{"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\/sk\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","title":{"rendered":"Data Engineer a Data Scientist: ak\u00fd je vlastne rozdiel?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Povolania Data Scientist a Data Engineer sa \u010dasto zamie\u0148aj\u00fa. Ka\u017ed\u00e1 spolo\u010dnos\u0165 m\u00e1 svoju \u0161pecifiku pr\u00e1ce s \u00fadajmi, r\u00f4zne ciele ich analyzovania a odli\u0161n\u00e9 predstavy o tom, kto z odborn\u00edkov by sa mal zamera\u0165 na ak\u00fa \u010das\u0165 pr\u00e1ce, a preto m\u00e1 ka\u017ed\u00e1 po\u017eiadavka svoje vlastn\u00e9 krit\u00e9ri\u00e1.\u00a0<\/p>\n<p>Vysvetl\u00edme, ak\u00fd je rozdiel medzi t\u00fdmito odborn\u00edkmi, ak\u00e9 \u00falohy v podnikan\u00ed rie\u0161ia, ak\u00e9 zru\u010dnosti maj\u00fa a ko\u013eko zar\u00e1baj\u00fa. Materi\u00e1l sa uk\u00e1zal by\u0165 rozsiahly, preto sme ho rozdelili na dva \u010dl\u00e1nky.<\/p>\n<p>V prvom \u010dl\u00e1nku Elena Gerasimova, ved\u00faca 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 na Netol\u00f3gii, vysvet\u013euje, ak\u00fd je rozdiel medzi Data Scientist a Data Engineer a s ak\u00fdmi n\u00e1strojmi pracuj\u00fa.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Ako sa rozli\u0161uj\u00fa \u00falohy in\u017einierov a scientistov<\/h2>\n<p>\nIn\u017einier \u00fadajov je odborn\u00edk, ktor\u00fd na jednej strane vyv\u00edja, testuje a udr\u017eiava infra\u0161trukt\u00faru pr\u00e1ce s \u00fadajmi: datab\u00e1zy, \u00falo\u017eisk\u00e1 a syst\u00e9my hromadn\u00e9ho spracovania. Na druhej strane je to ten, kto \u010dist\u00ed a \u201eupratuje\u201c \u00fadaje pre pou\u017eitie analytikmi a d\u00e1tov\u00fdmi vedcami, teda vytv\u00e1ra potrubia spracovania \u00fadajov.<\/p>\n<p>Data Scientist vytv\u00e1ra a tr\u00e9nuje predikt\u00edvne (a nielen) modely pomocou algoritmov strojov\u00e9ho u\u010denia a neur\u00f3nov\u00fdch siet\u00ed, \u010d\u00edm pom\u00e1ha podniku odha\u013eova\u0165 skryt\u00e9 vzory, predpoveda\u0165 v\u00fdvoj udalost\u00ed a optimalizova\u0165 k\u013e\u00fa\u010dov\u00e9 obchodn\u00e9 procesy.<\/p>\n<p>Hlavn\u00fd rozdiel medzi Data Scientist a Data Engineer spo\u010d\u00edva v tom, \u017ee zvy\u010dajne maj\u00fa r\u00f4zne ciele. Obe sa sna\u017eia o to, aby \u00fadaje boli dostupn\u00e9 a kvalitn\u00e9. Ale Data Scientist h\u013ead\u00e1 odpovede na svoje ot\u00e1zky a testuje hypot\u00e9zy v ekosyst\u00e9me \u00fadajov (napr\u00edklad na z\u00e1klade Hadoop), zatia\u013e \u010do Data Engineer vytv\u00e1ra pipeline pre \u00fadr\u017ebu algoritmu strojov\u00e9ho u\u010denia, ktor\u00fd nap\u00edsal d\u00e1tov\u00fd vedec, v klastri Spark v r\u00e1mci toho ist\u00e9ho ekosyst\u00e9mu.