{"id":77176,"date":"2020-04-08T13:42:19","date_gmt":"2020-04-08T11:42:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza"},"modified":"2020-04-08T13:42:19","modified_gmt":"2020-04-08T11:42:19","slug":"data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","title":{"rendered":"Data Engineer a Data Scientist: jak\u00fd je vlastn\u011b rozd\u00edl?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Profese Data Scientist a Data Engineer se \u010dasto zam\u011b\u0148uj\u00ed. Ka\u017ed\u00e1 spole\u010dnost m\u00e1 svou specifiku pr\u00e1ce s daty, r\u016fzn\u00e9 c\u00edle jejich anal\u00fdzy a odli\u0161n\u00e9 p\u0159edstavy o tom, kdo z odborn\u00edk\u016f by se m\u011bl zab\u00fdvat jakou \u010d\u00e1st\u00ed pr\u00e1ce, a proto si ka\u017ed\u00fd klad\u0435 sv\u00e9 vlastn\u00ed po\u017eadavky.\u00a0<\/p>\n<p>Pod\u00edv\u00e1me se na to, jak\u00fd je rozd\u00edl mezi t\u011bmito specialisty, jak\u00e9 obchodn\u00ed \u00fakoly \u0159e\u0161\u00ed, jak\u00e9 dovednosti maj\u00ed a kolik vyd\u011bl\u00e1vaj\u00ed. Materi\u00e1l je rozs\u00e1hl\u00fd, a proto jsme ho rozd\u011blili na dv\u011b publikace.<\/p>\n<p>V prvn\u00edm \u010dl\u00e1nku Elena Gerasimova, vedouc\u00ed fakulty \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/navigation\/?direction=data-science?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_all_ou&amp;utm_content=07042020_dataengscientist\">Data Science a analytika<\/a><\/noindex>\u201c v Netologii, vysv\u011btluje, jak\u00fd je rozd\u00edl mezi Data Scientist a Data Engineer a s jak\u00fdmi n\u00e1stroji pracuj\u00ed.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Jak se li\u0161\u00ed role in\u017een\u00fdr\u016f a v\u011bdc\u016f<\/h2>\n<p>\nDatov\u00fd in\u017een\u00fdr je odborn\u00edk, kter\u00fd na jedn\u00e9 stran\u011b vyv\u00edj\u00ed, testuje a udr\u017euje infrastrukturu pro pr\u00e1ci s daty: datab\u00e1ze, \u00falo\u017ei\u0161t\u011b a syst\u00e9my pro hromadn\u00e9 zpracov\u00e1n\u00ed. Na druh\u00e9 stran\u011b to je ten, kdo \u010dist\u00ed a \"vyhlazuje\" data pro pou\u017eit\u00ed analytiky a datov\u00fdmi v\u011bdci, tedy vytv\u00e1\u0159\u00ed datov\u00e9 zpracovatelsk\u00e9 kan\u00e1ly.<\/p>\n<p>Data Scientist vytv\u00e1\u0159\u00ed a tr\u00e9nuje prediktivn\u00ed (a nejen) modely pomoc\u00ed algoritm\u016f strojov\u00e9ho u\u010den\u00ed a neuronov\u00fdch s\u00edt\u00ed, \u010d\u00edm\u017e pom\u00e1h\u00e1 podnik\u016fm nach\u00e1zet skryt\u00e9 vzory, p\u0159edpov\u00eddat v\u00fdvoj ud\u00e1lost\u00ed a optimalizovat kl\u00ed\u010dov\u00e9 obchodn\u00ed procesy.<\/p>\n<p>Hlavn\u00ed rozd\u00edl mezi Data Scientist a Data Engineer spo\u010d\u00edv\u00e1 v tom, \u017ee obvykle maj\u00ed r\u016fzn\u00e9 c\u00edle. Oba pracuj\u00ed na tom, aby data byla dostupn\u00e1 a kvalitn\u00ed. Ale Data Scientist hled\u00e1 odpov\u011bdi na sv\u00e9 ot\u00e1zky a ov\u011b\u0159uje hypot\u00e9zy v datov\u00e9m ekosyst\u00e9mu (nap\u0159\u00edklad na platform\u011b Hadoop), zat\u00edmco Data Engineer vytv\u00e1\u0159\u00ed pipeline pro \u00fadr\u017ebu algoritmu strojov\u00e9ho u\u010den\u00ed, kter\u00fd napsal datov\u00fd v\u011bdec, v clustru Spark uvnit\u0159 stejn\u00e9ho ekosyst\u00e9mu.