece o resultado exato solicitado por usuários finais ao usar uma série de funcionalidades do Snowflake para dados semiestruturados. A seguir, vamos dar continuidade ao processo de promover essa consulta com base na camada bruta, passando pela camada harmonizada, até chegar à camada de análise de dados, para que os usuários finais possam ler os resultados.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EObservação:\u003C/strong\u003E aos usuários mais familiarizados com modelos de dados Bronze, Silver e Gold, podemos pensar na camada bruta como Bronze, a camada harmonizada como Silver e a de análise de dados como Gold.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Criar um modo de exibição harmonizado com o SQL de nivelamento semiestruturado\u003C/h3\u003E\n","\u003Cp\u003ECom base na consulta que encerrou a última seção, execute agora a próxima consulta que inclui este SQL e as colunas adicionais da tabela \u003Ccode\u003Emenu\u003C/code\u003E já estruturadas.\u003C/p\u003E\n","\u003Cp\u003ENessa consulta, usaremos \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/create-view\"\u003ECREATE VIEW\u003C/a\u003E em nosso esquema harmonizado para encapsular a lógica de processamento e colunas adicionais semiestruturadas como tabela.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive Um modo de exibição permite que o resultado de uma consulta seja acessado como se fosse uma tabela. As exibições têm muitas finalidades, incluindo combinação, separação e proteção de dados.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\n    AS\nSELECT \n    m.menu_id,\n    m.menu_type_id,\n    m.menu_type,\n    m.truck_brand_name,\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\n    m.menu_item_name,\n    m.item_category,\n    m.item_subcategory,\n    m.cost_of_goods_usd,\n    m.sale_price_usd,\n    obj.value:"ingredients"::VARIANT AS ingredients,\n    obj.value:"is_healthy_flag"::VARCHAR(1) AS is_healthy_flag,\n    obj.value:"is_gluten_free_flag"::VARCHAR(1) AS is_gluten_free_flag,\n    obj.value:"is_dairy_free_flag"::VARCHAR(1) AS is_dairy_free_flag,\n    obj.value:"is_nut_free_flag"::VARCHAR(1) AS is_nut_free_flag\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\n    LATERAL FLATTEN (input => m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/5.1.harmonized_view.png\" alt=\"assets/5.1.harmonized_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDentro da exibição \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E criada, agora é possível executar consultas sem usar o SQL complexo de antes. Considerando que a camada de análise de dados é onde nossos colaboradores acessam dados, o próximo passo é levar tudo até lá.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 2 - Promover da camada de harmonização para a análise de dados com facilidade\u003C/h3\u003E\n","\u003Cp\u003EConsultando o \u003Ccode\u003Eharmonized.menu_v\u003C/code\u003E, é hora de executar a próxima consulta que vai criar \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E e agregar valor imediatamente ao processo.\u003C/p\u003E\n","\u003Cp\u003ENesta consulta, veremos algumas funções novas, ainda não abordadas. Primeiro adicionamos um \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E, que pode ser visto dentro do comando \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/show\"\u003ESHOW VIEWS\u003C/a\u003E ou na interface do Snowsight para documentar o que um usuário pode ver ao consultar essa exibição.\u003C/p\u003E\n","\u003Cp\u003ETambém usaremos os parâmetros \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/sql/select#parameters\"\u003ESELECT * EXCLUDE e RENAME\u003C/a\u003E que podem facilitar o trabalho dos desenvolvedores de SQL ao simplificar a definição de consultas e as exibições.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive \u003Cstrong\u003EExclude:\u003C/strong\u003E quando você seleciona todas as colunas (SELECT * ou SELECT table_name.