\u00a0<\/p>\n<p>In\u017einier \u00fadajov prin\u00e1\u0161a hodnotu podnikaniu pracuj\u00fac v t\u00edme. Jeho \u00falohou je vystupova\u0165 ako d\u00f4le\u017eit\u00e9 spojivo medzi r\u00f4znymi \u00fa\u010dastn\u00edkmi: od v\u00fdvoj\u00e1rov a\u017e po obchodn\u00fdch spotrebite\u013eov reportov, a zvy\u0161ova\u0165 produktivitu analytikov \u2014 od marketingov\u00fdch a produktov\u00fdch a\u017e po BI.\u00a0<\/p>\n<p>Data Scientist, naopak, akt\u00edvne sa podie\u013ea na strat\u00e9gii spolo\u010dnosti a z\u00edskavan\u00ed poznatkov, rozhodovan\u00ed, implement\u00e1cii automatiza\u010dn\u00fdch algoritmov, modelovan\u00ed a generovan\u00ed hodnoty z \u00fadajov.<br \/>\n<img decoding=\"async\" alt=\"Data Engineer a Data Scientist: ak\u00fd je vlastne rozdiel?\" src=\"\/wp-content\/uploads\/2020\/04\/1ce3e892eb7a354b106dc2f6398ec5e7.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nPr\u00e1ca s d\u00e1tami podlieha princ\u00edpu GIGO (garbage in \u2014 garbage out): ak analytici a d\u00e1tov\u00ed vedci pracuj\u00fa s nepripraven\u00fdmi a potenci\u00e1lne nespr\u00e1vnymi \u00fadajmi, v\u00fdsledky aj s najsofistikovanej\u0161\u00edmi analytick\u00fdmi algoritmami bud\u00fa chybn\u00fdmi.\u00a0<\/p>\n<p>D\u00e1tov\u00ed in\u017einieri rie\u0161ia tento probl\u00e9m budovan\u00edm potrub\u00ed na spracovanie, \u010distenie a transform\u00e1ciu d\u00e1t, \u010do umo\u017e\u0148uje d\u00e1tov\u00e9mu vedcovi pracova\u0165 u\u017e s kvalitn\u00fdmi \u00fadajmi.\u00a0<\/p>\n<p>Na trhu je ve\u013ea n\u00e1strojov na pr\u00e1cu s d\u00e1tami, ktor\u00e9 pokr\u00fdvaj\u00fa ka\u017ed\u00fd z krokov: od vzniku d\u00e1t a\u017e po zobrazenie na paneli pre v\u00fdbor riadite\u013eov. D\u00f4le\u017eit\u00e9 je, aby rozhodnutie o ich pou\u017eit\u00ed prijal in\u017einier, nie preto, \u017ee je to trendy, ale preto, \u017ee to naozaj pom\u00f4\u017ee ostatn\u00fdm \u00fa\u010dastn\u00edkom procesu.\u00a0<\/p>\n<p>Podmienene: ak spolo\u010dnosti potrebuj\u00fa zbl\u00ed\u017ei\u0165 BI a ETL \u2014 na\u010d\u00edtanie d\u00e1t a aktualiz\u00e1cie spr\u00e1v, tu je typick\u00fd legacy z\u00e1klad, s ktor\u00fdm sa bude musie\u0165 Data Engineer zaobera\u0165 (je dobr\u00e9, ak v t\u00edme okrem neho bude aj architekt).<\/p>\n<p><b>Povinnosti Data Engineer<\/b><\/p>\n<ul>\n<li>V\u00fdvoj, stavba a \u00fadr\u017eba infra\u0161trukt\u00fary na pr\u00e1cu s d\u00e1tami.<\/li>\n<li>Spracovanie ch\u00fdb a vytv\u00e1ranie spo\u013eahliv\u00fdch potrub\u00ed na spracovanie d\u00e1t.<\/li>\n<li>Prisp\u00f4sobenie \u0161trukt\u00farovan\u00fdch d\u00e1t z r\u00f4znych dynamick\u00fdch zdrojov do podoby potrebnej pre pr\u00e1cu analytikov.<\/li>\n<li>Poskytovanie odpor\u00fa\u010dan\u00ed na zv\u00fd\u0161enie konzistencie a kvality d\u00e1t.<\/li>\n<li>Zabezpe\u010denie a podpora architekt\u00fary d\u00e1t pou\u017e\u00edvan\u00fdch d\u00e1tov\u00fdmi vedcami a analytikmi d\u00e1t.