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Ao criar uma receita ou um produto em uma linha de montagem, o resultado final é a soma de suas partes, reunindo tudo para criar algo útil (ou delicioso). Da mesma forma, quando uma empresa tem uma profusão de dados brutos, ela precisa organizar e entender tudo antes que eles possam ter uso para qualquer usuário. Isso é feito por meio de uma série de etapas que se chama processamento de dados.\u003C/p\u003E\n\u003Cp\u003EO processamento de dados é a forma como os dados brutos, e muitas vezes caóticos, são estruturados em um formato útil. Por meio de uma série de operações, as empresas descobrem o valor inexplorado contido dentro de colunas de números, páginas de respostas de pesquisa e planilhas repletas de informações. Ele está no centro da estratégia de negócios e possibilita tudo, desde análises de dados de negócios até aprendizado de máquina (machine learning, ML).\u003C/p\u003E\n\u003Cp\u003ENeste guia, vamos explorar o processamento de dados: o que ele é, importância, etapas, tipos, métodos, ferramentas e tecnologias dessa função. Vamos terminar respondendo algumas das principais perguntas relativas a essa operação crítica para os negócios.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"a","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-894500581d","additionalClasses":"headline-decoration","type":"heading2","lines":["O que é processamento de dados?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-b27a551189","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EO processamento de dados é uma série sistemática de operações que recebe dados brutos e não organizados e os transforma em informações úteis a partir das quais as organizações podem extrair insights significativos e tomar decisões fundamentadas. É um elemento básico da estratégia de negócios, sendo fundamental para tornar a análise de dados possível.\u003C/p\u003E\n\u003Cp\u003EHistoricamente, o processamento de dados tem sido um processo manual muito trabalhoso e demorado. Os computadores humanos (ou pessoas que receberam um \u003Ca href=\"https://www.nasa.gov/centers-and-facilities/jpl/when-computers-were-human/\"\u003Ecargo de “computador”\u003C/a\u003E) dependiam de ferramentas físicas, como livros contábeis, formulários e calculadoras, bem como sistemas baseados em papel, para coletar, armazenar e analisar dados. \u003Ca href=\"https://www.rudderstack.com/learn/data-collection/data-collection-history/\"\u003EHá um famoso caso dos \u003C/a\u003EEstados Unidos que levaram \u003Ci\u003Esete anos\u003C/i\u003E para publicar os resultados do recenseamento de 1880 por causa da lentidão dos processos de contagem manual. O fato levou Herman Hollerith, um funcionário do Census Bureau norte-americano, a inventar a máquina tabuladora. A criação reduziu drasticamente o tempo necessário para processar dados de recenseamento de anos para meses e estabeleceu o terreno para o moderno setor de processamento de dados.\u003C/p\u003E\n\u003Cp\u003EHoje, o processamento de dados é um processo eletrônico gerido por computadores e automação, em geral, monitorado por analistas, processadores, \u003Ca href=\"https://www.snowflake.com/pt_br/product/data-engineering/\"\u003Eengenheiros\u003C/a\u003E e cientistas de dados. \u003Ca href=\"https://www.snowflake.com/pt_br/product/ai/\"\u003EIA e ML\u003C/a\u003E desempenham um papel importante no gerenciamento de conjuntos de dados, especialmente grandes. O processamento de dados é muitas vezes descrito como um ciclo em que várias etapas são realizadas para trazer os dados do seu estado bruto para serem analisados, interpretados e depois armazenados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__714116681":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"id":"rel","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"is","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-0ddc201b5b","additionalClasses":"headline-decoration","type":"heading2","lines":["Por que o processamento de dados é importante?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3d68e81fee","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESem processamento de dados, os grandes volumes de dados que as organizações estão gerando a cada segundo não seriam mais do que ruído digital. O processamento de dados abre uma ponte entre informações não processadas, que raramente são úteis em seu estado bruto, e insights importantes que podem fundamentar decisões de negócios e dar às organizações uma vantagem competitiva.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EMelhor processo decisório: \u003C/b\u003Eas empresas não podem se basear em hipóteses e suposições para competir e crescer. Os insights claros obtidos através do processamento de dados podem melhorar as decisões tomadas de várias maneiras, incluindo:\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIdentificação de tendências de mercado:\u003C/b\u003E dados de vendas podem fornecer informações sobre os produtos que estão vendendo bem, que grupos demográficos estão comprando, em que época do ano as vendas são melhores etc.