{"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\/lv\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","title":{"rendered":"Datu in\u017eenieris un Datu zin\u0101tnieks: kas visp\u0101r ir at\u0161\u0137ir\u012bba?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Datu zin\u0101tnieka un datu in\u017eeniera profesijas bie\u017ei tiek jauktas. Katram uz\u0146\u0113mumam ir sava specifika darb\u0101 ar datiem, at\u0161\u0137ir\u012bgi m\u0113r\u0137i to anal\u012bzei un at\u0161\u0137ir\u012bgs priek\u0161stats, kur\u0161 speci\u0101lists ar kuru da\u013cu darba j\u0101nodarbojas, t\u0101p\u0113c ar\u012b pras\u012bbas katram ir savas.\u00a0<\/p>\n<p>Izp\u0113t\u012bsim, k\u0101da ir \u0161o speci\u0101listu at\u0161\u0137ir\u012bba, k\u0101das biznesa probl\u0113mas vi\u0146i risina, k\u0101das prasmes vi\u0146iem ir un cik vi\u0146i pelna. Materi\u0101ls izr\u0101d\u012bj\u0101s liels, t\u0101p\u0113c m\u0113s to sadal\u012bj\u0101m div\u0101s publik\u0101cij\u0101s.<\/p>\n<p>Pirmaj\u0101 rakst\u0101 Elena Gerasimova, \"<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\">Datu zin\u0101tne un analitika<\/a><\/noindex>\" fakult\u0101tes vad\u012bt\u0101ja Netolo\u0123ij\u0101, st\u0101sta par at\u0161\u0137ir\u012bbu starp Datu zin\u0101tnieku un Datu in\u017eenieri un ar k\u0101diem r\u012bkiem vi\u0146i str\u0101d\u0101.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>K\u0101 at\u0161\u0137iras in\u017eenieru un zin\u0101tnieku lomas<\/h2>\n<p>\nDatu in\u017eenieris ir speci\u0101lists, kur\u0161, no vienas puses, izstr\u0101d\u0101, test\u0113 un uztur datu apstr\u0101des infrastrukt\u016bru: datu b\u0101zes, kr\u0101tuves un masu apstr\u0101des sist\u0113mas. No otras puses \u2014 tas, kur\u0161 att\u012bra un \"k\u0101rto\" datus anal\u012bti\u0137u un datu zin\u0101tnieku izmanto\u0161anai, t\u0101tad izveido datu apstr\u0101des konveijerus.<\/p>\n<p>Datu zin\u0101tnieks izveido un apm\u0101ca prognoz\u0113jo\u0161os (un ne tikai) mode\u013cus, izmantojot ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s un neironu t\u012bklu algoritmus, pal\u012bdzot biznesam atrast sl\u0113ptas likumsakar\u012bbas, prognoz\u0113t notikumu att\u012bst\u012bbu un optimiz\u0113t galvenos biznesa procesus.<\/p>\n<p>Galven\u0101 at\u0161\u0137ir\u012bba starp Datu zin\u0101tnieku un Datu in\u017eenieri ir t\u0101, ka parasti vi\u0146iem ir at\u0161\u0137ir\u012bgi m\u0113r\u0137i. Abi str\u0101d\u0101, lai dati b\u016btu pieejami un kvalitat\u012bvi. Bet Datu zin\u0101tnieks atrod atbildes uz saviem jaut\u0101jumiem un p\u0101rbauda hipotezes datu ekosist\u0113m\u0101 (piem\u0113ram, uz Hadoop b\u0101zes), bet Datu in\u017eenieris izstr\u0101d\u0101 ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s algoritma apkalpo\u0161anas cauru\u013cvadu, ko uzrakst\u012bjis datu zin\u0101tnieks, Spark klaster\u012b iek\u0161\u0113j\u0101 taj\u0101 pa\u0161\u0101 ekosist\u0113m\u0101.