{"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\/en\/news\/diferenca_entre_data_lake_e_data_warehouse\/","title":{"rendered":"The differences between Data Lake and 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|||”]\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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Footer” _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_color_gradient_direction_radial=”top right” background_color_gradient_start_position=”42%” background_image=”https:\/\/www.sphereit.com.br\/wp-content\/uploads\/2020\/06\/cyber-security-17.png” z_index=”10″ min_height=”545px” height=”446px” overflow-x=”visible” overflow-y=”visible” custom_margin=”-2px||-2px||true|false” custom_padding=”||0vw||false|false” da_disable_devices=”off|off|off” global_module=”215″ 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 column_structure=”2_5,3_5″ make_equal=”on” disabled_on=”on|on|off” _builder_version=”4.4.8″ background_color=”#ffffff” min_height=”421px” custom_margin=”-137px|auto|-2px|auto|false|false” custom_padding=”0px||0px||true” border_radii=”on|10px|10px|10px|10px” box_shadow_style=”preset1″ box_shadow_vertical=”40px” box_shadow_blur=”60px” box_shadow_spread=”-8px” box_shadow_color=”rgba(10,45,97,0.48)” saved_tabs=”all” locked=”off”][et_pb_column type=”2_5″ _builder_version=”3.25.3″ custom_padding=”60px|40px|60px|40px|true|true”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” header_2_font=”eurostile|900|||||||” header_2_font_size=”56px” header_2_line_height=”1.1em” text_orientation=”center” width=”100%” custom_margin_tablet=”” custom_margin_phone=”-40px||7px||false|false” custom_margin_last_edited=”on|phone” header_2_font_size_tablet=”38px” header_2_font_size_phone=”26px” header_2_font_size_last_edited=”on|desktop” text_text_align=”center” locked=”off”]\n

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Data Lake and Data Warehouse are types of data storage useful for the infrastructure of dynamic and competitive organizations. Two possibilities with pros and cons to be evaluated according to the needs of the companies.<\/p>","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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