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAumento da eficiência operacional:\u003C/b\u003E analisar dados da cadeia de fornecedores, logística e produção pode ajudar as empresas a identificar desperdícios e gargalos para otimizar seus processos.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPrevisões baseadas em dados:\u003C/b\u003E a análise de dados preditiva usa dados históricos para prever os resultados, ajudando as empresas a antecipar as necessidades dos clientes, gerenciar o estoque e mitigar riscos.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003EPrecisão e confiabilidade aprimoradas:\u003C/b\u003E os dados não processados muitas vezes contêm erros, duplicações e inconsistências. E em muitos setores, como em \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/governance-risk-compliance/\"\u003Egovernança, risco e conformidade (GRC)\u003C/a\u003E, detecção de fraudes e finanças, um único erro ou discrepância pode se tornar algo ainda mais complicado. A etapa de limpeza do processamento de dados (que vamos explicar mais adiante) identifica e corrige esses problemas, tornando os dados mais precisos e confiáveis quando eles forem necessários para a análise.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EMaior vantagem competitiva:\u003C/b\u003E processar e utilizar dados de forma eficaz é um diferencial indispensável para as empresas que querem ter acesso aos dados da concorrência. Entre algumas das vantagens que isso oferece estão:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPersonalizar a experiência do cliente:\u003C/b\u003E processar dados de clientes permite às empresas oferecer recomendações personalizadas, marketing direcionado e serviços que possam interessar, o que aumenta a fidelidade da marca e a retenção dos clientes.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EResponder às mudanças de mercado:\u003C/b\u003E o processamento de dados quase em tempo real permite que as empresas reajam, com rapidez, às mudanças de mercado, seja um novo lançamento de produto por parte de um concorrente ou uma mudança na demanda do cliente.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003EMelhorar a segurança e a conformidade dos dados:\u003C/b\u003E processar dados não significa apenas torná-los úteis, mas também torná-los seguros. \u003Ca href=\"https://www.acceldata.io/blog/data-compliance\"\u003EMedidas de proteção específicas\u003C/a\u003E são incorporadas à limpeza e à organização dos dados, incluindo \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Emascaramento\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003Eanonimização\u003C/a\u003E, criptografia e tokenização. Os sistemas de processamento de dados também impõem regras de quem pode acessar, modificar ou eliminar os dados. Além disso, muitas regulamentações de dados, como RGPD e HIPAA, possuem requisitos rigorosos quanto a que dados podem ser coletados, como podem ser usados etc. Sistemas de processamento de dados documentam meticulosamente todas as etapas do ciclo de vida dos dados, da coleta à exclusão. Isso cria uma trilha de auditoria capaz de provar que uma organização está cumprindo com os regulamentos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"use","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-eb852b5d8b","additionalClasses":"headline-decoration","type":"heading2","lines":["Etapas do processamento de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-77fcd4476c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ENo início deste guia, comparamos o processamento de dados com uma linha de montagem industrial. Assim como acontece com as etapas de construção de um automóvel, desde a instalação do chassis até a pintura e o polimento final, o processamento de dados segue um fluxo de trabalho estruturado e com várias etapas. Cada etapa é essencial para converter dados brutos e fragmentados em dados limpos e confiáveis nos quais as organizações confiam para tomar decisões bem informadas e criar estratégias sólidas.\u003C/p\u003E\r\n\u003Ch4\u003E1. Coleta\u003C/h4\u003E\r\n\u003Cp\u003EChegou a hora de começar a coletar os dados. Eles podem vir de inúmeras fontes, como logs de transações e bancos de dados corporativos, estatísticas de interação em mídias sociais e pesquisas com clientes. Muitas vezes, eles são armazenados em \u003Ca href=\"https://www.snowflake.com/pt_br/product/analytics/\"\u003Edata lakes e warehouses\u003C/a\u003E. É fundamental que os dados extraídos durante esta primeira etapa sejam relevantes, precisos e vindos de fontes confiáveis. Caso contrário, há o risco de distorcer os resultados finais, comprometendo completamente o projeto desde o início.