\u00a0<\/p>\n<p>Datov\u00fd in\u017een\u00fdr p\u0159in\u00e1\u0161\u00ed hodnotu podnik\u00e1n\u00ed t\u00edm, \u017ee pracuje v t\u00fdmu. Jeho \u00fakolem je vystupovat jako d\u016fle\u017eit\u00fd \u010dl\u00e1nek mezi r\u016fzn\u00fdmi \u00fa\u010dastn\u00edky: od v\u00fdvoj\u00e1\u0159\u016f po obchodn\u00ed u\u017eivatele report\u016f \u2014 a zvy\u0161ovat produktivitu analytik\u016f \u2014 od marketingov\u00fdch a produktov\u00fdch a\u017e po BI.\u00a0<\/p>\n<p>Data Scientist naopak aktivn\u011b se pod\u00edl\u00ed na strategii firmy a z\u00edsk\u00e1v\u00e1n\u00ed insight\u016f, rozhodovac\u00edm procesu, implementaci automatizace, modelov\u00e1n\u00ed a generov\u00e1n\u00ed hodnoty z dat.<br \/>\n<img decoding=\"async\" alt=\"Data Engineer a Data Scientist: jak\u00fd je vlastn\u011b rozd\u00edl?\" src=\"\/wp-content\/uploads\/2020\/04\/1ce3e892eb7a354b106dc2f6398ec5e7.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nPr\u00e1ce s daty podl\u00e9h\u00e1 principu GIGO (garbage in \u2014 garbage out): pokud analytici a datov\u00ed v\u011bdci pracuj\u00ed s nep\u0159ipraven\u00fdmi a potenci\u00e1ln\u011b nespr\u00e1vn\u00fdmi daty, v\u00fdsledky, i s nejpokro\u010dilej\u0161\u00edmi analytick\u00fdmi algoritmy, budou nespr\u00e1vn\u00e9.\u00a0<\/p>\n<p>Datov\u00ed in\u017een\u00fd\u0159i \u0159e\u0161\u00ed tento probl\u00e9m t\u00edm, \u017ee vytv\u00e1\u0159ej\u00ed pipeline pro zpracov\u00e1n\u00ed, \u010di\u0161t\u011bn\u00ed a transformaci dat, \u010d\u00edm\u017e umo\u017e\u0148uj\u00ed datov\u00e9mu v\u011bdci pracovat s kvalitn\u00edmi daty.\u00a0<\/p>\n<p>Na trhu existuje mnoho n\u00e1stroj\u016f pro pr\u00e1ci s daty, kter\u00e9 pokr\u00fdvaj\u00ed ka\u017ed\u00fd z krok\u016f: od vzniku dat po v\u00fdstup na dashboard pro p\u0159edstavenstvo. Je d\u016fle\u017eit\u00e9, aby rozhodnut\u00ed o jejich pou\u017eit\u00ed p\u0159ij\u00edmal in\u017een\u00fdr, ne proto, \u017ee je to trendy, ale proto\u017ee to skute\u010dn\u011b pom\u016f\u017ee ostatn\u00edm \u00fa\u010dastn\u00edk\u016fm procesu.\u00a0<\/p>\n<p>Pokud pot\u0159eba spole\u010dnosti spo\u010d\u00edv\u00e1 v integraci BI a ETL \u2014 nahr\u00e1v\u00e1n\u00ed dat a aktualizace zpr\u00e1v, pak je to typick\u00fd legacy z\u00e1klad, se kter\u00fdm se Data Engineer bude muset vypo\u0159\u00e1dat (je dobr\u00e9, pokud v t\u00fdmu bude je\u0161t\u011b architekt).<\/p>\n<p><b>Povinnosti Data Engineera<\/b><\/p>\n<ul>\n<li>V\u00fdvoj, konstrukce a \u00fadr\u017eba infrastruktury pro pr\u00e1ci s daty.<\/li>\n<li>Zpracov\u00e1n\u00ed chyb a vytv\u00e1\u0159en\u00ed spolehliv\u00fdch pipeline pro zpracov\u00e1n\u00ed dat.