\u00a0<\/p>\n<p>Datu in\u017eenieris nes v\u0113rt\u012bbu biznes\u0101, str\u0101d\u0101jot komand\u0101. Vi\u0146a uzdevums ir b\u016bt svar\u012bgai saitei starp da\u017e\u0101diem dal\u012bbniekiem: no izstr\u0101d\u0101t\u0101jiem l\u012bdz biznesa datu pat\u0113r\u0113t\u0101jiem \u2014 un palielin\u0101t anal\u012bti\u0137u produktivit\u0101ti \u2014 no m\u0101rketinga un produktu l\u012bdz BI.\u00a0<\/p>\n<p>Datu zin\u0101tnieks, no otras puses, akt\u012bvi piedal\u0101s uz\u0146\u0113muma strat\u0113\u0123ij\u0101 un ieskat\u012bjumu g\u016b\u0161an\u0101, l\u0113mumu pie\u0146em\u0161an\u0101, automatiz\u0101cijas, model\u0113\u0161anas algoritmu ievie\u0161an\u0101 un v\u0113rt\u012bbas \u0123ener\u0113\u0161an\u0101 no datiem.<br \/>\n<img decoding=\"async\" alt=\"Datu in\u017eenieris un Datu zin\u0101tnieks: kas visp\u0101r ir at\u0161\u0137ir\u012bba?\" src=\"\/wp-content\/uploads\/2020\/04\/1ce3e892eb7a354b106dc2f6398ec5e7.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nDarba ar datiem pak\u013caujas GIGO principam (garbage in \u2014 garbage out): ja anal\u012bti\u0137i un datu zin\u0101tnieki str\u0101d\u0101 ar neapstr\u0101d\u0101tiem un potenci\u0101li nepareiziem datiem, tad pat visizsmalcin\u0101t\u0101kie anal\u012bzes algoritmi sniegs nepareizus rezult\u0101tus.\u00a0<\/p>\n<p>Datu in\u017eenieri risina \u0161o probl\u0113mu, izveidojot datu apstr\u0101des, att\u012br\u012b\u0161anas un transform\u0101cijas cauru\u013cvadus, \u013caujot datu zin\u0101tniekam str\u0101d\u0101t ar kvalitat\u012bviem datiem.\u00a0<\/p>\n<p>Tirg\u016b ir daudz r\u012bku datu apstr\u0101dei, kas aptver katru posmu: s\u0101kot no datu par\u0101d\u012b\u0161an\u0101s l\u012bdz to r\u0101d\u012b\u0161anai vad\u012bbas panel\u012b. Un ir svar\u012bgi, lai l\u0113mumu par to izmanto\u0161anu pie\u0146em in\u017eenieris, \u2014 nevis t\u0101p\u0113c, ka t\u0101 ir mod\u0113, bet gan t\u0101p\u0113c, ka tas patie\u0161\u0101m pal\u012bdz\u0113s citiem procesa dal\u012bbniekiem.\u00a0<\/p>\n<p>Nosac\u012bti: ja uz\u0146\u0113mumam j\u0101apvieno BI un ETL \u2014 datu iel\u0101de un zi\u0146ojumu atjaunin\u0101\u0161ana, \u0161is ir tipisks mantojuma pamats, ar kuru b\u016bs j\u0101str\u0101d\u0101 Datu in\u017eenierim (\u012bpa\u0161i labi, ja komand\u0101 vi\u0146am b\u016bs ar\u012b arhitekts).<\/p>\n<p><b>Datu in\u017eeniera pien\u0101kumi<\/b><\/p>\n<ul>\n<li>Datu darba infrastrukt\u016bras izstr\u0101de, izveide un apkalpo\u0161ana.<\/li>\n<li>K\u013c\u016bdu apstr\u0101de un uzticamu datu apstr\u0101des cauru\u013cvadu izveide.<\/li>\n<li>Neorganiz\u0113tu datu no da\u017e\u0101diem dinamiskajiem avotiem piel\u0101go\u0161ana anal\u012bti\u0137u vajadz\u012bb\u0101m.