{"id":39275,"date":"2019-10-31T22:29:10","date_gmt":"2019-10-31T19:29:10","guid":{"rendered":"https:\/\/prohoster.info\/blog\/plan-prokachki-dlya-polucheniya-professii-data-engineer\/"},"modified":"2019-10-31T22:29:10","modified_gmt":"2019-10-31T19:29:10","slug":"plan-prokachki-dlya-polucheniya-professii-data-engineer","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/administrirovanie\/plan-prokachki-dlya-polucheniya-professii-data-engineer","title":{"rendered":"Plano de aprimoramento para a profiss\u00e3o de Engenheiro de Dados","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Nos \u00faltimos oito anos, trabalhei como gerente de projetos (n\u00e3o escrevo c\u00f3digo no trabalho), o que, naturalmente, impactou negativamente meu conhecimento tecnol\u00f3gico. Decidi reduzir meu desvio tecnol\u00f3gico e adquirir a profiss\u00e3o de Data Engineer. A principal habilidade de um Data Engineer \u00e9 a capacidade de projetar, construir e manter armaz\u00e9ns de dados.<\/p>\n<p>Elaborei um plano de estudos que acredito ser \u00fatil n\u00e3o apenas para mim. O plano \u00e9 voltado para o autoestudo de cursos. A prioridade \u00e9 dada a cursos gratuitos em portugu\u00eas.<\/p>\n<p>Se\u00e7\u00f5es: <\/p>\n<ul>\n<li><b>Algoritmos e estruturas de dados. <\/b>Se\u00e7\u00e3o chave. Se voc\u00ea estud\u00e1-la, o restante tamb\u00e9m se tornar\u00e1 mais f\u00e1cil. \u00c9 importante praticar a escrita de c\u00f3digo e o uso das principais estruturas e algoritmos.<\/li>\n<li><b>Bancos e armaz\u00e9ns de dados, Business Intelligence.<\/b> Da mudan\u00e7a de algoritmos, passamos ao armazenamento e processamento de dados.<\/li>\n<li><b>Hadoop e Big Data. <\/b>Quando a base de dados n\u00e3o cabe no disco r\u00edgido, ou quando \u00e9 necess\u00e1rio analisar dados que o Excel j\u00e1 n\u00e3o consegue carregar, surgem os grandes dados. Na minha opini\u00e3o, \u00e9 necess\u00e1rio estudar este t\u00f3pico somente ap\u00f3s um aprendizado aprofundado das duas se\u00e7\u00f5es anteriores.<\/li>\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Algoritmos e estruturas de dados<\/h2>\n<p>\nInclu\u00ed em meu plano o estudo de Python, a revis\u00e3o dos fundamentos de matem\u00e1tica e algoritmos.<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/67\">Programa\u00e7\u00e3o em Python<\/a><\/noindex> <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/512\">Python: fundamentos e aplica\u00e7\u00f5es<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/2461\">\u00c1lgebra linear<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/91\/\">Introdu\u00e7\u00e3o \u00e0 matem\u00e1tica discreta <\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/217\/\">Algoritmos: teoria e pr\u00e1tica. M\u00e9todos<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/1547\">Algoritmos: teoria e pr\u00e1tica. Estruturas de dados<\/a><\/noindex><\/li>\n<\/ul>\n<p><\/p>\n<h2>Bancos de dados e armazenamentos de dados, Business Intelligence<\/h2>\n<p><\/p>\n<ul>\n<li>Livro: Martin Kleppmann \u2014 Aplica\u00e7\u00f5es de alta carga. Programa\u00e7\u00e3o, escalabilidade, suporte. O livro descreve como diferentes modelos de dados funcionam, sua implementa\u00e7\u00e3o interna, limita\u00e7\u00f5es e escolhas dependendo da tarefa. <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/551\">Introdu\u00e7\u00e3o a bancos de dados <\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/3203\">Imers\u00e3o em SGBD<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/2586\">Introdu\u00e7\u00e3o a bancos de dados n\u00e3o relacionais <\/a><\/noindex><\/li>\n<\/ul>\n<p>\nOs t\u00f3picos relacionados \u00e0 constru\u00e7\u00e3o de armaz\u00e9ns de dados, ETL, cubos OLAP dependem muito das ferramentas, portanto, neste documento, n\u00e3o incluo links para cursos. \u00c9 sensato estudar esses sistemas ao trabalhar em um projeto espec\u00edfico em uma empresa espec\u00edfica. Para se familiarizar com ETL, pode-se experimentar <noindex><a rel=\"nofollow\" href=\"https:\/\/www.talend.com\/\">Talend<\/a><\/noindex> ou <noindex><a rel=\"nofollow\" href=\"https:\/\/airflow.apache.org\/\">Airflow<\/a><\/noindex>.<\/p>\n<p>Na minha opini\u00e3o, \u00e9 importante estudar a metodologia moderna de design de armaz\u00e9ns de dados Data Vault. <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/348188\/\">link 1<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"http:\/\/www.dwh-club.com\/ru\/dwh-bi-articles\/vse-o-data-vault.html\">link 2<\/a><\/noindex>. E a melhor maneira de aprend\u00ea-lo \u00e9 implement\u00e1-lo em um exemplo simples. No GitHub, existem v\u00e1rios exemplos de implementa\u00e7\u00e3o do Data Vault <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/semashkinvg\/DataVault\">link<\/a><\/noindex>. Livro moderno sobre armaz\u00e9ns de dados: Modeling the Agile Data Warehouse with Data Vault de Hans Hultgren.