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Os data lakes surgiram como uma base fundamental da infraestrutura de dados moderna, desenvolvida para lidar com o volume, a variedade e a velocidade dos dados atuais.\u003C/p\u003E\n\u003Cp\u003EEste artigo explica o que é um data lake e detalha a arquitetura subjacente que permite o seu funcionamento de forma eficaz e em escala.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"data","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4f4da8e0d8","additionalClasses":"headline-decoration","type":"heading2","lines":["O que é um data lake?"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-e4610087d2","text":"\u003Cp\u003EUm data lake é um repositório centralizado capaz de permitir que as organizações armazenem todos os tipos de dados (estruturados, semiestruturados e não estruturados) em formato bruto. Diferente dos bancos de dados ou dos data warehouses tradicionais, os data lakes não precisam de esquemas ou formatações predefinidos antes do armazenamento, tornando-os altamente flexíveis e ágeis.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-31ef0e0120","type":"heading4","lines":["Principais características:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-22ee455b03","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESchema-on-read: \u003C/b\u003Eos dados são interpretados no momento do acesso, não da ingestão.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESuporte a diversos tipos de dados:\u003C/b\u003E de arquivos CSV e logs JSON a imagens e vídeos.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EOferece suporte a várias linguagens: \u003C/b\u003Ecomo SQL, Spark e outras.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDimensionável em escala e com boa relação custo/benefício: \u003C/b\u003Ecriado com base em armazenamento de objetos de baixo custo, geralmente na nuvem.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDesenvolvido para uma ampla variedade de usuários:\u003C/b\u003E desde analistas de dados a cientistas e engenheiros de dados.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EVoltado para atender a todo o ciclo de vida dos dados: \u003C/b\u003Edesde a ingestão e o armazenamento, processamento e transformação até análises de dados avançadas e IA.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"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,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-b0245cc6bf","additionalClasses":"headline-decoration","type":"heading2","lines":["Data Lake vs. Data Warehouse"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-10f901d3e0","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EUm \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Edata lake e um data warehouse\u003C/a\u003E são utilizados para armazenar e analisar dados, mas existem diferenças importantes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEstrutura de dados:\u003C/b\u003E um data lake armazena dados brutos e não processados sem uma estrutura predefinida. Ele usa um processo chamado \"schema-on-read\", onde os dados são organizados somente quando são acessados para análise. Um \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Edata warehouse\u003C/a\u003E requer que os dados sejam limpos e transformados para atender a um esquema predefinido antes do armazenamento, um processo chamado \"schema-on-write\".\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETipos de dados: \u003C/b\u003Eum data lake pode acomodar todos os tipos de dados, incluindo dados não estruturados, semiestruturados e estruturados. Data warehouses tradicionais armazenam dados estruturados e prontos para análise.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUsuários e finalidade:\u003C/b\u003E os data warehouses são muitas vezes otimizados para inteligência de mercado (business intelligence, BI) e relatórios, sendo usados por analistas de negócios. Os data lakes são muitas vezes usados por cientistas e engenheiros de dados para análises avançadas de dados e aprendizado de máquina em uma maior variedade de tipos de dados.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EEstratégia híbrida:\u003C/b\u003E muitas organizações combinam os dois sistemas em uma arquitetura \"lakehouse\" para obter a flexibilidade de um data lake com o desempenho de um data warehouse.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1697459514":{"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":"air","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7a313f8e1f","additionalClasses":"headline-decoration","type":"heading2","lines":["Benefícios dos data lakes"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-f19469608e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EUm data lake oferece muitas vantagens para as organizações que buscam gerenciar e analisar dados modernos em escala.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EArmazenamento econômico: os \u003C/b\u003Edata lakes são criados com base em armazenamentos de objetos de baixo custo, muitas vezes na nuvem, resultando em custos menores de armazenamento em comparação a sistemas tradicionais. Isso faz com que seja mais econômico armazenar grandes volumes de dados sem uma estrutura predefinida.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEscalabilidade para big data: os data lakes \u003C/b\u003Eforam desenvolvidos para lidar com o volume e a diversidade de dados atuais. Com facilidade, eles conseguem oferecer suporte a petabytes de dados. Os data lakes são altamente dimensionáveis e sua escala pode ser ajustada de acordo com as necessidades de negócios.