<\/li>\n<li>Ieteikumu snieg\u0161ana datu konsistences un kvalit\u0101tes uzlabo\u0161anai.<\/li>\n<li>Datu arhitekt\u016bras nodro\u0161in\u0101\u0161ana un uztur\u0113\u0161ana, ko izmanto datu zin\u0101tnieki un anal\u012bti\u0137i.<\/li>\n<li>Datu apstr\u0101de un uzglab\u0101\u0161ana sec\u012bgi un efekt\u012bvi izplat\u012bt\u0101 klaster\u012b uz desmitiem vai simtiem serveru.<\/li>\n<li>Tehnisko kompromisu izv\u0113rt\u0113\u0161ana r\u012bkos, lai izveidotu vienk\u0101r\u0161as, bet uzticamas arhitekt\u016bras, kas sp\u0113s iztur\u0113t neparedz\u0113tas situ\u0101cijas.<\/li>\n<li>Datu pl\u016bsmu un saist\u012bto sist\u0113mu uzraudz\u012bba un atbalsts (uzraudz\u012bbas un br\u012bdin\u0101jumu iestat\u012b\u0161ana).<\/li>\n<\/ul>\n<p>\nPast\u0101v ar\u012b cita specializ\u0101cija Datu in\u017eeniera ce\u013c\u0101 \u2014 ML in\u017eenieris. \u012asum\u0101, \u0161ie in\u017eenieri specializ\u0113jas ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s mode\u013cu piel\u0101go\u0161an\u0101 r\u016bpnieciskai ievie\u0161anai un lieto\u0161anai. Bie\u017ei modelis, kas n\u0101k no datu zin\u0101tnieka, ir p\u0113t\u012bjuma da\u013ca un var nedarboties re\u0101los apst\u0101k\u013cos.<\/p>\n<p><b>Datu zin\u0101tnieka pien\u0101kumi<\/b><\/p>\n<ul>\n<li>Funkciju izguve no datiem ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s algoritmu pielieto\u0161anai.<\/li>\n<li>Da\u017e\u0101du ma\u0161\u012bnm\u0101c\u012bbas r\u012bku izmanto\u0161ana datu mode\u013cu prognoz\u0113\u0161anai un klasific\u0113\u0161anai.<\/li>\n<li>Algoritmu ma\u0161\u012bnm\u0101c\u012bb\u0101 veiktsp\u0113jas un precizit\u0101tes palielin\u0101\u0161ana, optimiz\u0113jot un piel\u0101gojot algoritmus.<\/li>\n<li>Sp\u0113c\u012bgu hipot\u0113\u017eu izstr\u0101de saska\u0146\u0101 ar uz\u0146\u0113muma strat\u0113\u0123iju, kuras j\u0101test\u0113.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Gan Data Engineer, gan Data Scientist savieno iev\u0113rojams ieguld\u012bjums datu p\u0101rvald\u012bbas kult\u016bras att\u012bst\u012bb\u0101, kas uz\u0146\u0113mumam \u013cauj g\u016bt papildu pe\u013c\u0146u vai samazin\u0101t izdevumus.<\/p><\/blockquote>\n<p><\/p>\n<h2>Ar k\u0101diem valod\u0101m un r\u012bkiem str\u0101d\u0101 in\u017eenieri un zin\u0101tnieki.<\/h2>\n<p>\n\u0160odien gaidas no datu apstr\u0101d\u0101t\u0101jiem ir main\u012bju\u0161\u0101s. Agr\u0101k in\u017eenieri veidoja lielus SQL piepras\u012bjumus, manu\u0101li rakst\u012bja MapReduce un apstr\u0101d\u0101ja datus ar t\u0101diem r\u012bkiem k\u0101 Informatica ETL, Pentaho ETL, Talend.\u00a0<\/p>\n<p>2020. gad\u0101 speci\u0101listam ir nepiecie\u0161amas zin\u0101\u0161anas par Python un m\u016bsdienu apr\u0113\u0137inu r\u012bkiem (piem\u0113ram, Airflow), k\u0101 ar\u012b izpratne par m\u0101ko\u0146platformu darb\u012bbas principiem (to izmanto\u0161ana, lai ietaup\u012btu uz \u201edzel\u017eiem\u201d, vienlaikus iev\u0113rojot dro\u0161\u012bbas principus).