{"id":84189,"date":"2020-06-05T19:42:34","date_gmt":"2020-06-05T17:42:34","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/razvitie-data-vault-i-perehod-k-business-data-vault"},"modified":"2020-06-05T19:42:34","modified_gmt":"2020-06-05T17:42:34","slug":"razvitie-data-vault-i-perehod-k-business-data-vault","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/administrirovanie\/razvitie-data-vault-i-perehod-k-business-data-vault","title":{"rendered":"Desenvolvimento do DATA VAULT e transi\u00e7\u00e3o para o BUSINESS DATA VAULT","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>No artigo anterior, falei sobre os fundamentos do DATA VAULT, descrevi os principais elementos do DATA VAULT e suas fun\u00e7\u00f5es. No entanto, isso n\u00e3o exaure o tema DATA VAULT; \u00e9 necess\u00e1rio discutir as pr\u00f3ximas etapas da evolu\u00e7\u00e3o do DATA VAULT.<\/p>\n<p>Neste artigo, concentrarei-me no desenvolvimento do DATA VAULT e na transi\u00e7\u00e3o para o BUSINESS DATA VAULT ou simplesmente BUSINESS VAULT.<\/p>\n<h3>Raz\u00f5es para o surgimento do BUSINESS DATA VAULT<\/h3>\n<p>\n\u00c9 importante notar que, embora o DATA VAULT tenha certas vantagens, tamb\u00e9m apresenta desvantagens. Uma dessas desvantagens \u00e9 a complexidade na escrita de consultas anal\u00edticas. As consultas t\u00eam uma quantidade significativa de JOINs, resultando em c\u00f3digo longo e complicado. Al\u00e9m disso, os dados que entram no DATA VAULT n\u00e3o s\u00e3o submetidos a nenhuma transforma\u00e7\u00e3o, portanto, do ponto de vista do neg\u00f3cio, o DATA VAULT em sua forma pura n\u00e3o possui valor absoluto.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nFoi precisamente para eliminar essas desvantagens que a metodologia DATA VAULT foi expandida com elementos como:<\/p>\n<ul>\n<li>tabelas PIT (point in time);<\/li>\n<li>tabelas BRIDGE;<\/li>\n<li>DERIVA\u00c7\u00d5ES PREDEFINIDAS.<\/li>\n<\/ul>\n<p>\nVamos examinar mais detalhadamente a fun\u00e7\u00e3o desses elementos.<\/p>\n<h3>Tabelas PIT<\/h3>\n<p>\nNormalmente, um objeto de neg\u00f3cio (HUB) pode conter dados com diferentes frequ\u00eancias de atualiza\u00e7\u00e3o. Por exemplo, se falarmos sobre dados que caracterizam uma pessoa, podemos afirmar que informa\u00e7\u00f5es sobre n\u00famero de telefone, endere\u00e7o ou e-mail t\u00eam uma frequ\u00eancia de atualiza\u00e7\u00e3o mais alta do que, digamos, nome completo, dados do passaporte, estado civil ou sexo.<\/p>\n<p>Portanto, ao determinar os sat\u00e9lites, deve-se considerar a frequ\u00eancia de suas atualiza\u00e7\u00f5es. Por que isso \u00e9 importante? <\/p>\n<p>Se armazenarmos atributos com diferentes frequ\u00eancias de atualiza\u00e7\u00e3o em uma \u00fanica tabela, ser\u00e1 necess\u00e1rio adicionar uma linha na tabela a cada atualiza\u00e7\u00e3o do atributo que muda mais com frequ\u00eancia. Como consequ\u00eancia, teremos um aumento no volume de espa\u00e7o em disco e um aumento no tempo de execu\u00e7\u00e3o das consultas.<\/p>\n<p>Agora que dividimos os sat\u00e9lites de acordo com a frequ\u00eancia de atualiza\u00e7\u00e3o, e podemos carregar dados neles independentemente, \u00e9 necess\u00e1rio garantir a possibilidade de obter dados atualizados. \u00c9 melhor, sem o uso excessivo de JOINs.