<\/li>\n<li>Uveden\u00ed nestrukturovan\u00fdch dat z r\u016fzn\u00fdch dynamick\u00fdch zdroj\u016f do podoby pot\u0159ebn\u00e9 pro pr\u00e1ci analytik\u016f.<\/li>\n<li>Poskytov\u00e1n\u00ed doporu\u010den\u00ed pro zlep\u0161en\u00ed konzistence a kvality dat.<\/li>\n<li>Zaji\u0161t\u011bn\u00ed a podpora datov\u00e9 architektury, kterou pou\u017e\u00edvaj\u00ed datov\u00ed v\u011bdci a analytici dat.<\/li>\n<li>Zpracov\u00e1n\u00ed a ukl\u00e1d\u00e1n\u00ed dat konzistentn\u011b a efektivn\u011b v distribuovan\u00e9m clusteru s des\u00edtkami nebo stovkami server\u016f.<\/li>\n<li>Hodnocen\u00ed technick\u00fdch kompromis\u016f n\u00e1stroj\u016f pro vytv\u00e1\u0159en\u00ed jednoduch\u00fdch, av\u0161ak spolehliv\u00fdch architektur, kter\u00e9 dok\u00e1\u017eou p\u0159e\u017e\u00edt selh\u00e1n\u00ed.<\/li>\n<li>Kontrola a podpora datov\u00fdch tok\u016f a souvisej\u00edc\u00edch syst\u00e9m\u016f (nastaven\u00ed monitorov\u00e1n\u00ed a upozorn\u011bn\u00ed).<\/li>\n<\/ul>\n<p>\nExistuje je\u0161t\u011b jedna specializace v r\u00e1mci dr\u00e1hy Data Engineera \u2014 ML engineer. Pokud to shrneme, tito in\u017een\u00fd\u0159i se specializuj\u00ed na uveden\u00ed model\u016f strojov\u00e9ho u\u010den\u00ed do pr\u016fmyslov\u00e9ho nasazen\u00ed a u\u017e\u00edv\u00e1n\u00ed. \u010casto je model dodan\u00fd datov\u00fdm v\u011bdcem sou\u010d\u00e1st\u00ed v\u00fdzkumu a nemus\u00ed fungovat v re\u00e1ln\u00fdch podm\u00ednk\u00e1ch.<\/p>\n<p><b>Povinnosti Data Scientist<\/b><\/p>\n<ul>\n<li>Extrakce funkc\u00ed z dat pro aplikaci algoritm\u016f strojov\u00e9ho u\u010den\u00ed.<\/li>\n<li>Pou\u017eit\u00ed r\u016fzn\u00fdch n\u00e1stroj\u016f strojov\u00e9ho u\u010den\u00ed k predikci a klasifikaci vzorc\u016f v datech.<\/li>\n<li>Zv\u00fd\u0161en\u00ed v\u00fdkonu a p\u0159esnosti algoritm\u016f strojov\u00e9ho u\u010den\u00ed d\u00edky jemn\u00e9mu lad\u011bn\u00ed a optimalizaci algoritm\u016f.<\/li>\n<li>Vytv\u00e1\u0159en\u00ed 'siln\u00fdch' hypot\u00e9z v souladu se strategi\u00ed spole\u010dnosti, kter\u00e9 je t\u0159eba ov\u011b\u0159it.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Jak Data Engineer, tak Data Scientist se spojuj\u00ed s v\u00fdrazn\u00fdm p\u0159\u00ednosem k rozvoji kultury pr\u00e1ce s daty, d\u00edky kter\u00e9 m\u016f\u017ee spole\u010dnost z\u00edsk\u00e1vat dodate\u010dn\u00fd zisk nebo sni\u017eovat n\u00e1klady.<\/p><\/blockquote>\n<p><\/p>\n<h2>S jak\u00fdmi jazyky a n\u00e1stroji pracuj\u00ed in\u017een\u00fd\u0159i a v\u011bdci?<\/h2>\n<p>\nDnes se o\u010dek\u00e1v\u00e1n\u00ed od specialist\u016f na zpracov\u00e1n\u00ed dat zm\u011bnila. D\u0159\u00edve in\u017een\u00fd\u0159i shroma\u017e\u010fovali velk\u00e9 SQL dotazy, ru\u010dn\u011b psali MapReduce a zpracov\u00e1vali data pomoc\u00ed n\u00e1stroj\u016f, jako je Informatica ETL, Pentaho ETL, Talend.