*), EXCLUDE especifica as colunas que devem ser excluídas dos resultados.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERename:\u003C/strong\u003E ao selecionar todas as colunas (SELECT * ou SELECT table_name.*), especifique os apelidos (alias) das colunas que devem ser usados nos resultados.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/5.2.analytics_view.png\" alt=\"assets/5.2.analytics_view.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EEtapa 3 - Clicar em Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAnálise de dados semiestruturados processados no Snowsight\u003C/h2\u003E\n","\u003Ch3\u003EVisão geral\u003C/h3\u003E\n","\u003Cp\u003ECom nossa exibição de menu disponível na camada de análise de dados, vamos executar algumas consultas que forneceremos aos usuários finais, demonstrando como o Snowflake permite uma experiência de consulta relacional sobre dados semiestruturados sem a necessidade de cópias adicionais ou processamento complexo.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 1 - Analisar as matrizes\u003C/h3\u003E\n","\u003Cp\u003ENa seção anterior, obtivemos a coluna \u003Ccode\u003Eingredients\u003C/code\u003E como resultado da consulta. Agora, ela está disponível sem a necessidade de notação de pontos no modo de exibição \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ECom isso, execute a próxima consulta que emprega duas funções de matriz Snowflake adicionais \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/array_intersection\"\u003EARRAY_INTERSECTION\u003C/a\u003E e \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/arrays_overlap\"\u003EARRAYS_OVERLAP\u003C/a\u003E para verificar quais itens do menu não relacionados a bebidas coincidem em cada um dos menus de nossas marcas de food trucks.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT \n    m1.menu_type,\n    m1.menu_item_name,\n    m2.menu_type AS overlap_menu_type,\n    m2.menu_item_name AS overlap_menu_item_name,\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\n    ON m1.menu_item_id <> m2.menu_item_id -- avoid joining the same menu item to itself\n    AND m1.menu_type <> m2.menu_type \nWHERE 1=1\n    AND m1.item_category <> 'Beverage' -- remove beverages\n    AND m2.item_category <> 'Beverage' -- remove beverages\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\nORDER BY m1.menu_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.1.arrays.png\" alt=\"assets/6.1.arrays.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDe novo, ao utilizar o mesmo modo de exibição, criamos esse tipo de consulta capaz de agregar valor real de negócios aos operadores de food trucks, pois eles poderiam à medida que trabalham com o setor de compras prever e fazer pedidos de ingredientes semanais.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 2 - Fornecer métricas aos executivos\u003C/h3\u003E\n","\u003Cp\u003EAgora vamos ver como ajudar os executivos da Tasty Bytes a tomarem decisões baseadas em dados para os menus ao fornecer métricas de alto nível relativas às restrições alimentares que utilizamos atualmente em nossas marcas.\u003C/p\u003E\n","\u003Cp\u003EExecute a consulta a seguir, que usa \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/count\"\u003ECOUNT\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/sum\"\u003ESUM\u003C/a\u003E e instruções condicionais \u003Ca href=\"https://docs.snowflake.com/pt/sql-reference/functions/case\"\u003ECASE\u003C/a\u003E para agregar a métricas exigidas do \u003Ccode\u003Eanalytics.menu_v\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.2.metrics.png\" alt=\"assets/6.2.metrics.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECom o resultado obtido, saímos de uma tabela bruta com dados semiestruturados para uma única linha agregada facilmente acessível a qualquer usuário da organização, dando autonomia à Tasty Bytes para que se baseie cada vez mais em dados.