{"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\/hr\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","title":{"rendered":"Data Engineer i Data Scientist: koja je razlika?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Zanimanja Data Scientist i Data Engineer \u010desto se mije\u0161aju. Svaka tvrtka ima svoju specifi\u010dnost u radu s podacima, razli\u010dite ciljeve analize i razli\u010dite predstave o tome tko od stru\u010dnjaka treba raditi na kojem dijelu posla, stoga svaki postavlja svoje zahtjeve.\u00a0<\/p>\n<p>Razjasnimo razliku izme\u0111u ovih stru\u010dnjaka, koje poslovne zadatke rje\u0161avaju, kakve vje\u0161tine posjeduju i koliko zara\u0111uju. Materijal je opse\u017ean, stoga smo ga podijelili na dvije objave.<\/p>\n<p>U prvom \u010dlanku Elena Gerasimova, voditeljica fakulteta \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 i analitika<\/a><\/noindex>\u201c na Netologiji, obja\u0161njava razliku izme\u0111u Data Scientista i Data Engineera te s kojim alatima rade.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Kako se razlikuju uloge in\u017eenjera i znanstvenika<\/h2>\n<p>\nIn\u017eenjer podataka je stru\u010dnjak koji, s jedne strane, razvija, testira i odr\u017eava infrastrukturu za rad s podacima: baze podataka, skladi\u0161ta i sustave za masovnu obradu. S druge strane, on \u010di\u0161\u0107i i 'ure\u0111uje' podatke za kori\u0161tenje analiti\u010darima i data scientistima, tj. stvara cjevovode za obradu podataka.<\/p>\n<p>Data Scientist stvara i obu\u010dava prediktivne (i ne samo) modele pomo\u0107u algoritama strojnog u\u010denja i neuro mre\u017ea, poma\u017eu\u0107i poslovanju da prona\u0111e skrivene obrasce, prognozira razvoj doga\u0111aja i optimizira klju\u010dne poslovne procese.<\/p>\n<p>Glavna razlika izme\u0111u Data Scientista i Data Engineera je ta da obi\u010dno imaju razli\u010dite ciljeve. Oba rade na tome da podaci budu dostupni i kvalitetni. No, Data Scientist pronalazi odgovore na svoja pitanja i testira hipoteze unutar ekosustava podataka (na primjer, na temelju Hadoopa), dok Data Engineer stvara cjevovod za odr\u017eavanje algoritma strojnog u\u010denja koji je napisao data scientist u klasteru Sparka unutar istog ekosustava.\u00a0<\/p>\n<p>In\u017eenjer podataka donosi vrijednost poslovanju rade\u0107i u timu. Njegov zadatak je da bude va\u017ena karika izme\u0111u razli\u010ditih sudionika: od programera do poslovnih korisnika izvje\u0161taja, te pove\u0107a produktivnost analiti\u010dara \u2014 od marketinga i proizvoda do BI.\u00a0<\/p>\n<p>Data Scientist, s druge strane, aktivno sudjeluje u strategiji tvrtke i izvla\u010denju uvida, dono\u0161enju odluka, implementaciji algoritama automatizacije, modeliranja i generiranju vrijednosti iz podataka.<br \/>\n<img decoding=\"async\" alt=\"Data Engineer i Data Scientist: koja je razlika?\" src=\"\/wp-content\/uploads\/2020\/04\/1ce3e892eb7a354b106dc2f6398ec5e7.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nRad s podacima podla\u017ee principu GIGO (garbage in \u2014 garbage out): ako analiti\u010dari i data znanstvenici rade s nepripremljenim i potencijalno neto\u010dnim podacima, rezultati \u010dak i uz najslo\u017eenije analiti\u010dke algoritme bit \u0107e neto\u010dni.\u00a0<\/p>\n<p>In\u017eenjeri podataka rje\u0161avaju ovaj problem grade\u0107i cjevovode za obradu, \u010di\u0161\u0107enje i transformaciju podataka, omogu\u0107avaju\u0107i data znanstvenicima da rade s kvalitetnim podacima.\u00a0<\/p>\n<p>Na tr\u017ei\u0161tu postoji mnogo alata za rad s podacima koji pokrivaju svaki od koraka: od pojave podataka do prikaza na nadzornoj plo\u010di za upravu. Va\u017eno je da odluku o njihovom kori\u0161tenju donese in\u017eenjer, ne zato \u0161to je to moderno, ve\u0107 zato \u0161to \u0107e to stvarno pomo\u0107i ostalim sudionicima u procesu.\u00a0<\/p>\n<p>Uslovno: ako tvrtki treba uskladiti BI i ETL \u2014 u\u010ditavanje podataka i a\u017euriranje izvje\u0161taja, ovo je tipi\u010dna legacy osnova s kojom \u0107e se Data Engineer susresti (dobro je ako u timu osim njega bude i arhitekt).<\/p>\n<p><b>Obaveze Data Engineera<\/b><\/p>\n<ul>\n<li>Razvijanje, izgradnja i odr\u017eavanje infrastrukture za obradu podataka.<\/li>\n<li>Obrada gre\u0161aka i izrada pouzdanih cjevovoda za obradu podataka.<\/li>\n<li>Ure\u0111ivanje nestrukturiranih podataka iz razli\u010ditih dinami\u010dnih izvora u oblik potreban analiti\u010darima.