\u003C/p\u003E\r\n\u003Ch4\u003E2. Preparação\u003C/h4\u003E\r\n\u003Cp\u003ECom frequência, chamada de pré-processamento, essa é a etapa mais crítica e demorado, durante a qual os dados são limpos e organizados para garantir a qualidade e a consistência. Essas etapas incluem:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ELimpeza de dados: \u003C/b\u003Ecorreção de erros, preenchimento de valores ausentes, remoção de dados duplicados ou irrelevantes.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003ETransformação de dados\u003C/a\u003E:\u003C/b\u003E conversão de dados em um formato consistente (padronização do formato de data, alteração do texto em código numérico etc.).\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EValidação de dados:\u003C/b\u003E verificação dos dados em relação às regras para garantir a precisão.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.fullstory.com/blog/data-enrichment/\"\u003EEnriquecimento de dados\u003C/a\u003E: \u003C/b\u003Emelhoria do conjunto de dados com informações adicionais relevantes de fontes externas.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003E3. Entrada (Input)\u003C/h4\u003E\r\n\u003Cp\u003EÉ nesta etapa que os dados pré-processados são transferidos para o sistema de processamento, sendo o primeiro estágio em que os dados brutos começam a assumir a forma de dados utilizáveis. Exemplos de sistemas de processamento podem ser programas ou algoritmos projetados para tipos de dados ou objetivos de análise específicos, como o \u003Ca href=\"https://www.ibm.com/think/topics/apache-spark\"\u003EApache Spark\u003C/a\u003E para grandes conjuntos de dados. Entrada manual (para pequenos conjuntos de dados), importação de dados de fontes externas ou captura automática de dados: todos esses são métodos como os dados podem ser inseridos nesses sistemas neste estágio.\u003C/p\u003E\r\n\u003Ch4\u003E4. Processamento\u003C/h4\u003E\r\n\u003Cp\u003EComo o nome sugere, este é o coração do ciclo de processamento dos dados. Algumas técnicas diferentes são usadas para transformar os dados em informações significativas, dependendo do resultado ou dos insights desejados. Entre elas estão:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EClassificação: \u003C/b\u003Eorganizar os dados em uma ordem específica.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFiltragem:\u003C/b\u003E selecionar subconjuntos específicos de dados.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECálculo: \u003C/b\u003Erealizar operações matemáticas, como calcular totais ou médias.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAgregação:\u003C/b\u003E resumir os dados de vários registros.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003E5. Saída (output) e interpretação\u003C/h4\u003E\r\n\u003Cp\u003EApós o processamento, os dados são apresentados em um formato pronto para a ingestão e fácil de entender. O resultado é o produto final, que pode ser um gráfico, um dashboard ou outra representação visual. A fase de interpretação é a análise do resultado para tirar conclusões, identificar tendências e tomar decisões fundamentadas. É nesta fase onde o valor dos dados processados é finalmente aproveitado.\u003C/p\u003E\r\n\u003Ch4\u003E6. Armazenamento\u003C/h4\u003E\r\n\u003Cp\u003EA última etapa consiste em armazenar com segurança os dados processados em bancos de dados ou data warehouses para uso e recuperação futuros. Esta etapa é fundamental por alguns motivos:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditoria e conformidade:\u003C/b\u003E ela cria um registro para fins legais e regulatórios.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAnálises futuras:\u003C/b\u003E os dados podem ser usados como uma base para análises mais complexas.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EReferência:\u003C/b\u003E ela fornece uma fonte confiável de informações históricas para tomada de decisão.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"and","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-44bd85afb9","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de processamento de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cf964ab4da","text":"\u003Cp\u003EVários métodos são usados para transformar dados brutos em informações significativas e úteis. Embora existam poucos, e cada um se adapte melhor a diferentes cenários e necessidades, o processamento em lote, o processamento em tempo real e o processamento on-line são três dos tipos mais comuns.