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 3 - Transformar os resultados em gráficos\u003C/h3\u003E\n","\u003Cp\u003EAlguns executivos da Tasty Bytes preferem representações visuais dos dados. Então, vamos ver como é simples transformar tabelas em gráficos de fácil compreensão dentro do Snowsight.\u003C/p\u003E\n","\u003Cp\u003EExecute a próxima consulta que vai adicionar filtros a três de nossas marcas de food trucks ao SQL executado anteriormente.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    m.brand_name,\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\nFROM frostbyte_tasty_bytes.analytics.menu_v m\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\nGROUP BY m.brand_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.3.results.png\" alt=\"assets/6.3.results.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPor padrão, o Snowsight exibe os resultados em formato tabular. Porém, um recurso avançado do Snowsight, que ainda não abordamos, é o de \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/ui-snowsight-visualizations#using-charts\"\u003Euso de gráficos\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ENa captura de tela abaixo, siga as setas para criar seu próprio gráfico de barras comparando estas diferentes marcas de food trucks em termos de itens do menu que atendem a certas restrições alimentares.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.3.chart.png\" alt=\"assets/6.3.chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPara terminar esse quickstart, devemos ressaltar a facilidade com que um executivo da Tasty Bytes poderia fazer esse tipo de análise sozinho sem precisar conhecer processamento de dados semiestruturados que encapsulamos nas exibições criadas. Sendo assim, podemos estar certos de que estamos contribuindo para a democratização dos dados na Tasty Bytes.\u003C/p\u003E\n","\u003Ch3\u003EEtapa 4 - Clicar em Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusão e próximas etapas\u003C/h2\u003E\n","\u003Ch3\u003EConclusão\u003C/h3\u003E\n","\u003Cp\u003EÓtimo trabalho! Você concluiu com sucesso o quickstart “Tasty Bytes: do zero ao Snowflake - dados semiestruturados”.\u003C/p\u003E\n","\u003Cp\u003ENeste módulo, você:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAprendeu o que são os dados do tipo Variant.\u003C/li\u003E\u003Cli\u003EUsou a notação de pontos para consultar dados semiestruturados.\u003C/li\u003E\u003Cli\u003ENivelou dados semiestruturados.\u003C/li\u003E\u003Cli\u003EAnalisou matrizes.\u003C/li\u003E\u003Cli\u003ECriou dois modos de exibição.\u003C/li\u003E\u003Cli\u003ECriou um gráfico a partir de um conjunto de resultados no Snowsight.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECaso queira refazer este quickstart, use os scripts de Reset que aparecem no final da planilha associada.\u003C/p\u003E\n","\u003Ch3\u003EPróximas etapas\u003C/h3\u003E\n","\u003Cp\u003EPara continuar sua jornada no Snowflake Data Cloud, clique no link abaixo para ver mais quickstarts Powered By Tasty Bytes disponíveis para você.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/pt_br/developers/guides/tasty-bytes-introduction-ptbr/\"\u003EPowered by Tasty Bytes - Índice de quickstarts\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Tasty Bytes: do zero ao Snowflake - dados semiestruturados","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n\r\n## Processamento de dados semiestruturados no Snowflake\r\n\r\n### Visão geral\r\nDamos as boas-vindas ao quickstart “Powered by Tasty Bytes: do zero ao Snowflake”, voltado para o processamento de dados semiestruturados!