\u00a0<\/p>\n<p>V roce 2020 se specialist\u00e9 neobejdou bez znalosti Pythonu a modern\u00edch n\u00e1stroj\u016f pro prov\u00e1d\u011bn\u00ed v\u00fdpo\u010dt\u016f (nap\u0159\u00edklad Airflow), porozum\u011bn\u00ed princip\u016fm pr\u00e1ce s cloudov\u00fdmi platformami (pou\u017eit\u00ed k \u00faspor\u00e1m na 'hardware', p\u0159i dodr\u017een\u00ed bezpe\u010dnostn\u00edch princip\u016f).<\/p>\n<p>SAP, Oracle, MySQL, Redis \u2014 to jsou tradi\u010dn\u00ed n\u00e1stroje pro in\u017een\u00fdry dat ve velk\u00fdch spole\u010dnostech. Jsou dobr\u00e9, ale n\u00e1klady na licence jsou tak vysok\u00e9, \u017ee se je m\u00e1 smysl u\u010dit pouze v pr\u016fmyslov\u00fdch projektech. P\u0159itom existuje bezplatn\u00e1 alternativa v podob\u011b Postgres \u2014 je zdarma a hod\u00ed se nejen pro v\u00fduku.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer a Data Scientist: jak\u00fd je vlastn\u011b rozd\u00edl?\" src=\"\/wp-content\/uploads\/2020\/04\/8a002b86ee3a18d5b232c5232a62dd3e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nHistoricky se \u010dasto objevoval po\u017eadavek na Java a Scala, i kdy\u017e s rozvojem technologi\u00ed a p\u0159\u00edstup\u016f tyto jazyky ustupuj\u00ed do pozad\u00ed.<\/p>\n<p>Nicm\u00e9n\u011b, hardcore BigData: Hadoop, Spark a dal\u0161\u00ed n\u00e1stroje ji\u017e nejsou nezbytnou podm\u00ednkou pro in\u017een\u00fdry dat, ale p\u0159edstavuj\u00ed r\u016fzn\u00e9 n\u00e1stroje pro \u0159e\u0161en\u00ed probl\u00e9m\u016f, kter\u00e9 nelze vy\u0159e\u0161it tradi\u010dn\u00edm ETL.\u00a0<\/p>\n<blockquote><p>V trendu jsou slu\u017eby, kter\u00e9 umo\u017e\u0148uj\u00ed vyu\u017e\u00edvat n\u00e1stroje bez znalosti jazyka, ve kter\u00e9m jsou naps\u00e1ny (nap\u0159\u00edklad Hadoop bez znalosti Java), stejn\u011b jako poskytov\u00e1n\u00ed hotov\u00fdch slu\u017eeb pro zpracov\u00e1n\u00ed streamovan\u00fdch dat (rozpozn\u00e1v\u00e1n\u00ed hlasu nebo obraz\u016f na videu).<\/p><\/blockquote>\n<p>\nPr\u016fmyslov\u00e1 \u0159e\u0161en\u00ed od SAS a SPSS jsou popul\u00e1rn\u00ed, p\u0159i\u010dem\u017e Tableau, Rapidminer, Stata a Julia jsou tak\u00e9 \u0161iroce pou\u017e\u00edv\u00e1ny datov\u00fdmi v\u011bdci pro m\u00edstn\u00ed \u00fakoly.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer a Data Scientist: jak\u00fd je vlastn\u011b rozd\u00edl?\" src=\"\/wp-content\/uploads\/2020\/04\/cf18b6b4fe45d9286f2ec6d14418d28c.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nMo\u017enost vytv\u00e1\u0159et pipeline se analytik\u016fm a datov\u00fdm v\u011bdc\u016fm objevila teprve p\u0159ed p\u00e1r lety: nap\u0159\u00edklad je nyn\u00ed mo\u017en\u00e9 pomoc\u00ed relativn\u011b jednoduch\u00fdch skript\u016f sm\u011brovat data do \u00falo\u017ei\u0161t\u011b zalo\u017een\u00e9ho na PostgreSQL.\u00a0<\/p>\n<p>Obvykle z\u016fst\u00e1v\u00e1 pou\u017e\u00edv\u00e1n\u00ed pipeline a integrovan\u00fdch datov\u00fdch struktur v kompetenci datov\u00fdch in\u017een\u00fdr\u016f. Ale dnes je trend na T-odborn\u00edky siln\u011bj\u0161\u00ed ne\u017e kdy d\u0159\u00edve \u2014 s \u0161irok\u00fdmi kompetencemi v p\u0159\u00edbuzn\u00fdch oblastech, jeliko\u017e se n\u00e1stroje neust\u00e1le zjednodu\u0161uj\u00ed.