<\/li>\n<li>Spracovanie a ukladanie d\u00e1t postupne a efekt\u00edvne v distribuovanom klastere na desiatky alebo stovky serverov.<\/li>\n<li>Hodnotenie technick\u00fdch kompromise n\u00e1strojov na vytv\u00e1ranie jednoduch\u00fdch, ale spo\u013eahliv\u00fdch architekt\u00far, ktor\u00e9 pre\u017eij\u00fa zlyhania.<\/li>\n<li>Kontrola a podpora d\u00e1tov\u00fdch tokov a s\u00favisiacich syst\u00e9mov (nastavenie monitorovania a upozornen\u00ed).<\/li>\n<\/ul>\n<p>\nExistuje e\u0161te jedna \u0161pecializ\u00e1cia v r\u00e1mci dr\u00e1hy Data Engineer \u2014 ML engineer. Skr\u00e1tka, t\u00edto in\u017einieri sa \u0161pecializuj\u00fa na priv\u00e1dzanie modelov strojov\u00e9ho u\u010denia do priemyseln\u00fdch implement\u00e1ci\u00ed a pou\u017e\u00edvania. \u010casto model od d\u00e1tov\u00e9ho vedca je \u010das\u0165ou v\u00fdskumu a nemus\u00ed fungova\u0165 v ostr\u00fdch podmienkach.<\/p>\n<p><b>Povinnosti Data Scientist<\/b><\/p>\n<ul>\n<li>Extrahovanie charakterist\u00edk z d\u00e1t na aplik\u00e1ciu algoritmov strojov\u00e9ho u\u010denia.<\/li>\n<li>Pou\u017e\u00edvanie r\u00f4znych n\u00e1strojov strojov\u00e9ho u\u010denia na predpovedanie a klasifik\u00e1ciu vzorov v \u00fadajoch.<\/li>\n<li>Zv\u00fd\u0161enie v\u00fdkonnosti a presnosti algoritmov strojov\u00e9ho u\u010denia prostredn\u00edctvom jemn\u00e9ho ladenia a optimaliz\u00e1cie algoritmov.<\/li>\n<li>Formulovanie \u201asiln\u00fdch\u2018 hypot\u00e9z v s\u00falade so strat\u00e9giou spolo\u010dnosti, ktor\u00e9 je potrebn\u00e9 overi\u0165.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Data Engineer a Data Scientist sa zhoduj\u00fa vo v\u00fdraznom pr\u00edspevku k rozvoju kult\u00fary pr\u00e1ce s \u00fadajmi, s ktorou m\u00f4\u017ee spolo\u010dnos\u0165 z\u00edska\u0165 dodato\u010dn\u00fd zisk alebo zn\u00ed\u017ei\u0165 n\u00e1klady.<\/p><\/blockquote>\n<p><\/p>\n<h2>S ktor\u00fdmi jazykmi a n\u00e1strojmi pracuj\u00fa in\u017einieri a vedci?<\/h2>\n<p>\nDnes sa o\u010dak\u00e1vania od \u0161pecialistov na spracovanie \u00fadajov zmenili. K\u00fdm predt\u00fdm in\u017einieri zostavovali ve\u013ek\u00e9 SQL dotazy, manu\u00e1lne p\u00edsali MapReduce a spracov\u00e1vali \u00fadaje pomocou n\u00e1strojov ako Informatica ETL, Pentaho ETL, Talend.\u00a0<\/p>\n<p>V roku 2020 sa odborn\u00edk nem\u00f4\u017ee zaob\u00eds\u0165 bez znalosti Pythonu a modern\u00fdch n\u00e1strojov na vykon\u00e1vanie v\u00fdpo\u010dtov (napr\u00edklad Airflow), ako aj porozumenia princ\u00edpom pr\u00e1ce s cloudov\u00fdmi platformami (ich vyu\u017e\u00edvanie na \u00fasporu na \u201eharde\u201c, pri dodr\u017ean\u00ed z\u00e1sad bezpe\u010dnosti).