\r\n\r\nNeste quickstart, vamos aprender sobre o processamento de dados semiestruturados no Snowflake. Vamos aprofundar o conhecimento sobre o tipo de dados VARIANT, o processamento de dados semiestruturados em combinação de notação de pontos com nivelamento lateral, bem como a criação de exibições e gráficos no Snowsight.\r\n\r\nPara obter mais informações sobre dados semiestruturados no Snowflake, consulte a documentação [visão geral de dados semiestruturados](https://docs.snowflake.com/pt/user-guide/semistructured-concepts).\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- O que é o tipo de dados VARIANT.\r\n- Como usar notação de pontos para consultar dados semiestruturados.\r\n- Como nivelar dados semiestruturados.\r\n- Como analisar matrizes.\r\n- Como criar um modo de exibição.\r\n- Como criar um gráfico a partir de um conjunto de resultados no Snowsight.\r\n\r\n### O que será desenvolvido\r\n- Um processamento de dados semiestruturados de ponta a ponta.\r\n- Uma modo de exibição harmonizado (Silver) e outro de análise de dados (Gold).\r\n- Um gráfico de barras de nível executivo.\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-semi-structured-data-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-semi-structured-data-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-semi-structured-data-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, dados semiestruturados”.\r\n    - ![assets/rename_worksheet_tasty_bytes_setup.gif](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-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. \u003Cbutton\u003E[tb_zts_semi_structured_data.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/tb_zts_semi_structured_data.sql)\u003C/button\u003E\r\n\r\n### 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-semi-structured-data-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## Criação dos perfis de dados semiestruturados do menu\r\n\r\n### Visão geral\r\nEnquanto engenheiros de dados da Tasty Bytes, recebemos a tarefa de criar o perfil dos nossos dados de menu, que incluem uma coluna de dados semiestruturados. A partir desta tabela de menu, precisamos criar uma exibição da camada de análise de dados que apresente as restrições alimentares e os ingredientes aos usuários finais.\r\n\r\n### Etapa 1 - Definir o contexto e consultar a tabela\r\nPara começar, vamos executar as três primeiras consultas juntas, que vão: \r\n- Definir o contexto de função para `tasty_data_engineer`. \r\n- Definir o contexto de armazenamento para `tasty_de_wh`. \r\n- Gerar um conjunto de resultados do tipo [TOP](https://docs.snowflake.com/pt/sql-reference/constructs/top_n) 10 da nossa tabela. `raw_pos.menu`\r\n\r\n``` \r\nUSE ROLE tasty_data_engineer; \r\nUSE WAREHOUSE tasty_de_wh;\r\n\r\nSELECT TOP 10 \r\n    m.truck_brand_name, \r\n    m.menu_type, \r\n    m.menu_item_name, \r\n    m.menu_item_health_metrics_obj \r\nFROM frostbyte_tasty_bytes.raw_pos.menu m; \r\n```\r\n\r\n![assets/3.1.menu.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.1.menu.png)\r\n\r\nNo resultado, podemos ver que `menu_item_health_metrics_obj` deve mostrar os dados semiestruturados que contêm as métricas que precisamos fornecer mais adiante no processo. \r\n\r\nAo clicar em uma das células nesta coluna, o Snowsight vai expandir automaticamente o painel de estatísticas para que vejamos melhor o conteúdo.\r\n\r\n![assets/3.1.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.1.2.stats.png)\r\n\r\n### Etapa 2 - Explorar a coluna de dados semiestruturados\r\nPara analisar em mais detalhes como esta coluna é definida no Snowflake, execute a próxima consulta que usa [SHOW COLUMNS](https://docs.snowflake.com/pt/sql-reference/sql/show-columns) para descobrir os tipos de dados presentes na tabela `menu`.\r\n\r\n```\r\nSHOW COLUMNS IN frostbyte_tasty_bytes.raw_pos.menu;\r\n```\r\n\r\n![assets/3.2.show_columns.