{"id":2521,"date":"2021-05-03T14:18:37","date_gmt":"2021-05-03T17:18:37","guid":{"rendered":"https:\/\/www.sphereit.com.br\/news\/vazamento-de-dados-risco-e-impacto-copy\/"},"modified":"2021-05-04T12:01:16","modified_gmt":"2021-05-04T15:01:16","slug":"diferenca_entre_data_lake_e_data_warehouse","status":"publish","type":"post","link":"https:\/\/www.sphereit.com.br\/pt_br\/news\/diferenca_entre_data_lake_e_data_warehouse\/","title":{"rendered":"As diferen\u00e7as entre Data Lake e Data Warehouse"},"content":{"rendered":"

[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” admin_label=”Header – N\u00c3O EDITAR” _builder_version=”4.4.8″ use_background_color_gradient=”on” background_color_gradient_start=”#004183″ background_color_gradient_end=”#001f4c” background_color_gradient_type=”radial” background_image=”https:\/\/www.sphereit.com.br\/wp-content\/uploads\/2020\/06\/cyber-security-18.png” parallax=”on” custom_padding=”10vw||64px|||” custom_padding_tablet=”” custom_padding_phone=”23vw||64px||false|false” bottom_divider_style=”slant” bottom_divider_color=”#f1fafe” bottom_divider_height=”40px” bottom_divider_flip=”vertical” da_disable_devices=”off|off|off” locked=”off” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.4.8″ z_index=”1″ custom_margin=”-104px|auto||auto||”][et_pb_column type=”4_4″ _builder_version=”3.25.1″][et_pb_post_title author=”off” date_format=”d\/m\/Y” comments=”off” featured_image=”off” _builder_version=”4.9.0″ title_font=”eurostile|700||on|||||” title_text_align=”center” title_text_color=”#ffffff” title_font_size=”37px” meta_font=”||||||||” meta_text_align=”center” meta_text_color=”#ffffff” meta_font_size=”18px”][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”EDITAR AQUI” _builder_version=”4.4.8″ background_color=”#f1fafe” z_index=”10″ custom_padding=”54px|||||” bottom_divider_height=”26vw” bottom_divider_flip=”horizontal” da_disable_devices=”off|off|off” locked=”off” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.4.8″ custom_padding=”19px|||||”][et_pb_column type=”4_4″ _builder_version=”4.4.8″][et_pb_text _builder_version=”4.9.0″ text_font=”eurostile||||||||” text_font_size=”16px” text_orientation=”justified” custom_padding=”||75px|||”]<\/p>\n

\"\"<\/p>\n

Data Lake e Data Warehouse s\u00e3o tipos de armazenamento de dados \u00fateis para a infraestrutura de organiza\u00e7\u00f5es din\u00e2micas e competitivas. Duas possibilidades com pr\u00f3s e contras a serem avaliados segundo as necessidades das empresas.<\/strong><\/p>\n

A expans\u00e3o do mundo do Big Data originou variadas formas de armazenamento, tais como o Data Lake e o Data Warehouse. Com fun\u00e7\u00f5es parecidas e, \u00e0s vezes, confundidas, ambos hospedam dados corporativos para an\u00e1lise e relat\u00f3rios de neg\u00f3cios, por\u00e9m com diferentes sistemas de gera\u00e7\u00e3o e padr\u00f5es de acesso.<\/p>\n

Data Lake \u00e9 um dep\u00f3sito de dados \u2013 estruturados, n\u00e3o estruturados e h\u00edbridos \u2013 num s\u00f3 lugar, com qualidade limitada, mas que fornece base para a gera\u00e7\u00e3o de relat\u00f3rios, visualiza\u00e7\u00e3o e an\u00e1lise avan\u00e7ada. Seus dados n\u00e3o se prendem a objetivos predefinidos (schema-on-read<\/em>), o que significa que podem ser armazenados sem limpeza, tratamento ou organiza\u00e7\u00e3o, ou seja, em seu estado bruto. Por armazenar dados a um baixo custo e de modo escal\u00e1vel e por coletar, importar e processar dados de infraestruturas anal\u00edticas j\u00e1 utilizadas, pode passar por upgrades sucessivos, conforme aumentam os dados, sem que haja uma desatualiza\u00e7\u00e3o a curto prazo. N\u00e3o h\u00e1 restri\u00e7\u00f5es para a ferramenta, da\u00ed seu nome, um \u201clago\u201d, que abriga informa\u00e7\u00f5es num \u00fanico local: o Big Data. Suas maiores vantagens s\u00e3o: abrigar qualquer tipo de dado; ter flexibilidade; democratizar o acesso; armazenar grandes quantidades de dados e algoritmos.<\/p>\n