<\/p>\n<p>SAP, Oracle, MySQL, Redis \u2014 to s\u00fa tradi\u010dn\u00e9 n\u00e1stroje data in\u017einiera vo ve\u013ek\u00fdch spolo\u010dnostiach. S\u00fa dobr\u00e9, ale n\u00e1klady na licencie s\u00fa tak\u00e9 vysok\u00e9, \u017ee m\u00e1 zmysel u\u010di\u0165 sa s nimi pracova\u0165 iba v priemyseln\u00fdch projektoch. Existuje v\u0161ak bezplatn\u00e1 alternat\u00edva v podobe Postgresu \u2014 je bezplatn\u00fd a hod\u00ed sa nielen na vzdel\u00e1vanie.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer a Data Scientist: ak\u00fd je vlastne rozdiel?\" src=\"\/wp-content\/uploads\/2020\/04\/8a002b86ee3a18d5b232c5232a62dd3e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nHistoricky sa \u010dasto vyskytuje dopyt po Jave a Scale, aj ke\u010f s rozvojom technol\u00f3gi\u00ed a pr\u00edstupov tieto jazyky ustupuj\u00fa do pozadia.<\/p>\n<p>Napriek tomu hardcore BigData: Hadoop, Spark a \u010fal\u0161\u00ed zverinec u\u017e nie s\u00fa nevyhnutnou podmienkou pre data in\u017einiera, sk\u00f4r s\u00fa to r\u00f4zne n\u00e1stroje na rie\u0161enie probl\u00e9mov, ktor\u00e9 nie s\u00fa rie\u0161ite\u013en\u00e9 tradi\u010dn\u00fdm ETL.\u00a0<\/p>\n<blockquote><p>V trende s\u00fa slu\u017eby na pou\u017e\u00edvanie n\u00e1strojov bez znalosti jazyka, v ktorom s\u00fa nap\u00edsan\u00e9 (napr\u00edklad Hadoop bez znalosti Javy), ako aj poskytovanie hotov\u00fdch slu\u017eieb na spracovanie streamingov\u00fdch \u00fadajov (rozpozn\u00e1vanie hlasu alebo obrazov vo videu).<\/p><\/blockquote>\n<p>\nPriemyseln\u00e9 rie\u0161enia od SAS a SPSS s\u00fa popul\u00e1rne, pri\u010dom Tableau, Rapidminer, Stata a Julia sa tie\u017e \u0161iroko pou\u017e\u00edvaj\u00fa d\u00e1tov\u00fdmi vedcami na lok\u00e1lne \u00falohy.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer a Data Scientist: ak\u00fd je vlastne rozdiel?\" src=\"\/wp-content\/uploads\/2020\/04\/cf18b6b4fe45d9286f2ec6d14418d28c.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nMo\u017enos\u0165 samostatne budova\u0165 pipeline sa analytikom a d\u00e1tov\u00fdm vedcom objavila len pred p\u00e1r rokmi: napr\u00edklad, u\u017e je mo\u017en\u00e9 pomocou relat\u00edvne jednoduch\u00fdch skriptov posiela\u0165 d\u00e1ta do \u00falo\u017eiska na b\u00e1ze PostgreSQL.\u00a0<\/p>\n<p>Zvy\u010dajne je pou\u017e\u00edvanie potrub\u00ed a integrovan\u00fdch \u0161trukt\u00far d\u00e1t v kompetencii d\u00e1tov\u00fdch in\u017einierov. Dnes je v\u0161ak trend na T-typov odborn\u00edkov silnej\u0161\u00ed ako kedyko\u013evek predt\u00fdm \u2013 s \u0161irok\u00fdmi kompetenciami v pr\u00edbuzn\u00fdch oblastiach, preto\u017ee n\u00e1stroje sa neust\u00e1le zjednodu\u0161uj\u00fa.<\/p>\n<h2>Pre\u010do by mal Data Engineer a Data Scientist spolupracova\u0165<\/h2>\n<p>\nPr\u00e1ca v tesnej spolupr\u00e1ci s in\u017einiermi umo\u017e\u0148uje d\u00e1tov\u00fdm vedcom s\u00fastredi\u0165 sa na v\u00fdskumn\u00fa \u010das\u0165, vytv\u00e1raj\u00fac funk\u010dn\u00e9 algoritmy strojov\u00e9ho u\u010denia.