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.2.show_columns.png)\r\n\r\nAo observar o conjunto de resultados, vamos notar que `menu_item_health_metrics_obj` é do tipo de dados [VARIANT](https://docs.snowflake.com/pt/sql-reference/data-types-semistructured).\r\n\r\n\u003Easide positive Para dados que são em sua maioria regulares e usam apenas tipos de dados nativos do formato semiestruturado que você está usando (por exemplo, cadeias de caracteres e inteiros no formato JSON), os requisitos de armazenamento e o desempenho de consulta para operações em dados relacionais e dados em uma coluna VARIANT são muito semelhantes.\r\n\u003E\r\n\r\n### Etapa 3 - Percorrer dados semiestruturados usando a notação de pontos\r\nNa coluna `menu_item_health_metrics_obj`, vimos que `menu_item_id` estava incluído entre os dados aninhados de ingredientes e restrições alimentares que precisamos acessar. \r\n\r\nExecute a consulta a seguir, em que começamos a usar a [notação de pontos](https://docs.snowflake.com/pt/user-guide/querying-semistructured#dot-notation) para percorrer os dados semiestruturados.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id AS menu_item_id,\r\n    m.menu_item_health_metrics_obj:menu_item_health_metrics AS menu_item_health_metrics\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m;\r\n```\r\n\r\n![assets/3.3.dot.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.3.dot.png)\r\n\r\nAo usar a notação de pontos, podemos extrair os dados completamente `menu_item_id`, mas parece que ainda há objetos semiestruturados no resultado da coluna `menu_item_health_metrics`. \r\n\r\nVamos clicar novamente em uma das células da coluna para analisar melhor.\r\n\r\n![assets/3.3.2.stats.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/3.3.2.stats.png)\r\n\r\n**Estamos progredindo!** Vamos ver como processar ainda mais a coluna `menu_item_health_metrics` na próxima seção usando funções adicionais do Snowflake.\r\n\r\n### Etapa 4 - Clicar em Next --\u003E\r\n\r\n## Nivelamento de dados semiestruturados\r\n\r\n### Visão geral\r\nDepois de ver como é fácil consultar os dados semiestruturados em uma coluna Variant usando a notação de pontos, nosso engenheiro de dados da Tasty já pode fornecer os dados solicitados aos colaboradores internos.\r\n\r\nNesta seção, vamos realizar um processamento adicional dos dados semiestruturados para atender aos requisitos.\r\n\r\n### Etapa 1 - Introdução ao nivelamento lateral\r\nPara extrair da coluna `menu_item_health_metrics_obj` dados mais detalhados solicitados pelos usuários, execute a consulta a seguir. Ela usa a notação de pontos junto da função [FLATTEN](https://docs.snowflake.com/pt/sql-reference/functions/flatten) do Snowflake e o recurso [LATERAL JOIN](https://docs.snowflake.com/pt/sql-reference/constructs/join-lateral) para nos fornecer a primeira matriz `ingredient` solicitada.\r\n\r\n\u003Easide positive **Flatten:** é uma função de tabela que usa uma coluna VARIANT, OBJECT ou ARRAY para criar uma exibição lateral. Flatten pode ser usada para converter dados semiestruturados em uma representação relacional.\r\n\u003E\r\n\u003E**Lateral Join:** diferente do resultado de uma junção não lateral, o resultado de uma junção lateral inclui apenas as linhas geradas a partir da exibição em linha. Não é necessário unir as linhas do lado esquerdo às do lado direito, pois as linhas da esquerda já foram incluídas ao serem passadas para a exibição em linha. \r\n\u003E\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.1.lat_flat.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.1.lat_flat.png)\r\n\r\n### Etapa 2 - Explorar de uma função de matriz\r\nAntes de extrair os dados de restrições alimentares solicitados, execute a próxima consulta, que destaca as funções de matriz do Snowflake, que explorará a coluna `ingredients` em busca de qualquer `menu_item_name` que inclua “alface” (“lettuce”, se os dados estiverem em inglês) usando [ARRAY_CONTAINS](https://docs.snowflake.com/pt/sql-reference/functions/array_contains).