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFlexibilidade para vários tipos de dados: \u003C/b\u003Eum data lake pode ingerir qualquer tipo de dados, de qualquer fonte, a qualquer velocidade, sejam eles \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Eestruturados, semiestruturados ou não estruturados\u003C/a\u003E. Esse método \"schema-on-read\" permite o máximo de flexibilidade para a descoberta de dados.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnálise de dados avançada e prontidão para a IA:\u003C/b\u003E um data lake serve como uma base flexível para uma ampla variedade de cargas de trabalho analíticas. Ele oferece suporte a análise de dados avançada, aprendizado de máquina e análise de dados em tempo real. A capacidade de fornecer dados brutos em grandes volumes é essencial para o treinamento de modelos de IA e de aprendizado de máquina.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_1767623498":{"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","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"what","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bee7d28697","additionalClasses":"headline-decoration","type":"heading2","lines":["O que é a arquitetura de data lake?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-b5d3f01738","text":"\u003Cp\u003EEnquanto o data lake refere-se ao conceito, a arquitetura do data lake refere-se à estrutura e aos componentes subjacentes que permitem o seu funcionamento com eficiência. É um sistema estruturado desenvolvido para gerenciar a ingestão, o armazenamento, o processamento, a descoberta, a governança e o consumo de conjuntos de dados em grande escala.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-a9f247fcb6","type":"heading4","lines":["Principais componentes da arquitetura do data lake:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-e63eb574b9","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EFuncionalidade\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada de ingestão\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EObtém dados de várias fontes (streaming, lote, internet das Coisas e APIs).\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada de armazenamento\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EConserva dados brutos em um armazenamento de objetos dimensionável.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EMetadados e camada de catalogação\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EIndexa, marca e organiza dados para descoberta.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada de processamento\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ELida com a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Etransformação de dados\u003C/a\u003E por meio de ferramentas de processamento em lote/em tempo real.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada de acesso\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EPossibilita consultas, descoberta e análise de dados.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECamada de segurança e governança\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EGerencia a privacidade de dados, os controles de acesso, a auditoria e a conformidade.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/text"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"id":"rel","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1520074195":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1202161416":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1835740287":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__746839706":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1923870193":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1477902138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"type","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7a404a81e2","type":"heading2","lines":["Tipos de dados compatíveis"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-b77e48ce82","text":"\u003Cp\u003EData lakes foram criados para armazenar grandes quantidades de dados em seu formato nativo e bruto. Por isso, eles oferecem suporte a diversos tipos de dados, que de modo geral podem ser categorizados em três tipos principais:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-3b0a9c9ed5","type":"heading4","lines":["Dados estruturados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1835740287":{"id":"text-ae7bc74b6f","text":"\u003Cp\u003EÉ um tipo de dados é altamente organizado que se encaixa perfeitamente em linhas e colunas, bem como em um banco de dados relacional ou em uma planilha. Esses dados possuem um esquema predefinido, que facilita a pesquisa, a análise e o gerenciamento. Exemplos de tipos de dados estruturados incluem:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-1e6aa38714","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ETabelas de banco de dados relacionais: \u003C/b\u003Edados organizados com colunas e linhas fixas.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPlanilhas (por exemplo, arquivos CSV):\u003C/b\u003E dados tabulares com colunas definidas.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDados numéricos:\u003C/b\u003E números inteiros, números de ponto flutuante e decimais.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDados categóricos:\u003C/b\u003E rótulos ou categorias com um número limitado de valores.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDados de data e hora:\u003C/b\u003E carimbos de data/hora, datas e valores de tempo.