<\/p>\n<h2>Pro\u010d by m\u011bli Data Engineer a Data Scientist spolupracovat<\/h2>\n<p>\nPrac\u00ed v \u00fazk\u00e9 spolupr\u00e1ci s in\u017een\u00fdry se mohou datov\u00ed v\u011bdci zam\u011b\u0159it na v\u00fdzkumnou \u010d\u00e1st a vytv\u00e1\u0159et algoritmy strojov\u00e9ho u\u010den\u00ed p\u0159ipraven\u00e9 k pou\u017eit\u00ed.<br \/>\nA in\u017een\u00fd\u0159i se mohou soust\u0159edit na \u0161k\u00e1lovatelnost, znovupou\u017eit\u00ed dat a zaji\u0161t\u011bn\u00ed toho, aby pipelines pro vstup a v\u00fdstup dat v ka\u017ed\u00e9m jednotliv\u00e9m projektu vyhovovaly glob\u00e1ln\u00ed architektu\u0159e. <\/p>\n<p>Takov\u00e9 rozd\u011blen\u00ed povinnost\u00ed zaji\u0161\u0165uje konzistenci mezi skupinami odborn\u00edk\u016f, kte\u0159\u00ed pracuj\u00ed na r\u016fzn\u00fdch projektech strojov\u00e9ho u\u010den\u00ed.\u00a0<\/p>\n<p>Spolupr\u00e1ce pom\u00e1h\u00e1 efektivn\u011b vytv\u00e1\u0159et nov\u00e9 produkty. Rychlost a kvalita se dosahuj\u00ed d\u00edky rovnov\u00e1ze mezi vytv\u00e1\u0159en\u00edm slu\u017eeb pro v\u0161echny (glob\u00e1ln\u00ed \u00falo\u017ei\u0161t\u011b nebo integrace dashboard\u016f) a realizac\u00ed ka\u017ed\u00e9 konkr\u00e9tn\u00ed pot\u0159eby \u010di projektu (specializovan\u00e1 pipeline, propojen\u00ed extern\u00edch zdroj\u016f).\u00a0<\/p>\n<p>\u00dazk\u00e1 spolupr\u00e1ce s datov\u00fdmi v\u011bdci a analytiky pom\u00e1h\u00e1 in\u017een\u00fdr\u016fm rozv\u00edjet analytick\u00e9 a v\u00fdzkumn\u00e9 dovednosti pro psan\u00ed kvalitn\u011bj\u0161\u00edho k\u00f3du. Zlep\u0161uje se v\u00fdm\u011bna znalost\u00ed mezi u\u017eivateli \u00falo\u017ei\u0161\u0165 a datov\u00fdch jezer, co\u017e \u010din\u00ed projekty flexibiln\u011bj\u0161\u00edmi a zaru\u010duje trvalej\u0161\u00ed v\u00fdsledky.<\/p>\n<p>Ve firm\u00e1ch, kter\u00e9 maj\u00ed za c\u00edl rozvoj kultury pr\u00e1ce s daty a budov\u00e1n\u00ed obchodn\u00edch proces\u016f na jejich z\u00e1klad\u011b, se datov\u00ed v\u011bdci a datov\u00ed in\u017een\u00fd\u0159i vz\u00e1jemn\u011b dopl\u0148uj\u00ed a vytv\u00e1\u0159ej\u00ed komplexn\u00ed syst\u00e9m anal\u00fdzy dat.\u00a0<\/p>\n<p>V n\u00e1sleduj\u00edc\u00edm materi\u00e1lu se pod\u00edv\u00e1me na to, jak\u00e9 vzd\u011bl\u00e1n\u00ed by m\u011bli m\u00edt datov\u00ed in\u017een\u00fd\u0159i a datov\u00ed v\u011bdci, jak\u00e9 dovednosti by m\u011bli rozv\u00edjet a jak\u00fd je trh.<\/p>\n<h2>Od redakce Netologie<\/h2>\n<p>\nPokud se zva\u017eujete povol\u00e1n\u00ed Data Engineera nebo Data Scientista, zveme v\u00e1s k prozkoum\u00e1n\u00ed na\u0161ich kurz\u016f:<\/p>\n<ul>\n<li>Povol\u00e1n\u00ed \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/data-engineer?