\u003C/p\u003E\r\n\u003Ch4\u003E1. Processamento em lote\u003C/h4\u003E\r\n\u003Cp\u003EO \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/streaming-data-pipelines/\"\u003Eprocessamento em lote\u003C/a\u003E é um método no qual um grande volume de dados é coletado ao longo de um intervalo de tempo e processado de uma só vez, em um lote. Esse método é ideal para tarefas não urgentes e que podem ser agendadas para realização fora do horário de pico de trabalho, para reduzir os recursos de processamento. Exemplos de casos de uso desse tipo seriam: sistemas de folha de pagamento, faturamento mensal, relatórios do fim de dia e geração de relatórios bancários. Por exemplo, uma empresa de cartão de crédito pode coletar todas as transações durante o dia e processá-las em um único lote durante a noite para atualizar as contas dos clientes.\u003C/p\u003E\r\n\u003Ch4\u003E2. Processamento em tempo real\u003C/h4\u003E\r\n\u003Cp\u003EO processamento em tempo real lida com os dados à medida que eles são gerados, fornecendo resultados imediatos. Este método é fundamental para situações em que o prazo de entrada dos dados em produção precisa ser instantâneo, especialmente em sistemas em que o atraso pode ter sérias consequências. \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003EDetecção de fraudes em transações financeiras\u003C/a\u003E, sistemas de GPS e sistemas de controle de tráfego aéreo são exemplos de onde esse tipo de processamento de dados é usado.\u003C/p\u003E\r\n\u003Ch4\u003E3. Processamento online\u003C/h4\u003E\r\n\u003Cp\u003EO \u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Eprocessamento online\u003C/a\u003E é um tipo de processamento em tempo real que é interativo. Ele processa transações iniciadas pelo usuário à medida que elas ocorrem, fornecendo uma resposta imediata. É isso que você experimenta todos os dias quando interage com websites e apps. Em resumo, o usuário inicia um pedido ou insere dados e o sistema os processa e fornece feedback imediatamente. Esses sistemas estão sempre online e prontos para processar solicitações do usuário a qualquer momento. Comércio eletrônico, bancos online, reservas de companhias aéreas e jogos online utilizam o processamento online. Já comprou ingressos online para shows ou sessões de cinema? É dessa forma que o seu pagamento é processado e o sistema é atualizado imediatamente para garantir que ninguém mais possa comprar um ingresso para os mesmos lugares.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy__987904085":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"id":"how","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-1e4ca65e7b","additionalClasses":"headline-decoration","type":"heading2","lines":["Métodos de processamento de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-9f76dfb6dd","text":"\u003Cp\u003EExistem diferentes métodos de processamento de dados, e nem todos os métodos são compatíveis com todos os tipos de processamento.\u003C/p\u003E\r\n\u003Ch4\u003E1. Processamento manual de dados\u003C/h4\u003E\r\n\u003Cp\u003EEste é o método mais antigo e tradicional de processamento de dados. Ele envolve coletar, organizar e analisar dados de forma inteiramente manual, sem a ajuda de máquinas. Ele é lento, trabalhoso, propenso a erros e não é ideal para grandes volumes de dados. No entanto, é uma boa escolha para operações de pequena escala ou empresas, ou onde o julgamento humano é essencial, como realizar uma recontagem manual de votos durante uma eleição.\u003C/p\u003E\r\n\u003Ch4\u003E2. Processamento mecânico de dados\u003C/h4\u003E\r\n\u003Cp\u003ESe você estiver usando máquinas e dispositivos simples para processar dados, como calculadoras, máquinas de escrever ou máquinas perfuradoras de cartão, você está usando o método de processamento mecânico de dados. A máquina tabuladora Hollerith mencionada antes neste guia é um exemplo deste método. O processamento mecânico de dados é ideal para tarefas simples. Ele produz menos erros do que o processo manual, mas ainda não é uma boa escolha para grandes conjuntos de dados.\u003C/p\u003E\r\n\u003Ch4\u003E3. Processamento eletrônico de dados\u003C/h4\u003E\r\n\u003Cp\u003EO \u003Ca href=\"https://www.lenovo.com/us/en/glossary/what-is-edp/?orgRef=https%253A%252F%252Fwww.google.com%252F&srsltid=AfmBOooBYFjcgNsJVG7eEUuzaIdrjkhzOJ_4Z3st5mhFK4tMT58CPgpa\"\u003Eprocessamento eletrônico de dados (electronic data processing, EDP)\u003C/a\u003E é o método mais moderno e amplamente utilizado. Ele depende de soluções eletrônicas, como computadores, servidores e automação para processar dados. É um método altamente eficiente, preciso e com escala dimensionável, capaz de lidar com grandes volumes de dados em tempo real. O EDP automatiza todo o ciclo de processamento de dados, da entrada à produção. Hoje em dia, ele é usado em praticamente todos os setores para tudo, desde sistemas simples de folha de pagamento até aplicações de big data.