\r\n\r\n```\r\nSELECT \r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj\r\nWHERE ARRAY_CONTAINS('Lettuce'::VARIANT, obj.value:\"ingredients\"::VARIANT);\r\n```\r\n\r\n![assets/4.2.array_contains.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.2.array_contains.png)\r\n\r\nCom base nos resultados, vemos que muitos dos itens do menu contêm “alface” (“lettuce”). Esse tipo de análise pode ser bem útil para os gerentes de compras de cadeia de suprimentos no caso de recalls alimentares realizados em cidades e países onde atuamos.\r\n\r\n### Etapa 3 - Estruturar dados semiestruturados em escala\r\nTendo acabado de ver o valor que podemos agregar à organização, é hora de executar a última consulta desta seção. \r\n\r\nEsta consulta vai usar a notação de pontos e combinar as funções de tabela LATERAL JOIN e FLATTEN para gerar o conjunto de resultados solicitado inicialmente.\r\n\r\n```\r\nSELECT \r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/4.3.lat_flat_2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/4.3.lat_flat_2.png)\r\n\r\n**Maravilha!** O resultado parece atender exatamente aos requisitos solicitados por nossos colaboradores. Na seção a seguir veremos como levar os resultados à nossa camada de análise de dados, onde eles poderão ser acessados.\r\n\r\n### Etapa 4 - Clicar em Next --\u003E\r\n\r\n## Criar modos de exibição estruturados com dados semiestruturados\r\n\r\n### Visão geral\r\nNa última seção, criamos uma consulta que fornece o resultado exato solicitado por usuários finais ao usar uma série de funcionalidades do Snowflake para dados semiestruturados. A seguir, vamos dar continuidade ao processo de promover essa consulta com base na camada bruta, passando pela camada harmonizada, até chegar à camada de análise de dados, para que os usuários finais possam ler os resultados.\r\n\r\n**Observação:** aos usuários mais familiarizados com modelos de dados Bronze, Silver e Gold, podemos pensar na camada bruta como Bronze, a camada harmonizada como Silver e a de análise de dados como Gold.\r\n\r\n### Etapa 1 - Criar um modo de exibição harmonizado com o SQL de nivelamento semiestruturado\r\nCom base na consulta que encerrou a última seção, execute agora a próxima consulta que inclui este SQL e as colunas adicionais da tabela `menu` já estruturadas. \r\n\r\nNessa consulta, usaremos [CREATE VIEW](https://docs.snowflake.com/pt/sql-reference/sql/create-view) em nosso esquema harmonizado para encapsular a lógica de processamento e colunas adicionais semiestruturadas como tabela.\r\n\r\n\u003Easide positive Um modo de exibição permite que o resultado de uma consulta seja acessado como se fosse uma tabela. As exibições têm muitas finalidades, incluindo combinação, separação e proteção de dados. \r\n\u003E\r\n\r\n```\r\nCREATE OR REPLACE VIEW frostbyte_tasty_bytes.harmonized.menu_v\r\n    AS\r\nSELECT \r\n    m.menu_id,\r\n    m.menu_type_id,\r\n    m.menu_type,\r\n    m.truck_brand_name,\r\n    m.menu_item_health_metrics_obj:menu_item_id::integer AS menu_item_id,\r\n    m.menu_item_name,\r\n    m.item_category,\r\n    m.item_subcategory,\r\n    m.cost_of_goods_usd,\r\n    m.sale_price_usd,\r\n    obj.value:\"ingredients\"::VARIANT AS ingredients,\r\n    obj.value:\"is_healthy_flag\"::VARCHAR(1) AS is_healthy_flag,\r\n    obj.value:\"is_gluten_free_flag\"::VARCHAR(1) AS is_gluten_free_flag,\r\n    obj.value:\"is_dairy_free_flag\"::VARCHAR(1) AS is_dairy_free_flag,\r\n    obj.value:\"is_nut_free_flag\"::VARCHAR(1) AS is_nut_free_flag\r\nFROM frostbyte_tasty_bytes.raw_pos.menu m,\r\n    LATERAL FLATTEN (input =\u003E m.menu_item_health_metrics_obj:menu_item_health_metrics) obj;\r\n```\r\n\r\n![assets/5.1.harmonized_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/5.1.harmonized_view.png)\r\n\r\nDentro da exibição `harmonized.menu_v` criada, agora é possível executar consultas sem usar o SQL complexo de antes. Considerando que a camada de análise de dados é onde nossos colaboradores acessam dados, o próximo passo é levar tudo até lá.