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_deg_ou&amp;utm_content=07042020_dataengscientist\">Data in\u017een\u00fdr<\/a><\/noindex>\u00bb.\u00a0\u00a0<\/li>\n<li>Povol\u00e1n\u00ed \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/data-scientist\/?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_ds_ou&amp;utm_content=07042020_dataengscientist\">Data Scientist<\/a><\/noindex>\u00bb.<\/li>\n<\/ul>\n<p>Zdroj: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/netologyru\/blog\/496082\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u0438 Data Scientist \u0438 Data Engineer \u0447\u0430\u0441\u0442\u043e \u043f\u0443\u0442\u0430\u044e\u0442. \u0423 \u043a\u0430\u0436\u0434\u043e\u0439 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 \u0441\u0432\u043e\u044f \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u043a\u0430 \u0440\u0430\u0431\u043e\u0442\u044b \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438, \u0440\u0430\u0437\u043d\u044b\u0435 \u0446\u0435\u043b\u0438 \u0438\u0445 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0438 \u0440\u0430\u0437\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435, \u043a\u0442\u043e \u0438\u0437 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u043e\u0432 \u043a\u0430\u043a\u043e\u0439 \u0447\u0430\u0441\u0442\u044c\u044e \u0440\u0430\u0431\u043e\u0442\u044b \u0434\u043e\u043b\u0436\u0435\u043d \u0437\u0430\u043d\u0438\u043c\u0430\u0442\u044c\u0441\u044f, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0438 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u044f \u043a\u0430\u0436\u0434\u044b\u0439 \u043f\u0440\u0435\u0434\u044a\u044f\u0432\u043b\u044f\u0435\u0442 \u0441\u0432\u043e\u0438.\u00a0 \u0420\u0430\u0437\u0431\u0438\u0440\u0430\u0435\u043c\u0441\u044f, \u0432 \u0447\u0451\u043c \u0440\u0430\u0437\u043d\u0438\u0446\u0430 \u044d\u0442\u0438\u0445 \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u043e\u0432, \u043a\u0430\u043a\u0438\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u0431\u0438\u0437\u043d\u0435\u0441\u0430 \u043e\u043d\u0438 \u0440\u0435\u0448\u0430\u044e\u0442, \u043a\u0430\u043a\u0438\u043c\u0438 \u043d\u0430\u0432\u044b\u043a\u0430\u043c\u0438 \u043e\u0431\u043b\u0430\u0434\u0430\u044e\u0442 \u0438 \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0437\u0430\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u044e\u0442. \u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":77177,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-77176","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=\"\u041f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u0438 Data Scientist \u0438 Data Engineer \u0447\u0430\u0441\u0442\u043e \u043f\u0443\u0442\u0430\u044e\u0442.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/prohoster.info\/cs\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza\" \/>\n\t\t<meta 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