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"],":type":"snowflake-site/components/container"},"container_copy_copy__714116681":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"id":"why","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-52c1acdb7e","additionalClasses":"headline-decoration","type":"heading2","lines":["Ferramentas e tecnologias de processamento de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-19a488c2a5","text":"\u003Cp\u003EO processamento de dados moderno depende de uma combinação de ferramentas avançadas e tecnologias emergentes para extrair insights valiosos de dados brutos e não processados. Estas soluções possibilitam tudo, do armazenamento de dados básico à análise complexa e automatizada.\u003C/p\u003E\r\n\u003Ch4\u003E1. Bancos de dados e data warehouses\u003C/h4\u003E\r\n\u003Cp\u003ESão ferramentas indispensáveis para o armazenamento e o gerenciamento de dados. No entanto, elas têm \u003Ca href=\"https://www.coursera.org/articles/data-warehouse-vs-database\"\u003Efunções diferentes\u003C/a\u003E no \u003Ca href=\"https://www.snowflake.com/pt_br/fundamentals/modernizing-data-pipelines/\"\u003Epipeline de processamento\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003EO papel dos \u003Cb\u003Ebancos de dados\u003C/b\u003E é armazenar e organizar informações de uma única fonte de dados para uma função específica do seu negócio. Pense nele, como um arquivo fichário meticulosamente organizado para uma única finalidade. Eles foram desenvolvidos para tarefas rápidas e frequentes e pequenas consultas. Os bancos de dados mais comuns incluem sistemas baseados em SQL, como MySQL, PostgreSQL e Microsoft SQL Server.\u003C/p\u003E\r\n\u003Cp\u003EPor outro lado, os \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Edata warehouses\u003C/a\u003E são grandes repositórios centralizados capazes de armazenar grandes volumes de dados históricos de várias fontes. Eles foram desenvolvidos para análise e são, em essência, a biblioteca onde analistas de dados vão buscar informações para responder a perguntas sobre complexas tendências de negócios. Data warehouses foram criados para executar consultas complexas em grandes conjuntos de dados para produzir relatórios e inteligência de mercado. Os \u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003Edata warehouses\u003C/a\u003E muitas vezes usam tecnologias de big data, como Snowflake, Hadoop, Apache Spark e \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E. \u003C/p\u003E\r\n\u003Ch4\u003E2. Inteligência artificial e aprendizado de máquina\u003C/h4\u003E\r\n\u003Cp\u003EA IA e o ML são tecnologias avançadas que automatizam e melhoram todas as fases do processamento de dados. Elas ultrapassam a realização de cálculos simples para identificar padrões e fazer previsões. A IA pode automatizar a limpeza e a preparação de dados, detectar e corrigir erros automaticamente, completar valores ausentes e padronizar formatos de dados. Quando os modelos de ML são treinados com dados históricos, eles podem fazer previsões, encontrar anomalias e segmentar dados.\u003C/p\u003E\r\n\u003Ch4\u003E3. Plataformas de análise de dados e tecnologia de nuvem\u003C/h4\u003E\r\n\u003Cp\u003EProvedores de nuvem, como Amazon Web Services (AWS), Google Cloud Platform (GCP) e Microsoft Azure, permitem às empresas ampliar ou reduzir os recursos de processamento de dados conforme necessário, sem precisar adquirir ou manter hardware caro e local. Eles também possibilitam o processamento de grandes volumes de dados em escala, o que seria impossível para a maioria das empresas realizar de outra forma.\u003C/p\u003E\r\n\u003Cp\u003EAs plataformas de análise de dados são soluções de software que muitas vezes são baseadas na nuvem e fornecem um ambiente completo para processamento de dados. Snowflake e Tableau oferecem uma plataforma unificada para armazenar dados, executar consultas analíticas, criar visualizações e simplificar fluxos de trabalho complexos. Por exemplo, com o \u003Ca href=\"https://www.snowflake.com/pt_br/product/platform/\"\u003ESnowflake AI Data Cloud\u003C/a\u003E os dados são otimizados para operações de alto desempenho, uma vez carregados na plataforma e executados nas principais nuvens públicas.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"],":type":"snowflake-site/components/container"},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"customer","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-53e43f4a32","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusão"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f2e12c4b17","text":"\u003Cp\u003EO processamento de dados é o mecanismo indispensável que conduz a transformação de dados brutos e não organizados em insights essenciais para os negócios de que as organizações precisam para tomar decisões fundamentadas. Caminhou-se bastante desde os dias de registro e análise de dados puramente manuais até agora quando podemos contar com soluções avançadas e automatizadas, com tecnologia de IA e ML, para lidar com o grande volume de dados que as empresas produzem hoje, e que continua a crescer exponencialmente. Mais do que nunca, o processamento de dados inteligente e eficiente é indispensável para lidar com o volume de dados que as organizações produzem todos os dias e garantir o crescimento e o sucesso futuros.