{"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\/it\/blog\/administrirovanie\/plan-prokachki-dlya-polucheniya-professii-data-engineer","title":{"rendered":"Piano di sviluppo per diventare Data Engineer","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Negli ultimi otto anni ho lavorato come project manager (senza scrivere codice al lavoro), il che ha avuto un impatto negativo sul mio background tecnologico. Ho deciso di ridurre il mio divario tecnologico e diventare un Data Engineer. La competenza principale di un Data Engineer \u00e8 la capacit\u00e0 di progettare, costruire e mantenere data warehouse.<\/p>\n<p>Ho creato un piano di studi che penso sar\u00e0 utile non solo per me. Il piano \u00e8 incentrato sullo studio autonomo dei corsi, dando priorit\u00e0 ai corsi gratuiti in lingua russa.<\/p>\n<p>Sezioni: <\/p>\n<ul>\n<li><b>Algoritmi e strutture dati. <\/b>Sezione chiave. Se la padroneggi, tutto il resto seguir\u00e0. \u00c8 importante praticare la scrittura di codice e utilizzare le principali strutture e algoritmi.<\/li>\n<li><b>Database e data warehouse, Business Intelligence.<\/b> Dagli algoritmi passiamo alla memorizzazione e all'elaborazione dei dati.<\/li>\n<li><b>Hadoop e Big Data. <\/b>Quando la base non entra nel disco rigido, o quando i dati devono essere analizzati, ma Excel non pu\u00f2 pi\u00f9 caricarli, inizia il grande data. A mio avviso, bisogna passare a questa sezione solo dopo aver approfondito le due precedenti.<\/li>\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Algoritmi e strutture dati<\/h2>\n<p>\nNel mio piano ho incluso lo studio del Python, il ripasso delle basi di matematica e algoritmi.<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/67\">Programmazione in Python<\/a><\/noindex> <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/512\">Python: basi e applicazioni<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/2461\">Algebra lineare<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/91\/\">Introduzione alla matematica discreta <\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/217\/\">Algoritmi: teoria e pratica. Metodi<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/1547\">Algoritmi: teoria e pratica. Strutture dati<\/a><\/noindex><\/li>\n<\/ul>\n<p><\/p>\n<h2>Basi di dati e archiviazione, Business Intelligence<\/h2>\n<p><\/p>\n<ul>\n<li>Libro: Martin Kleppmann \u2014 Applicazioni ad alto carico. Programmazione, scalabilit\u00e0, manutenzione. Il libro descrive come funzionano diversi modelli di dati, la loro implementazione interna, le limitazioni e le scelte in base al compito. <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/551\">Introduzione ai database <\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/3203\">Immersione nei DBMS<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/2586\">Introduzione ai database non relazionali <\/a><\/noindex><\/li>\n<\/ul>\n<p>\nI temi legati alla costruzione di data warehouse, ETL, cubi OLAP dipendono fortemente dagli strumenti, quindi in questo documento non fornisco riferimenti a corsi. \u00c8 importante studiare tali sistemi quando si lavora su un progetto specifico in una specifica azienda. Per familiarizzare con l'ETL, puoi provare <noindex><a rel=\"nofollow\" href=\"https:\/\/www.talend.com\/\">Talend<\/a><\/noindex> o <noindex><a rel=\"nofollow\" href=\"https:\/\/airflow.apache.org\/\">Airflow<\/a><\/noindex>.<\/p>\n<p>A mio avviso, \u00e8 fondamentale studiare la moderna metodologia di progettazione dei data warehouse 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 il modo migliore per apprenderlo \u00e8 prendere un esempio semplice e implementarlo. Su GitHub ci sono alcuni esempi di implementazione di Data Vault. <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/semashkinvg\/DataVault\">link<\/a><\/noindex>. Un libro moderno sui data warehouse: Modeling the Agile Data Warehouse with Data Vault di Hans Hultgren.<\/p>\n<p>Per familiarizzare con gli strumenti di Business Intelligence per gli utenti finali, puoi utilizzare il costruttore di report, dashboard e mini data warehouse gratuito Power BI Desktop. Materiali didattici: <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>Inizia con l'implementazione autonoma di MapReduce senza librerie esterne. Questo ti permetter\u00e0 in futuro di comprendere meglio le implementazioni multithreading. Un eccellente esempio in Python \u00e8 descritto <noindex><a rel=\"nofollow\" href=\"http:\/\/michaelnielsen.org\/blog\/write-your-first-mapreduce-program-in-20-minutes\/\">qui<\/a><\/noindex>.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/stepik.org\/course\/150\">Hadoop. Un sistema per l'elaborazione di grandi volumi di dati.<\/a><\/noindex> <\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/openedu.ru\/course\/misis\/ABD\/\">Introduzione all'ingegneria dei big data <\/a><\/noindex><\/li>\n<\/ul>\n<p><\/p>\n<h2>Conclusione<\/h2>\n<p>\nNon tutto ci\u00f2 che si studia pu\u00f2 essere applicato nel lavoro. Pertanto, \u00e8 necessario un progetto di diploma in cui proverai ad applicare le nuove conoscenze. <\/p>\n<p>Nel piano non ci sono argomenti legati all'analisi dei dati e al Machine Learning, poich\u00e9 questi appartengono maggiormente alla professione di Data Scientist. Inoltre, non ci sono temi legati ai cloud AWS, Azure poich\u00e9 queste tematiche dipendono fortemente dalla scelta della piattaforma.<\/p>\n<p>Domande alla comunit\u00e0:<br \/>\nQuanto \u00e8 adeguato il mio piano di sviluppo? Cosa togliere o aggiungere?<br \/>\nQuale progetto consiglieresti come lavoro di diploma?<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 4.9.10 - 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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Piano di crescita per diventare Data engineer | ProHoster","description":"Negli ultimi otto anni ho lavorato come project manager (non scrivo codice al lavoro), il che influisce naturalmente in modo negativo sul mio background tecnologico. Ho deciso di ridurre il mio gap tecnologico e di intraprendere la professione di Data engineer. La principale competenza di un Data engineer \u00e8 la capacit\u00e0 di progettare, costruire e mantenere i data warehouse. Ho redatto un piano di studio, credo sar\u00e0 utile non solo per me. 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\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. 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