Data Warehouse \u00e9 um dep\u00f3sito central de dados integrados e estruturados, de duas ou mais fontes, usado principalmente para relat\u00f3rios e an\u00e1lises e tido como principal componente da intelig\u00eancia de neg\u00f3cios (business intelligence<\/em>). Ele implementa padr\u00f5es anal\u00edticos predefinidos e distribu\u00eddos para um grande n\u00famero de usu\u00e1rios na empresa. Sua caracter\u00edstica \u00e9 possuir um \u201cschema\u201d<\/em>, com dados limpos, tratados e organizados que funciona como um estoque a ser consultado periodicamente, com informa\u00e7\u00f5es bem localizadas e facilmente acessadas, sobre clientes e fornecedores, mas que tendem a ficar desatualizadas a curto prazo. Armazenar volumes de dados num Data Warehouse \u00e9 complexo e custoso pelo fato de, antes da coleta, ser preciso prepar\u00e1-los, transform\u00e1-los e estrutur\u00e1-los. Seus maiores benef\u00edcios s\u00e3o: integrar diferentes fontes em uma vis\u00e3o \u00fanica, armazenar dados higienizados, favorecer insights e permitir an\u00e1lise de hist\u00f3ricos.<\/p>\n

De acordo com o Gartner, Inc., uma certa \u201cmoda\u201d em rela\u00e7\u00e3o ao Data Lake est\u00e1 gerando certa confus\u00e3o na \u00e1rea de gest\u00e3o e \u00e9 preciso compreender como ele opera e como se obt\u00e9m valor a partir dele. O fato de os dados estarem no \u201clago\u201d, acess\u00edveis a todos da organiza\u00e7\u00e3o n\u00e3o pressup\u00f5e que todos sejam qualificados para sua manipula\u00e7\u00e3o e an\u00e1lise. \u00c9 preciso um controle empresarial de gest\u00e3o. Muitos veem o Data Lake e o Data Warehouse como op\u00e7\u00f5es intercambi\u00e1veis, mas, na verdade, cada um tem uma finalidade prim\u00e1ria diferente. Quando combinados, suportam cargas de trabalho complexas, diversificadas e distribu\u00eddas. N\u00e3o se trata de saber qual o melhor, mas qual a melhor op\u00e7\u00e3o para determinada empresa<\/strong>. <\/strong>E a escolha depende de vari\u00e1veis como o porte da empresa, as limita\u00e7\u00f5es e os objetivos dos projetos de Big Data. Apesar das diferen\u00e7as t\u00e9cnicas, conceituais e de finalidade, as ferramentas s\u00e3o complementares e, quando trabalham de modo integrado, geram uma boa rela\u00e7\u00e3o custo-benef\u00edcio e proporcionam \u00e0s organiza\u00e7\u00f5es otimiza\u00e7\u00e3o de processos e de tempo.<\/p>\n

https:\/\/www.talend.com\/resources\/data-lake-vs-data-warehouse\/<\/a><\/p>\n

https:\/\/planin.com\/cuidado-com-a-ilusao-do-data-lake-aconselha-o-gartner\/<\/a><\/p>\n

http:\/\/datascienceacademy.com.br\/blog\/como-diferenciar-data-hub-data-lake-e-data-warehouse\/<\/a><\/p>\n

http:\/\/www.gartner.com\/document\/2805917<\/a>.<\/p>\n

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Data Lake e Data Warehouse s\u00e3o tipos de armazenamento de dados \u00fateis para a infraestrutura de organiza\u00e7\u00f5es din\u00e2micas e competitivas. Duas possibilidades com pr\u00f3s e contras a serem avaliados segundo as necessidades das empresas.<\/p>\n","protected":false},"author":4,"featured_media":2530,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[13],"tags":[],"class_list":["post-2521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"yoast_head":"\nAs diferen\u00e7as entre Data Lake e Data Warehouse - Sphere IT<\/title>\n<meta name=\"description\" content=\"Data Lake e Data Warehouse s\u00e3o tipos de armazenamento de dados \u00fateis para a infraestrutura de organiza\u00e7\u00f5es din\u00e2micas e competitivas. 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