<\/p>\n<p>SAP, Oracle, MySQL, Redis - tie ir tradicion\u0101li in\u017eeniera datiem r\u012bki liel\u0101s komp\u0101nij\u0101s. Tie ir labi, ta\u010du licen\u010du izmaksas ir tik augstas, ka iem\u0101c\u012bties str\u0101d\u0101t ar tiem ir j\u0113ga tikai r\u016bpnieciskos projektos. Tom\u0113r ir bezmaksas alternat\u012bva Postgres veid\u0101 - tas ir bez maksas un der\u012bgs ne tikai m\u0101c\u012bb\u0101m.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"Datu in\u017eenieris un Datu zin\u0101tnieks: kas visp\u0101r ir at\u0161\u0137ir\u012bba?\" src=\"\/wp-content\/uploads\/2020\/04\/8a002b86ee3a18d5b232c5232a62dd3e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nV\u0113sturiski bie\u017ei tiek piepras\u012btas Java un Scala, lai gan ar tehnolo\u0123iju un pieeju att\u012bst\u012bbu \u0161ie valodas atk\u0101pjas otraj\u0101 pl\u0101n\u0101.<\/p>\n<p>Tom\u0113r hardcore BigData: Hadoop, Spark un citi r\u012bki vairs nav oblig\u0101ta pras\u012bba datu in\u017eenieriem, bet ir r\u012bku veids, lai risin\u0101tu uzdevumus, kurus nav iesp\u0113jams atrisin\u0101t ar tradicion\u0101lo ETL.\u00a0<\/p>\n<blockquote><p>Popul\u0101ras tendences ietver r\u012bku izmanto\u0161anas pakalpojumus bez valodas zin\u0101\u0161an\u0101m, kur\u0101 tie ir rakst\u012bti (piem\u0113ram, Hadoop bez Java zin\u0101\u0161an\u0101m), k\u0101 ar\u012b gatavu pakalpojumu nodro\u0161in\u0101\u0161anu pl\u016bsmas datu apstr\u0101d\u0113 (balss vai att\u0113la atpaz\u012b\u0161ana video).<\/p><\/blockquote>\n<p>\nPopul\u0101ri ir industri\u0101li risin\u0101jumi no SAS un SPSS, k\u0101 ar\u012b Tableau, Rapidminer, Stata un Julia pla\u0161i izmanto datu zin\u0101tnieki viet\u0113jiem uzdevumiem.<\/p>\n<p><img decoding=\"async\" alt=\"Datu in\u017eenieris un Datu zin\u0101tnieks: kas visp\u0101r ir at\u0161\u0137ir\u012bba?\" src=\"\/wp-content\/uploads\/2020\/04\/cf18b6b4fe45d9286f2ec6d14418d28c.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nAnal\u012bti\u0137iem un datu zin\u0101tniekiem iesp\u0113ja pa\u0161iem veidot datu caurules ir par\u0101d\u012bjusies tikai pirms p\u0101ris gadiem: piem\u0113ram, tagad var ar sal\u012bdzino\u0161i vienk\u0101r\u0161iem skriptiem nos\u016bt\u012bt datus uz PostgreSQL b\u0101zes datu noliktavu.\u00a0<\/p>\n<p>Parasti konveijeru un integr\u0113to datu strukt\u016bru izmanto\u0161ana paliek datu in\u017eenieru zi\u0146\u0101. Ta\u010du \u0161odien ir k\u0101 nekad sp\u0113c\u012bg\u0101ks T-veida speci\u0101listu trends \u2014 ar pla\u0161\u0101m zin\u0101\u0161an\u0101m blakus jom\u0101s, jo r\u012bki k\u013c\u016bst arvien vienk\u0101r\u0161\u0101ki.