{"id":84225,"date":"2020-06-06T07:42:14","date_gmt":"2020-06-06T05:42:14","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/vitriny-dannyh-data-vault"},"modified":"2020-06-06T07:42:14","modified_gmt":"2020-06-06T05:42:14","slug":"vitriny-dannyh-data-vault","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/administrirovanie\/vitriny-dannyh-data-vault","title":{"rendered":"Vitrines de dados DATA VAULT","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Nos artigos anteriores, <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/users\/asenterprise\/posts\/\">artigos<\/a><\/noindex>, conhecemos os fundamentos do DATA VAULT, a expans\u00e3o do DATA VAULT para um estado mais adequado \u00e0 an\u00e1lise e a cria\u00e7\u00e3o do BUSINESS DATA VAULT. \u00c9 hora de encerrar a s\u00e9rie com o terceiro artigo.<\/p>\n<p>Como anunciei no artigo anterior, <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">publica\u00e7\u00e3o<\/a><\/noindex>, este artigo ser\u00e1 dedicado ao tema de BI, mais especificamente \u00e0 prepara\u00e7\u00e3o do DATA VAULT como fonte de dados para BI. Vamos explorar como criar tabelas de fatos e dimens\u00f5es, e assim, criar uma esquema de estrela.<\/p>\n<p>Quando comecei a estudar materiais em ingl\u00eas sobre a cria\u00e7\u00e3o de data warehouses sobre o DATA VAULT, tive a impress\u00e3o de que o processo era bastante complexo. As mat\u00e9rias s\u00e3o extensas, referem-se a mudan\u00e7as nas formula\u00e7\u00f5es que surgiram na metodologia Data Vault 2.0 e destacam a import\u00e2ncia dessas formula\u00e7\u00f5es. <\/p>\n<p>No entanto, ao me aprofundar na tradu\u00e7\u00e3o, tornou-se claro que o processo n\u00e3o \u00e9 t\u00e3o complicado assim. Mas, talvez voc\u00ea tenha uma opini\u00e3o diferente.<\/p>\n<p>Assim, vamos ao que interessa.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h3>Tabelas de dimens\u00f5es e fatos no DATA VAULT<\/h3>\n<p>\nA informa\u00e7\u00e3o mais dif\u00edcil de entender:<\/p>\n<ul>\n<li>As tabelas de dimens\u00f5es s\u00e3o constru\u00eddas a partir das informa\u00e7\u00f5es dos hubs e seus sat\u00e9lites;<\/li>\n<li>As tabelas de fatos s\u00e3o constru\u00eddas com base nas informa\u00e7\u00f5es dos links e seus sat\u00e9lites.<\/li>\n<\/ul>\n<p>\nE isso \u00e9 evidente ap\u00f3s a leitura do artigo sobre <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">os fundamentos do DATA VAULT.<\/a><\/noindex>Os hubs armazenam chaves \u00fanicas de objetos de neg\u00f3cios, seus sat\u00e9lites registram o estado dos atributos dos objetos de neg\u00f3cios ligados ao tempo, e os sat\u00e9lites vinculados aos links que suportam transa\u00e7\u00f5es armazenam caracter\u00edsticas num\u00e9ricas dessas transa\u00e7\u00f5es.<\/p>\n<p>Com isso, a teoria, em princ\u00edpio, chega ao fim.<\/p>\n<p>Ainda assim, na minha opini\u00e3o, \u00e9 importante notar alguns conceitos que podem aparecer em artigos sobre a metodologia DATA VAULT:<\/p>\n<ul>\n<li>Raw Data Marts \u2013 showcases de dados \u2018crus\u2019;<\/li>\n<li>Information Marts \u2013 showcases de informa\u00e7\u00e3o.<\/li>\n<\/ul>\n<p>\nO conceito de 'Raw Data Marts' refere-se a showcases constru\u00eddos sobre os dados do DATA VAULT por meio da execu\u00e7\u00e3o de JOINs relativamente simples. A abordagem de 'Raw Data Marts' permite expandir o projeto do armaz\u00e9m de dados de forma flex\u00edvel e em um curto per\u00edodo de tempo, com informa\u00e7\u00f5es adequadas para an\u00e1lise. Esta abordagem n\u00e3o envolve a execu\u00e7\u00e3o de transforma\u00e7\u00f5es complexas de dados e a aplica\u00e7\u00e3o de regras de neg\u00f3cios antes de serem colocadas no showcase; no entanto, os dados dos 'Raw Data Marts' devem ser compreens\u00edveis para o usu\u00e1rio de neg\u00f3cios e servem como base para transforma\u00e7\u00f5es futuras, por exemplo, por meio de ferramentas de BI.<\/p>\n<p>O conceito de \u201cInformation Marts\u201d surgiu na metodologia Data Vault 2.0, substituindo o antigo conceito de \u201cData Marts\u201d. Essa mudan\u00e7a se deve \u00e0 compreens\u00e3o da tarefa de implementar um modelo de dados para a elabora\u00e7\u00e3o de relat\u00f3rios como uma transforma\u00e7\u00e3o de dados em informa\u00e7\u00f5es. O esquema \u201cInformation Marts\u201d deve, primordialmente, fornecer informa\u00e7\u00f5es adequadas para a tomada de decis\u00f5es empresariais.