<\/p>\n<p>Para conhecer as ferramentas de Business Intelligence para usu\u00e1rios finais, voc\u00ea pode usar o construtor de relat\u00f3rios gratuito, dashboards e mini-armaz\u00e9ns de dados Power BI Desktop. Materiais de aprendizado: <noindex><a rel=\"nofollow\" href=\"https:\/\/docs.microsoft.com\/ru-ru\/learn\/paths\/create-use-analytics-reports-power-bi\/\">link 1<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/docs.microsoft.com\/ru-ru\/learn\/paths\/consume-data-with-power-bi\/\">link 2<\/a><\/noindex>.<\/p>\n<h2>Hadoop e Big Data<\/h2>\n<p><\/p>\n<ul>\n<li>Comece implementando o MapReduce por conta pr\u00f3pria, sem bibliotecas externas. Isso permitir\u00e1 que voc\u00ea entenda melhor as implementa\u00e7\u00f5es multithread no futuro. Um excelente exemplo em Python est\u00e1 descrito <noindex><a rel=\"nofollow\" href=\"http:\/\/michaelnielsen.org\/blog\/write-your-first-mapreduce-program-in-20-minutes\/\">aqui<\/a><\/noindex>.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/150\">Hadoop. Sistema para processamento de grandes volumes de dados.<\/a><\/noindex> <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/openedu.ru\/course\/misis\/ABD\/\">Introdu\u00e7\u00e3o \u00e0 engenharia de grandes dados <\/a><\/noindex><\/li>\n<\/ul>\n<p><\/p>\n<h2>Conclus\u00e3o<\/h2>\n<p>\nNem tudo que se estuda pode ser aplicado no trabalho. Portanto, \u00e9 necess\u00e1rio um projeto de gradua\u00e7\u00e3o, onde voc\u00ea poder\u00e1 tentar aplicar novos conhecimentos. <\/p>\n<p>O plano n\u00e3o inclui t\u00f3picos relacionados \u00e0 an\u00e1lise de dados e Machine Learning, pois isso est\u00e1 mais relacionado \u00e0 profiss\u00e3o de Data Scientist. Tamb\u00e9m n\u00e3o h\u00e1 t\u00f3picos sobre nuvens AWS, Azure, pois esses temas dependem muito da escolha da plataforma.<\/p>\n<p>Quest\u00f5es para a comunidade:<br \/>\nQu\u00e3o adequado \u00e9 meu plano de desenvolvimento? O que devo remover ou adicionar?<br \/>\nQue projeto voc\u00ea recomenda como trabalho de conclus\u00e3o de curso?<br \/>\n<br \/>Fonte: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/472622\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0432\u043e\u0441\u0435\u043c\u044c \u043b\u0435\u0442 \u044f \u0440\u0430\u0431\u043e\u0442\u0430\u044e \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0435\u043c \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 (\u043d\u0435 \u043f\u0438\u0448\u0443 \u043a\u043e\u0434 \u043d\u0430 \u0440\u0430\u0431\u043e\u0442\u0435), \u0447\u0442\u043e \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043d\u0435\u0433\u0430\u0442\u0438\u0432\u043d\u043e \u0432\u043b\u0438\u044f\u0435\u0442 \u043d\u0430 \u043c\u043e\u0439 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0431\u0435\u043a\u044d\u043d\u0434. \u042f \u0440\u0435\u0448\u0438\u043b \u0441\u043e\u043a\u0440\u0430\u0442\u0438\u0442\u044c \u0441\u0432\u043e\u0451 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043e\u0442\u0441\u0442\u0430\u0432\u0430\u043d\u0438\u0435 \u0438 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u044e Data engineer. \u041e\u0441\u043d\u043e\u0432\u043d\u043e\u0439 \u043d\u0430\u0432\u044b\u043a Data engineer \u2014 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u044c \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0442\u044c, \u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0438 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0442\u044c \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0430 \u0434\u0430\u043d\u043d\u044b\u0445. \u0421\u043e\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u043b\u0430\u043d \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0434\u0443\u043c\u0430\u044e \u043e\u043d \u0431\u0443\u0434\u0435\u0442 \u043f\u043e\u043b\u0435\u0437\u0435\u043d \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0434\u043b\u044f \u043c\u0435\u043d\u044f. \u041f\u043b\u0430\u043d [&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-39275","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=\"\u041f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0432\u043e\u0441\u0435\u043c\u044c \u043b\u0435\u0442 \u044f 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