\r\n\r\n### Etapa 2 - Promover da camada de harmonização para a análise de dados com facilidade\r\nConsultando o `harmonized.menu_v`, é hora de executar a próxima consulta que vai criar `analytics.menu_v` e agregar valor imediatamente ao processo. \r\n\r\nNesta consulta, veremos algumas funções novas, ainda não abordadas. Primeiro adicionamos um [COMMENT](https://docs.snowflake.com/pt/sql-reference/sql/comment), que pode ser visto dentro do comando [SHOW VIEWS](https://docs.snowflake.com/pt/sql-reference/sql/show) ou na interface do Snowsight para documentar o que um usuário pode ver ao consultar essa exibição.\r\n\r\nTambém usaremos os parâmetros [SELECT * EXCLUDE e RENAME](https://docs.snowflake.com/pt/sql-reference/sql/select#parameters) que podem facilitar o trabalho dos desenvolvedores de SQL ao simplificar a definição de consultas e as exibições.\r\n\r\n\u003Easide positive **Exclude:** quando você seleciona todas as colunas (SELECT * ou SELECT table_name.*), EXCLUDE especifica as colunas que devem ser excluídas dos resultados.\r\n\u003E\r\n\u003E **Rename:** ao selecionar todas as colunas (SELECT * ou SELECT table_name.*), especifique os apelidos (alias) das colunas que devem ser usados nos resultados.\r\n\u003E\r\n\r\n![assets/5.2.analytics_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/5.2.analytics_view.png)\r\n\r\n### Etapa 3 - Clicar em Next --\u003E\r\n\r\n## Análise de dados semiestruturados processados no Snowsight\r\n\r\n### Visão geral\r\nCom nossa exibição de menu disponível na camada de análise de dados, vamos executar algumas consultas que forneceremos aos usuários finais, demonstrando como o Snowflake permite uma experiência de consulta relacional sobre dados semiestruturados sem a necessidade de cópias adicionais ou processamento complexo.\r\n\r\n### Etapa 1 - Analisar as matrizes\r\nNa seção anterior, obtivemos a coluna `ingredients` como resultado da consulta. Agora, ela está disponível sem a necessidade de notação de pontos no modo de exibição `analytics.menu_v`. \r\n\r\nCom isso, execute a próxima consulta que emprega duas funções de matriz Snowflake adicionais [ARRAY_INTERSECTION](https://docs.snowflake.com/pt/sql-reference/functions/array_intersection) e [ARRAYS_OVERLAP](https://docs.snowflake.com/pt/sql-reference/functions/arrays_overlap) para verificar quais itens do menu não relacionados a bebidas coincidem em cada um dos menus de nossas marcas de food trucks.\r\n\r\n```\r\nSELECT \r\n    m1.menu_type,\r\n    m1.menu_item_name,\r\n    m2.menu_type AS overlap_menu_type,\r\n    m2.menu_item_name AS overlap_menu_item_name,\r\n    ARRAY_INTERSECTION(m1.ingredients, m2.ingredients) AS overlapping_ingredients\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m1\r\nJOIN frostbyte_tasty_bytes.analytics.menu_v m2\r\n    ON m1.menu_item_id \u003C\u003E m2.menu_item_id -- avoid joining the same menu item to itself\r\n    AND m1.menu_type \u003C\u003E m2.menu_type \r\nWHERE 1=1\r\n    AND m1.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND m2.item_category \u003C\u003E 'Beverage' -- remove beverages\r\n    AND ARRAYS_OVERLAP(m1.ingredients, m2.ingredients) -- evaluates to TRUE if one ingredient is in both arrays\r\nORDER BY m1.menu_type;\r\n```\r\n\r\n![assets/6.1.arrays.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.1.arrays.png)\r\n\r\nDe novo, ao utilizar o mesmo modo de exibição, criamos esse tipo de consulta capaz de agregar valor real de negócios aos operadores de food trucks, pois eles poderiam à medida que trabalham com o setor de compras prever e fazer pedidos de ingredientes semanais.