<\/p>\n<p>As defini\u00e7\u00f5es bastante prolixas refletem dois fatos simples:<\/p>\n<ol>\n<li>Os data marts do tipo \u201cRaw Data Marts\u201d s\u00e3o constru\u00eddos sobre o DATA VAULT bruto (RAW), um reposit\u00f3rio que cont\u00e9m apenas os conceitos b\u00e1sicos: HUBS, LINKS, SATELLITES;<\/li>\n<li>Os data marts \u201cInformation Marts\u201d s\u00e3o constru\u00eddos com elementos do BUSINESS VAULT: PIT, BRIDGE.<\/li>\n<\/ol>\n<p>\nSe tomarmos exemplos de armazenamento de informa\u00e7\u00f5es sobre um funcion\u00e1rio, podemos dizer que o data mart que exibe o n\u00famero de telefone atual (v\u00e1lido at\u00e9 hoje) do funcion\u00e1rio \u00e9 um data mart do tipo \u201cRaw Data Marts\u201d. Para a forma\u00e7\u00e3o de tal data mart, utiliza-se a chave de neg\u00f3cios do funcion\u00e1rio e a fun\u00e7\u00e3o MAX() aplicada ao atributo de data de carga no sat\u00e9lite (MAX(SatLoadDate)). Quando \u00e9 necess\u00e1rio armazenar a hist\u00f3ria da mudan\u00e7a do atributo no data mart \u2013 \u00e9 preciso saber de que data at\u00e9 que data o telefone foi v\u00e1lido, a chave prim\u00e1ria dessa tabela ser\u00e1 a compila\u00e7\u00e3o da chave de neg\u00f3cios e da data de carga no sat\u00e9lite, tamb\u00e9m ser\u00e1 adicionado um campo para a data de t\u00e9rmino do per\u00edodo de validade.<\/p>\n<p>A cria\u00e7\u00e3o de um data mart que armazena informa\u00e7\u00f5es atuais de cada atributo de v\u00e1rios sat\u00e9lites que pertencem ao hub, por exemplo, n\u00famero de telefone, endere\u00e7o, nome completo, implica o uso da tabela PIT, atrav\u00e9s da qual \u00e9 f\u00e1cil obter todas as datas de validade. Data marts desse tipo s\u00e3o classificados como \u201cInformation Marts\u201d.<\/p>\n<p>Ambas as abordagens s\u00e3o relevantes tanto para dimens\u00f5es quanto para fatos. <\/p>\n<p>Para a cria\u00e7\u00e3o de data marts que armazenam informa\u00e7\u00f5es de v\u00e1rios links e hubs, pode-se utilizar tabelas BRIDGE.<\/p>\n<p>Com este artigo, finalizo o ciclo sobre o conceito de DATA VAULT, espero que a informa\u00e7\u00e3o que compartilhei seja \u00fatil na implementa\u00e7\u00e3o de seus projetos.<\/p>\n<p>Como sempre, para finalizar, algumas links \u00fateis:<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.vertabelo.com\/blog\/data-vault-series-building-an-information-mart-with-your-data-vault\/\">Artigo<\/a><\/noindex> Kenta Graziano, que, al\u00e9m de uma descri\u00e7\u00e3o detalhada, cont\u00e9m esquemas do modelo;<\/li>\n<\/ul>\n<p><\/p>\n<ul>\n<li>Livro: \u201cBuilding a Scalable Data Warehouse with DATA VAULT 2.0\u201d;<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">Artigo \u201cFundamentos do Data Vault\u201d<\/a><\/noindex>;<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">Artigo \u201cDesenvolvimento do DATA VAULT e transi\u00e7\u00e3o para o BUSINESS DATA VAULT\u201d<\/a><\/noindex>.<\/li>\n<\/ul>\n<p>Fonte: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/505292\/\">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\u0438\u0445 \u0441\u0442\u0430\u0442\u044c\u044f\u0445, \u043c\u044b \u043f\u043e\u0437\u043d\u0430\u043a\u043e\u043c\u0438\u043b\u0438\u0441\u044c \u0441 \u043e\u0441\u043d\u043e\u0432\u0430\u043c\u0438 DATA VAULT, \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0435\u043c DATA VAULT \u0434\u043e \u0431\u043e\u043b\u0435\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0435\u0433\u043e \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0438 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435\u043c BUSINESS DATA VAULT. \u041d\u0430\u0441\u0442\u0430\u043b\u043e \u0432\u0440\u0435\u043c\u044f \u0437\u0430\u0432\u0435\u0440\u0448\u0430\u0442\u044c \u0441\u0435\u0440\u0438\u044e \u0442\u0440\u0435\u0442\u044c\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435\u0439. \u041a\u0430\u043a \u044f \u0430\u043d\u043e\u043d\u0441\u0438\u0440\u043e\u0432\u0430\u043b \u0432 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u0438, \u044d\u0442\u0430 \u0441\u0442\u0430\u0442\u044c\u044f \u0431\u0443\u0434\u0435\u0442 \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u0430 \u0442\u0435\u043c\u0435 BI, \u0430 \u0442\u043e\u0447\u043d\u0435\u0435 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0435 DATA VAULT \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f BI. \u0420\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-84225","post","type-post","status-publish","format-standard","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 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0438\u0445 \u0441\u0442\u0430\u0442\u044c\u044f\u0445, 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