<\/p>\n<p>Por exemplo, \u00e9 necess\u00e1rio obter informa\u00e7\u00f5es atualizadas (com base na data da \u00faltima atualiza\u00e7\u00e3o) de sat\u00e9lites que t\u00eam diferentes frequ\u00eancias de atualiza\u00e7\u00e3o. Para isso, n\u00e3o basta fazer um JOIN, mas tamb\u00e9m criar v\u00e1rias subconsultas (para cada sat\u00e9lite contendo informa\u00e7\u00f5es) escolhendo a data m\u00e1xima de atualiza\u00e7\u00e3o MAX(Data de Atualiza\u00e7\u00e3o). Com cada novo JOIN, esse c\u00f3digo cresce rapidamente e se torna dif\u00edcil de entender.<\/p>\n<p>A tabela PIT foi criada para simplificar tais consultas; as tabelas PIT s\u00e3o preenchidas simultaneamente com a inser\u00e7\u00e3o de novos dados no DATA VAULT. Tabela PIT:<\/p>\n<p><img decoding=\"async\" alt=\"Desenvolvimento do DATA VAULT e transi\u00e7\u00e3o para o BUSINESS DATA VAULT\" src=\"\/wp-content\/uploads\/2020\/06\/f3e0c2068e075cd5fba5321c47d755fa.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nAssim, temos informa\u00e7\u00f5es sobre a relev\u00e2ncia dos dados de todos os sat\u00e9lites em cada momento no tempo. Usando JOINs com a tabela PIT, podemos eliminar completamente as subconsultas, desde que a PIT seja preenchida diariamente e sem lacunas. Mesmo se houver lacunas na PIT, \u00e9 poss\u00edvel obter dados relevantes utilizando apenas uma subconsulta para a pr\u00f3pria PIT. Uma subconsulta funcionar\u00e1 mais rapidamente do que subconsultas para cada sat\u00e9lite.<\/p>\n<h3>BRIDGE<\/h3>\n<p>\nAs tabelas do tipo BRIDGE tamb\u00e9m s\u00e3o utilizadas para simplificar consultas anal\u00edticas. No entanto, diferentemente da PIT, elas servem para agilizar as consultas entre diferentes hubs, links e seus sat\u00e9lites.<\/p>\n<p>A tabela cont\u00e9m todas as chaves necess\u00e1rias para todos os sat\u00e9lites, que s\u00e3o frequentemente usadas nas consultas. Al\u00e9m disso, se necess\u00e1rio, as chaves de neg\u00f3cios hash podem ser complementadas por chaves em formato textual, se os nomes das chaves forem necess\u00e1rios para an\u00e1lise.<\/p>\n<p>O fato \u00e9 que, sem o uso de BRIDGE, ao obter dados de sat\u00e9lites pertencentes a diferentes hubs, ser\u00e1 necess\u00e1rio realizar JOIN n\u00e3o apenas entre os sat\u00e9lites, mas tamb\u00e9m entre os links que conectam os hubs.<\/p>\n<p>A presen\u00e7a ou aus\u00eancia de BRIDGE \u00e9 determinada pela configura\u00e7\u00e3o do reposit\u00f3rio e pela necessidade de otimizar a velocidade de execu\u00e7\u00e3o das consultas. \u00c9 dif\u00edcil inventar um exemplo universal para BRIDGE.<\/p>\n<h3>DERIVA\u00c7\u00d5ES PREDEFINIDAS<\/h3>\n<p>\nOutro tipo de objeto que nos aproxima do BUSINESS DATA VAULT s\u00e3o as tabelas que cont\u00eam m\u00e9tricas pr\u00e9-calculadas. Essas tabelas s\u00e3o realmente importantes para os neg\u00f3cios, pois cont\u00eam informa\u00e7\u00f5es agregadas de acordo com regras estabelecidas e permitem o acesso a elas de forma relativamente simples.<\/p>\n<p>As Deriva\u00e7\u00f5es PREDEFINIDAS arquiteturalmente representam, nada mais, do que mais um sat\u00e9lite de um determinado hub. Ele, assim como o sat\u00e9lite comum, cont\u00e9m a chave do neg\u00f3cio e a data de cria\u00e7\u00e3o do registro no sat\u00e9lite. No entanto, aqui as semelhan\u00e7as terminam. A composi\u00e7\u00e3o adicional dos atributos desse \"sat\u00e9lite especializado\" \u00e9 determinada pelos usu\u00e1rios de neg\u00f3cios com base nos indicadores mais requisitados e previamente calculados.<\/p>\n<p>Por exemplo, um hub contendo informa\u00e7\u00f5es sobre um funcion\u00e1rio pode incluir um sat\u00e9lite com os seguintes indicadores:<\/p>\n<ul>\n<li>Sal\u00e1rio m\u00ednimo;<\/li>\n<li>Sal\u00e1rio m\u00e1ximo;<\/li>\n<li>Sal\u00e1rio m\u00e9dio;<\/li>\n<li>Total acumulado de sal\u00e1rios e assim por diante.