<br \/>\nIn\u017einieri sa m\u00f4\u017eu s\u00fastredi\u0165 na \u0161k\u00e1lovate\u013enos\u0165, op\u00e4tovn\u00e9 pou\u017eitie d\u00e1t a zabezpe\u010di\u0165, aby potrubia na vstup a v\u00fdstup d\u00e1t v ka\u017edom projekte zodpovedali glob\u00e1lnej architekt\u00fare. <\/p>\n<p>Tak\u00e9to rozdelenie povinnost\u00ed zabezpe\u010duje s\u00falad medzi skupinami odborn\u00edkov pracuj\u00facich na r\u00f4znych projektoch strojov\u00e9ho u\u010denia.\u00a0<\/p>\n<p>Spolupr\u00e1ca pom\u00e1ha efekt\u00edvne vytv\u00e1ra\u0165 nov\u00e9 produkty. R\u00fdchlos\u0165 a kvalita s\u00fa dosahovan\u00e9 v\u010faka rovnov\u00e1he medzi vytv\u00e1ran\u00edm slu\u017eby pre v\u0161etk\u00fdch (glob\u00e1lne \u00falo\u017eisko alebo integr\u00e1cia dashboardov) a realiz\u00e1ciou ka\u017edej konkr\u00e9tnej potreby alebo projektu (\u0161pecializovan\u00fd pipeline, pripojenie extern\u00fdch zdrojov).\u00a0<\/p>\n<p>Tesn\u00e1 spolupr\u00e1ca s d\u00e1tov\u00fdmi vedcami a analytikmi pom\u00e1ha in\u017einierom rozv\u00edja\u0165 analytick\u00e9 a v\u00fdskumn\u00e9 zru\u010dnosti na p\u00edsanie kvalitnej\u0161ieho k\u00f3du. Zlep\u0161uje sa v\u00fdmena vedomost\u00ed medzi pou\u017e\u00edvate\u013emi \u00falo\u017e\u00edsk a d\u00e1tov\u00fdch jazier, \u010do rob\u00ed projekty flexibilnej\u0161\u00edmi a zabezpe\u010duje trv\u00e1cnej\u0161ie v\u00fdsledky.<\/p>\n<p>Vo firm\u00e1ch, ktor\u00e9 si klad\u00fa za cie\u013e rozvoj kult\u00fary pr\u00e1ce s d\u00e1tami a budovanie obchodn\u00fdch procesov na ich z\u00e1klade, sa d\u00e1tov\u00ed vedci a d\u00e1tov\u00ed in\u017einieri navz\u00e1jom dop\u013a\u0148aj\u00fa a vytv\u00e1raj\u00fa komplexn\u00fd syst\u00e9m anal\u00fdzy d\u00e1t.\u00a0<\/p>\n<p>V nasleduj\u00facom materi\u00e1li sa budeme zaobera\u0165 t\u00fdm, ak\u00e9 vzdelanie by mali ma\u0165 d\u00e1tov\u00ed in\u017einieri a d\u00e1tov\u00ed vedci, ak\u00e9 zru\u010dnosti by mali rozv\u00edja\u0165 a ako je usporiadan\u00fd trh.<\/p>\n<h2>Od redakcie Netology<\/h2>\n<p>\nAk sa zauj\u00edmate o profesiu d\u00e1tov\u00e9ho in\u017einiera alebo d\u00e1tov\u00e9ho vedca, poz\u00fdvame v\u00e1s presk\u00fama\u0165 programy na\u0161ich kurzov:<\/p>\n<ul>\n<li>Profesia \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\">D\u00e1tov\u00fd in\u017einier<\/a><\/noindex>\u00bb.\u00a0\u00a0<\/li>\n<li>Profesia \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\">D\u00e1tov\u00fd vedec<\/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 5.0.5 - 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\/sk\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza\" \/>\n\t\t<meta 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