<\/p>\n<h2>K\u0101p\u0113c Datu in\u017eenieriem un Datu zin\u0101tniekiem str\u0101d\u0101t kop\u0101<\/h2>\n<p>\nStr\u0101d\u0101jot cie\u0161\u0101 sadarb\u012bb\u0101 ar in\u017eenieriem, Datu zin\u0101tnieki var koncentr\u0113ties uz p\u0113tniecisko da\u013cu, izstr\u0101d\u0101jot gatavus algoritmus ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s procesiem.<br \/>\nSavuk\u0101rt in\u017eenieriem ir j\u0101fokus\u0113jas uz m\u0113rogojam\u012bbu un datu atk\u0101rtotu izmanto\u0161anu, k\u0101 ar\u012b j\u0101p\u0101rliecin\u0101s, ka datu ievades un izvades caurules katr\u0101 projekt\u0101 atbilst glob\u0101lajai arhitekt\u016brai. <\/p>\n<p>\u0160\u0101da pien\u0101kumu sadale nodro\u0161ina darb\u012bbu saska\u0146ot\u012bbu starp speci\u0101listu grup\u0101m, kas str\u0101d\u0101 pie da\u017e\u0101diem ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s projektiem.\u00a0<\/p>\n<p>Sadarb\u012bba pal\u012bdz efekt\u012bvi izstr\u0101d\u0101t jaunus produktus. \u0100trums un kvalit\u0101te tiek sasniegta, pateicoties l\u012bdzsvaram starp pakalpojuma izveidi visiem (glob\u0101la datu noliktava vai pane\u013cu integr\u0101cija) un konkr\u0113t\u0101 piepras\u012bjuma vai projekta (\u0161aur\u0101s specializ\u0101cijas caurule, \u0101r\u0113jo avotu piesl\u0113g\u0161ana) realiz\u0101ciju.\u00a0<\/p>\n<p>Tuva sadarb\u012bba ar datu zin\u0101tniekiem un anal\u012bti\u0137iem pal\u012bdz in\u017eenieriem att\u012bst\u012bt anal\u012btisk\u0101s un p\u0113tniecisk\u0101s prasmes lab\u0101ku kodu rakst\u012b\u0161anai. Uzlabojas zin\u0101\u0161anu apmai\u0146a starp datu noliktavu un datu ezeru lietot\u0101jiem, padarot projektus elast\u012bg\u0101kus un nodro\u0161inot ilgsto\u0161\u0101kus rezult\u0101tus.<\/p>\n<p>Uz\u0146\u0113mumos, kuri cen\u0161as att\u012bst\u012bt datu darba kult\u016bru un veidot bizness procesus uz to pamata, Datu zin\u0101tnieki un Datu in\u017eenieri papildina viens otru un veido pilnv\u0113rt\u012bgu datu anal\u012bzes sist\u0113mu.\u00a0<\/p>\n<p>N\u0101kamaj\u0101 materi\u0101l\u0101 past\u0101st\u012bsim par to, k\u0101dam j\u0101b\u016bt Datu in\u017eeniera un Datu zin\u0101tnieka izgl\u012bt\u012bbai, k\u0101das prasmes j\u0101att\u012bsta un k\u0101da ir tirgus situ\u0101cija.<\/p>\n<h2>No Netology redakcijas<\/h2>\n<p>\nJa piev\u0113r\u0161at uzman\u012bbu Data Engineer vai Data Scientist profesijai, aicin\u0101m iepaz\u012bties ar m\u016bsu kursu programmu:<\/p>\n<ul>\n<li>Profesion\u0101l\u0101 \"<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\">Datu in\u017eenieris<\/a><\/noindex>\u00bb.\u00a0\u00a0<\/li>\n<li>Profesion\u0101l\u0101 \"<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\">Datu zin\u0101tnieks<\/a><\/noindex>\u00bb.<\/li>\n<\/ul>\n<p>Avots: <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\/lv\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza\" \/>\n\t\t<meta 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