<\/li>\n<\/ul>\n<p>\n\u00c9 l\u00f3gico incluir as DERIVA\u00c7\u00d5ES PREDEFINIDAS na tabela PIT desse mesmo hub, assim \u00e9 poss\u00edvel obter sem esfor\u00e7o cortes de dados sobre o funcion\u00e1rio em uma data espec\u00edfica.<\/p>\n<h3>CONCLUS\u00d5ES<\/h3>\n<p>\nComo mostra a pr\u00e1tica, o uso do DATA VAULT pelos usu\u00e1rios de neg\u00f3cios \u00e9 um tanto complicado por v\u00e1rias raz\u00f5es:<\/p>\n<ul>\n<li>O c\u00f3digo das consultas \u00e9 complexo e volumoso;<\/li>\n<li>A abund\u00e2ncia de JOINs afeta a performance das consultas;<\/li>\n<li>Para escrever consultas anal\u00edticas \u00e9 necess\u00e1rio um conhecimento excepcional da estrutura do reposit\u00f3rio.<\/li>\n<\/ul>\n<p>\nPara simplificar o acesso aos dados, o DATA VAULT \u00e9 ampliado com objetos adicionais:<\/p>\n<ul>\n<li>tabelas PIT (point in time);<\/li>\n<li>tabelas BRIDGE;<\/li>\n<li>DERIVA\u00c7\u00d5ES PREDEFINIDAS.<\/li>\n<\/ul>\n<p>\nNa pr\u00f3xima <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/505292\/\">artigo<\/a><\/noindex> Eu planejo compartilhar, na minha opini\u00e3o, o que h\u00e1 de mais interessante para quem trabalha com BI. Apresentarei maneiras de criar tabelas de fatos e tabelas de dimens\u00f5es com base no DATA VAULT.<\/p>\n<p>Os materiais do artigo s\u00e3o baseados em:<\/p>\n<ul>\n<li>Sobre <noindex><a rel=\"nofollow\" href=\"https:\/\/www.vertabelo.com\/blog\/data-vault-series-the-business-data-vault\/\">publica\u00e7\u00e3o<\/a><\/noindex> Kenta Graziano, que, al\u00e9m de uma descri\u00e7\u00e3o detalhada, cont\u00e9m esquemas do modelo;<\/li>\n<li>No livro: \"Construindo um Armaz\u00e9m de Dados Escal\u00e1vel com DATA VAULT 2.0\";<\/li>\n<li>Artigo <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">Fundamentos do Data Vault<\/a><\/noindex>.<\/li>\n<\/ul>\n<p>Fonte: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u044f \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u043b \u043e\u0431 \u043e\u0441\u043d\u043e\u0432\u0430\u0445 DATA VAULT, \u043e\u043f\u0438\u0441\u0430\u043b \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b DATA VAULT \u0438 \u0438\u0445 \u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435. \u041d\u0430 \u044d\u0442\u043e\u043c \u043d\u0435\u043b\u044c\u0437\u044f \u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0442\u0435\u043c\u0443 DATA VAULT \u0438\u0441\u0447\u0435\u0440\u043f\u0430\u043d\u043d\u043e\u0439, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c \u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0441\u0442\u0443\u043f\u0435\u043d\u044f\u0445 \u044d\u0432\u043e\u043b\u044e\u0446\u0438\u0438 DATA VAULT. \u0418 \u0432 \u044d\u0442\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u044f \u0441\u043a\u043e\u043d\u0446\u0435\u043d\u0442\u0440\u0438\u0440\u0443\u044e\u0441\u044c \u043d\u0430 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0438 DATA VAULT \u0438 \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u0443 \u043a BUSINESS DATA VAULT \u0438\u043b\u0438 \u043f\u0440\u043e\u0441\u0442\u043e BUSINESS VAULT. \u041f\u0440\u0438\u0447\u0438\u043d\u044b \u043f\u043e\u044f\u0432\u043b\u0435\u043d\u0438\u044f [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":84190,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-84189","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412 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