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy__127842237":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"id":"faq","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-2d0a561913","additionalClasses":"headline-decoration","type":"heading2","lines":["Perguntas frequentes sobre processamento de dados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-a4d59e93b3","showDivider":false,"accordionItemsList":[{"title":"Que tipos de software estão disponíveis para processamento de grandes volumes de dados?","richText":"\u003Cp\u003E\u003Cb\u003EEstruturas de processamento distribuídas:\u003C/b\u003E Apache Hadoop e Apache Spark\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EData warehouses baseados em nuvem:\u003C/b\u003E Google BigQuery, Amazon Redshift, Microsoft Azure HDInsight\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EBancos de dados NoSQL:\u003C/b\u003E MongoDB, Apache Cassandra\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ESistemas de processamento de fluxo: \u003C/b\u003EApache Flink, Apache Storm\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EFerramentas de visualização e inteligência de mercado (BI): \u003C/b\u003ETableau, Microsoft Power BI\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPlataformas integradas de dados:\u003C/b\u003E Snowflake\u003C/p\u003E\r\n"},{"title":"Quais são algumas fontes de dados comumente usadas para big data?","richText":"\u003Cp\u003EBiga data pode vir de uma grande variedade de fontes, que podem ser amplamente categorizadas em três tipos: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Edados estruturados, não estruturados e semiestruturados\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EDados estruturados:\u003C/b\u003E altamente organizado, seguem um formato predeterminado. São normalmente armazenados em tabelas, o que os torna o tipo de dados mais fácil de pesquisar, gerenciar e analisar usando ferramentas tradicionais. Exemplos: transações financeiras, dados do ponto de venda (point of sale, POS), históricos de saúde.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EDados não estruturados:\u003C/b\u003E não possuem um formato predefinido. São o tipo mais comum de big data, que ainda traz grandes desafios para a análise. Incluem texto, imagens, áudio e vídeo. Exemplos: dados de mídias sociais, PDFs e emails, dados de sensores de termostatos inteligentes ou dispositivos vestíveis.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003EDados semiestruturados\u003C/a\u003E:\u003C/b\u003E um híbrido dos outros dois tipos. Não têm uma estrutura rígida, como os dados estruturados, mas têm algumas propriedades organizacionais que facilitam a categorização e a análise, diferente dos dados não estruturados. Exemplos: arquivos XML e JSON, logs, páginas da web.\u003C/p\u003E\r\n"},{"title":"Quais são alguns exemplos de processamento de dados?","richText":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EProcessamento de folha de pagamento: \u003C/b\u003Eos dados de funcionários, incluindo horas trabalhadas, deduções, salário e informações fiscais, são usados para calcular e emitir verificações de salário conforme o calendário.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERecomendações de comércio eletrônico:\u003C/b\u003E quando você navega em uma loja online, os sistemas dessa empresa processam seu histórico de pesquisa, compras anteriores etc. para recomendar produtos que você possa gostar.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrevisão meteorológica:\u003C/b\u003E os meteorologistas processam um grande volume de dados de satélites, sensores terrestres e estações meteorológicas para criar modelos complexos que preveem padrões climáticos e geram previsões. Entretanto, o nível de precisão de suas previsões locais é outra questão.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],":type":"snowflake-site/components/container"},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"id":"ai","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"title_v2":{"id":"title-v2-1578fba90c","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usam o AI Data Cloud para processamento de dados 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processamento de dados com tom quase perfeito.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Luminate potencializa a análise de dados de entretenimento com processamento de dados 300% mais rápido e insights mais aprofundados"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/luminate/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Ler o caso"},"image":{"id":"image","lazyEnabled":true,"alt":"luminate 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