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1923870193":{"id":"title-v2-bd71e9f8f0","type":"heading4","lines":["Dados semiestruturados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1477902138":{"id":"text-00a64a4951","text":"\u003Cp\u003ESão dados que não seguem uma estrutura tabular rígida, mas possuem algumas propriedades organizacionais, tornando-os mais fáceis de analisar do que dados não estruturados. 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Exemplos incluem:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy":{"id":"text-40b39719c7","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EJSON (JavaScript Object Notation): \u003C/b\u003Eum formato leve que utiliza pares de chave-valor e objetos aninhados.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EXML (extensible markup language): \u003C/b\u003Euma linguagem de marcação que define um conjunto de regras para codificar documentos em um formato legível tanto por pessoas e por máquinas.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ECSV (valores separados por vírgula) com estruturas complexas: \u003C/b\u003Eenquanto o CSV básico é estruturado, ele pode se tornar semiestruturado com quantidades variáveis de colunas ou dados aninhados dentro de campos.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos de log: \u003C/b\u003Emuitas vezes contêm datas, tipos de eventos e mensagens que podem ser analisados.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBancos de dados NoSQL:\u003C/b\u003E documentos ou armazenamentos de chave-valor onde o esquema pode variar de uma entrada para outra.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EHTML (hypertext markup language):\u003C/b\u003E embora seja usada principalmente para páginas da web, contém elementos e dados estruturados.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EYAML (YAML Ain't Markup Language™):\u003C/b\u003E um padrão de serialização de dados fácil de ser lido para todas as linguagens de programação.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1520074195":{"id":"title-v2-8886ff849b","type":"heading4","lines":["Dados não estruturados"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__746839706":{"id":"text-b4826d5251","text":"\u003Cp\u003EEsse tipo de dados não possui formato ou organização predefinidos, fazendo com que eles sejam difíceis de analisar usando os métodos tradicionais. Para obter insights a partir desse tipo de dados, muitas vezes é necessário empregar ferramentas e técnicas especializadas, como processamento de linguagem natural ou aprendizado de máquina. Exemplos de tipos de dados não estruturados incluem:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy_":{"id":"text-0267cd6c16","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos de texto:\u003C/b\u003E documentos (.txt, .doc, .pdf), emails e publicações em mídias sociais.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos de imagens:\u003C/b\u003E JPEG, PNG, GIF.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos de áudio: \u003C/b\u003EMP3, WAV.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos de vídeo:\u003C/b\u003E MP4, AVI, MOV.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDados de sensores:\u003C/b\u003E fluxos de dados de dispositivos da Internet das Coisas que podem não ter uma estrutura consistente.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EArquivos binários:\u003C/b\u003E arquivos executáveis e formatos de dados proprietários.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1202161416":{"id":"text-428fa42b32","text":"\u003Cp\u003EA capacidade de armazenar e processar toda essa variedade de tipos de dados em seu formato nativo é uma característica e vantagem fundamental de um data lake. Esse método \"schema-on-read\" oferece flexibilidade e permite que cientistas e analistas de dados pesquisem e analisem dados de várias maneiras, sem as restrições de uma estrutura predefinida.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","title_v2_copy_copy_c","text_copy_860073138__1835740287","text_copy_copy","title_v2_copy_copy_c_1923870193","text_copy_860073138__1477902138","text_copy_copy_copy","title_v2_copy_copy_c_1520074195","text_copy_860073138__746839706","text_copy_copy_copy_","text_copy_860073138__1202161416"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"}},":itemsOrder":["container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_981691511":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-2691278cf4","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1340528379":"aem-GridColumn aem-GridColumn--default--12"},"id":"life","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-41c50bc360","type":"heading2","lines":["Ciclo de vida dos dados"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-b2393b6b66","text":"\u003Cp\u003EDentro de um data lake, o ciclo de vida dos dados descreve as etapas pelas quais eles passam, desde sua criação ou aquisição inicial até seu arquivamento ou exclusão final. É um processo contínuo que ajuda a garantir que os dados sejam gerenciados, utilizados e governados de forma eficaz durante toda a sua existência dentro do data lake. Veja a seguir uma visão geral do ciclo de vida dos dados em um data lake, tendo em mente que implementações específicas podem variar:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1340528379":{"id":"text-36b9937bdf","text":"\u003Cp\u003E\u003Cb\u003E1. Ingestão de dados: \u003C/b\u003Eesse é o estágio inicial em que dados de vários sistemas de origem são trazidos para o data lake. Essas fontes podem ser diversas, incluindo bancos de dados estruturados, logs semiestruturados, documentos não estruturados, streaming de dados de dispositivos de Internet das Coisas, feeds de mídias sociais e muito mais. A principal característica da ingestão em um data lake é com frequência o \"ingest as-is\", o que significa que os dados geralmente são carregados em seu formato original sem processamento, sem grandes transformações iniciais ou definições de esquema. Isto permite o máximo de flexibilidade para análises futuras. As ferramentas e os processos usados nesta etapa incluem upload em lote, ingestão de streaming em tempo real e conectores de dados.