\r\n\r\n### Etapa 2 - Fornecer métricas aos executivos\r\nAgora vamos ver como ajudar os executivos da Tasty Bytes a tomarem decisões baseadas em dados para os menus ao fornecer métricas de alto nível relativas às restrições alimentares que utilizamos atualmente em nossas marcas.\r\n\r\nExecute a consulta a seguir, que usa [COUNT](https://docs.snowflake.com/pt/sql-reference/functions/count), [SUM](https://docs.snowflake.com/pt/sql-reference/functions/sum) e instruções condicionais [CASE](https://docs.snowflake.com/pt/sql-reference/functions/case) para agregar a métricas exigidas do `analytics.menu_v`.\r\n\r\n```\r\nSELECT\r\n    COUNT(DISTINCT menu_item_id) AS total_menu_items,\r\n    SUM(CASE WHEN is_healthy_flag = 'Y' THEN 1 ELSE 0 END) AS healthy_item_count,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m;\r\n```\r\n\r\n![assets/6.2.metrics.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.2.metrics.png)\r\n\r\nCom o resultado obtido, saímos de uma tabela bruta com dados semiestruturados para uma única linha agregada facilmente acessível a qualquer usuário da organização, dando autonomia à Tasty Bytes para que se baseie cada vez mais em dados. \r\n\r\n\r\n### Etapa 3 - Transformar os resultados em gráficos\r\nAlguns executivos da Tasty Bytes preferem representações visuais dos dados. Então, vamos ver como é simples transformar tabelas em gráficos de fácil compreensão dentro do Snowsight. \r\n\r\nExecute a próxima consulta que vai adicionar filtros a três de nossas marcas de food trucks ao SQL executado anteriormente.\r\n\r\n```\r\nSELECT\r\n    m.brand_name,\r\n    SUM(CASE WHEN is_gluten_free_flag = 'Y' THEN 1 ELSE 0 END) AS gluten_free_item_count,\r\n    SUM(CASE WHEN is_dairy_free_flag = 'Y' THEN 1 ELSE 0 END) AS dairy_free_item_count,\r\n    SUM(CASE WHEN is_nut_free_flag = 'Y' THEN 1 ELSE 0 END) AS nut_free_item_count\r\nFROM frostbyte_tasty_bytes.analytics.menu_v m\r\nWHERE m.brand_name IN  ('Plant Palace', 'Peking Truck','Revenge of the Curds')\r\nGROUP BY m.brand_name;\r\n```\r\n\r\n![assets/6.3.results.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.3.results.png)\r\n\r\nPor padrão, o Snowsight exibe os resultados em formato tabular. Porém, um recurso avançado do Snowsight, que ainda não abordamos, é o de [uso de gráficos](https://docs.snowflake.com/pt/user-guide/ui-snowsight-visualizations#using-charts).\r\n\r\nNa captura de tela abaixo, siga as setas para criar seu próprio gráfico de barras comparando estas diferentes marcas de food trucks em termos de itens do menu que atendem a certas restrições alimentares.\r\n\r\n![assets/6.3.chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-semi-structured-data-ptbr/6.3.chart.png)\r\n\r\nPara terminar esse quickstart, devemos ressaltar a facilidade com que um executivo da Tasty Bytes poderia fazer esse tipo de análise sozinho sem precisar conhecer processamento de dados semiestruturados que encapsulamos nas exibições criadas. Sendo assim, podemos estar certos de que estamos contribuindo para a democratização dos dados na Tasty Bytes.\r\n\r\n### Etapa 4 - 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 - dados semiestruturados”. \r\n\r\nNeste módulo, você: \r\n- Aprendeu o que são os dados do tipo Variant.\r\n- Usou a notação de pontos para consultar dados semiestruturados. \r\n- Nivelou dados semiestruturados. \r\n- Analisou matrizes.\r\n- Criou dois modos de exibição. \r\n- Criou um gráfico a partir de um conjunto de resultados no Snowsight.\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/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-045ddfc22a","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-3c62be5f7a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b30ef08a90","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-920fdf3566","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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