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E2. Armazenamento e persistência de dados:\u003C/b\u003E após a ingestão, os dados brutos são armazenados dentro do data lake. A arquitetura muitas vezes utiliza sistemas de armazenamento distribuído, como o Hadoop Distributed File System (HDFS) ou o armazenamento de objetos baseado na nuvem (por exemplo, Amazon S3, Azure Data Lake Storage e Google Cloud Storage). Os dados permanecem no formato original, permitindo diversos tipos de análises de dados posteriormente. A escalabilidade e o custo-benefício da camada de armazenamento são fundamentais para lidar com os volumes potencialmente grandes de dados presentes em um data lake. Diferentes níveis de armazenamento podem ser empregados com base na frequência de acesso e nas políticas de retenção de dados.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E3. Processamento e transformação de dados: \u003C/b\u003Eessa etapa envolve a preparação dos dados brutos para análise e consumo. Dependendo do caso de uso analítico específico, os dados podem passar por várias transformações, incluindo limpeza, filtragem, unificação, agregação e enriquecimento. Neste ponto o \"schema-on-read\" entra em jogo. O esquema é aplicado quando os dados estão sendo processados para uma finalidade específica, e não no momento da ingestão. Diferentes mecanismos e estruturas de processamento são usados neste estágio, como Spark, Hadoop MapReduce, ferramentas de armazenamento de dados conectadas ao data lake e serviços de processamento sem servidor.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E4. Descoberta e análise de dados: \u003C/b\u003Eé nessa etapa que cientistas de dados, analistas e usuários empresariais exploram os dados processados para descobrir padrões, obter insights e responder a perguntas dos negócios. Eles podem usar várias ferramentas e técnicas, incluindo consultas do tipo SQL, ferramentas de visualização de dados, pacotes de análise estatística e algoritmos de aprendizado de máquina. A flexibilidade do data lake permite vários tipos de estratégias de análise com os mesmos dados, dependendo das necessidades específicas.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E5. Consumo de dados e ação: \u003C/b\u003Eos insights e os dados processados são consumidos por várias aplicações e usuários posteriores. Isso pode envolver a geração de relatórios e dashboards, o fornecimento de dados para sistemas operacionais, o suporte a aplicações em tempo real ou a orientação de decisões de negócios. Os dados consumidos podem estar em diversos formatos e serem acessados por meio de diferentes interfaces, dependendo da aplicação que os utiliza.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E6. Governança e segurança de dados:\u003C/b\u003E durante todo o ciclo de vida, tanto a segurança quanto a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/\"\u003Egovernança de dados\u003C/a\u003E são fundamentais. Isso inclui a definição e a aplicação de políticas relacionadas à \u003Ca href=\"https://www.snowflake.com/en/fundamentals/a-practical-guide-to-data-quality/\"\u003Equalidade dos dados\u003C/a\u003E, gerenciamento de metadados, linhagem de dados, controle de acesso, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Emascaramento de dados\u003C/a\u003E e conformidade com as regulamentações em vigor. Uma governança efetiva ajuda a garantir que os dados dentro do data lake sejam confiáveis, seguros e devidamente gerenciados.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E7. Arquivamento e eliminação de dados: \u003C/b\u003Eà medida que os dados envelhecem e seu valor comercial diminui, eles podem precisar ser arquivados por razões de conformidade ou para otimizar os custos de armazenamento. Eventualmente, os dados que não forem mais necessários podem ser eliminados de acordo com políticas de retenção definidas. Essa etapa final mantém o data lake eficiente e em conformidade ao longo do tempo.\u003C/p\u003E\n\u003Cp\u003EEm essência, o ciclo de vida dos dados em um data lake é projetado para ser flexível e adaptável, permitindo que as organizações ingiram grandes volumes de dados diversificados, processem-nos conforme necessário para casos de uso analítico específicos e extraiam insights valiosos, mantendo a governança e a segurança adequadas. O princípio do \"schema-on-read\" e a separação entre armazenamento e processamento são características-chave que o diferencia dos métodos tradicionais de armazenamento de dados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","text_copy_860073138__1340528379"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_cont":{"id":"flexible-column-container-55a1a31b4b","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-958eb2e603","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-28a19856b3","modalId":"video","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Como a Elysium Analytics aprimorou seu data lake semântico de segurança com Snowflake Native Apps"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=rznH2KyV6MY"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Ver o vídeo"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a62365e1-1fbd-4c9e-9dd1-7e7f8d1b749b/elysium%25403x.png?preferwebp=true&quality=85","alt":"elysium logo","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"id":"container-f5aa658eb4","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["container_copy_copy_","flexible_column_cont"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1663285187":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1885071390":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"why","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6f7790399c","additionalClasses":"headline-decoration","type":"heading2","lines":["Por que data lakes são importantes"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-620b4ff0e5","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESeja analisado conceitualmente ou sob a perspectiva da arquitetura, o valor dos data lakes está em sua capacidade de atender às necessidades modernas de dados. Estes são os principais benefícios dos data lakes: \u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EEscalabilidade:\u003C/b\u003E suporte a petabytes de dados sem esforço.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFlexibilidade: \u003C/b\u003Eingestão de qualquer tipo de dados, de qualquer fonte, a qualquer velocidade.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EEconomia de custos:\u003C/b\u003E redução dos custos de armazenamento comparado aos sistemas tradicionais.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnálise de dados avançada: \u003C/b\u003Ea base para as tecnologias de IA, aprendizado de máquina e análises de dados em tempo real.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDemocratização de dados:\u003C/b\u003E amplo acesso aos dados das equipes técnicas e não técnicas.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-3347ac6cee","type":"heading4","lines":["Evoluindo para o data lake moderno"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613_1885071390":{"id":"text-4a7cef81ec","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EOs data lakes tradicionais estão evoluindo para arquiteturas modernas de data lake, muitas vezes chamadas de \"lakehouses\", ou sistemas híbridos que combinam o armazenamento flexível de data lakes com a consulta estruturada e o desempenho dos data warehouses.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETendências na arquitetura moderna de data lake:\u003C/b\u003E\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EImplementação nativa da nuvem\u003C/li\u003E\n\u003Cli\u003EMecanismos de processamento integrados\u003C/li\u003E\n\u003Cli\u003EEstruturas de governança integrada\u003C/li\u003E\n\u003Cli\u003EPlataformas unificadas que reduzem a distância entre data lakes e data warehouses.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text_copy_1241045613_1885071390"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__967194517":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1885071390":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_422171653":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"use","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ca53dc44fc","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafios e casos de uso de data lakes"],":type":"snowflake-site/components/title-v2"},"title_v2_copy_copy_c":{"id":"title-v2-ac2caf89ee","type":"heading4","lines":["Desafios dos data lakes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_1241045613_1885071390":{"id":"text-09e62c02d3","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ECapacidade de descoberta de dados: \u003C/b\u003Ea falta de catalogação e de gerenciamento de metadados adequados torna difícil para os usuários localizar e compreender os dados de que precisam.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESegurança e governança: \u003C/b\u003Egarantir a conformidade e proteger informações confidenciais em um data lake exige medidas sólidas de segurança e governança, o que pode ser desafiador por causa da dimensão do data lake e da diversidade das fontes de dados.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EComplexidade de integração: \u003C/b\u003Ea natureza diversificada e, muitas vezes, não estruturada das fontes de dados, aliada à escala do data lake, requer ferramentas sofisticadas e modernas para evitar a criação de um \"pântano de dados\" não gerenciável e inacessível.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_422171653":{"id":"title-v2-df6dd3fd07","type":"heading4","lines":["Casos de uso"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613":{"id":"text-cff30272ae","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003EPipelines de aprendizado de máquina e treinamento de IA\u003C/li\u003E\n\u003Cli\u003EProcessamento e análise de dados em tempo real\u003C/li\u003E\n\u003Cli\u003EArmazenamento e arquivamento de dados de longo prazo\u003C/li\u003E\n\u003Cli\u003EConsolidação de dados corporativos\u003C/li\u003E\n\u003Cli\u003EInteligência de mercado e relatórios\u003C/li\u003E\n\u003Cli\u003EAnálise de dados avançada e IA\u003C/li\u003E\n\u003Cli\u003EAnálise de dados preditiva\u003C/li\u003E\n\u003Cli\u003EDescoberta de dados e análise de tendências\u003C/li\u003E\n\u003Cli\u003EExperiências do cliente e marketing: visão 360° do cliente\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","title_v2_copy_copy_c","text_copy_1241045613_1885071390","title_v2_copy_copy_c_422171653","text_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__2142961013":{"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,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-d197c3f017","additionalClasses":"headline-decoration","type":"heading2","lines":["Perguntas frequentes sobre data 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