<\/li>\n<li>Davanje preporuka za pobolj\u0161anje konzistentnosti i kvalitete podataka.<\/li>\n<li>Osiguravanje i podr\u017eavanje arhitekture podataka koju koriste data znanstvenici i analiti\u010dari podataka.<\/li>\n<li>Obrada i pohrana podataka dosljedno i u\u010dinkovito u distribuiranom klasteru na desetinama ili stotinama poslu\u017eitelja.<\/li>\n<li>Procjena tehni\u010dkih kompromisa alata za izgradnju jednostavnih, ali pouzdanih arhitektura koje mogu izdr\u017eati kvarove.<\/li>\n<li>Nadzor i podr\u0161ka podacima i povezanim sustavima (postavljanje nadzora i upozorenja).<\/li>\n<\/ul>\n<p>\nPostoji jo\u0161 jedna specijalizacija unutar putanje Data Engineera \u2014 ML in\u017eenjer. Ukratko, takvi in\u017eenjeri se specijaliziraju za dovo\u0111enje modela strojog u\u010denja do industrijske primjene i kori\u0161tenja. \u010cesto model koji dolazi od data znanstvenika je dio istra\u017eivanja i mo\u017eda ne\u0107e raditi u stvarnim okolnostima.<\/p>\n<p><b>Obaveze Data Scientista<\/b><\/p>\n<ul>\n<li>Izdvajanje zna\u010dajki iz podataka za primjenu algoritama strojnog u\u010denja.<\/li>\n<li>Kori\u0161tenje razli\u010ditih alata za strojno u\u010denje za predvi\u0111anje i klasifikaciju obrazaca u podacima.<\/li>\n<li>Pove\u0107anje performansi i to\u010dnosti algoritama strojnog u\u010denja putem fine prilagodbe i optimizacije algoritama.<\/li>\n<li>Formiranje 'jakih' hipoteza u skladu sa strategijom tvrtke koje je potrebno provjeriti.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>I Data Engineer i Data Scientist povezuju zna\u010dajan doprinos razvoju kulture rada s podacima, putem koje kompanija mo\u017ee ostvariti dodatnu dobit ili smanjiti tro\u0161kove.<\/p><\/blockquote>\n<p><\/p>\n<h2>S kojim jezicima i alatima rade in\u017eenjeri i znanstvenici podataka<\/h2>\n<p>\nDanas su o\u010dekivanja od stru\u010dnjaka za obradu podataka promijenjena. Ranije su in\u017eenjeri pisali velike SQL upite, ru\u010dno pisali MapReduce i obra\u0111ivali podatke pomo\u0107u alata kao \u0161to su Informatica ETL, Pentaho ETL, Talend.\u00a0<\/p>\n<p>U 2020. godini, stru\u010dnjaku nije mogu\u0107e zaobi\u0107i poznavanje Pythona i modernih alata za izvo\u0111enje prora\u010duna (npr. Airflow), kao i razumijevanje principa rada s oblacima (kori\u0161tenje za u\u0161tedu na 'hardveru', uz po\u0161tivanje principa sigurnosti).<\/p>\n<p>SAP, Oracle, MySQL, Redis \u2014 to su tradicionalni alati za in\u017eenjere podataka u velikim kompanijama. Dobar su odabir, ali tro\u0161ak licenci je toliko visok da ima smisla u\u010diti raditi s njima samo u industrijskim projektima. Uz to, postoji besplatna alternativa u obliku Postgresa \u2014 besplatan je i prikladan ne samo za u\u010denje.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer i Data Scientist: koja je razlika?\" src=\"\/wp-content\/uploads\/2020\/04\/8a002b86ee3a18d5b232c5232a62dd3e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nPovijesno se \u010desto tra\u017ee Java i Scala, iako s razvojem tehnologija i pristupa ti jezici gube na va\u017enosti.<\/p>\n<p>Ipak, hardcore BigData: Hadoop, Spark i ostali alati \u2014 vi\u0161e nisu obavezni uvjet za in\u017eenjera podataka, ve\u0107 vrsta alata za rje\u0161avanje problema koje nije mogu\u0107e rije\u0161iti tradicionalnim ETL-om.\u00a0<\/p>\n<blockquote><p>U trendu su servisi za kori\u0161tenje alata bez poznavanja jezika na kojem su napisani (npr. Hadoop bez poznavanja Jave), kao i pru\u017eanje gotovih servisa za obradu streaming podataka (prepoznavanje glasa ili slika na videu).<\/p><\/blockquote>\n<p>\nPopularna su industrijska rje\u0161enja od SAS-a i SPSS-a, dok se Tableau, Rapidminer, Stata i Julia tako\u0111er \u0161iroko koriste od strane znanstvenika podataka za lokalne zadatke.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer i Data Scientist: koja je razlika?\" src=\"\/wp-content\/uploads\/2020\/04\/cf18b6b4fe45d9286f2ec6d14418d28c.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nMogu\u0107nost da sami kreiraju pipeline-ove imala su analiti\u010dari i Data Scientist-i tek prije nekoliko godina: na primjer, ve\u0107 je mogu\u0107e relativno jednostavnim skriptama usmjeriti podatke u skladi\u0161te temeljenom na PostgreSQL-u.\u00a0<\/p>\n<p>Obi\u010dno kori\u0161tenje cjevovoda i integriranih struktura podataka ostaje u nadle\u017enosti Data in\u017eenjera. No danas je kao nikada prije naglasak na T-oblikovanim stru\u010dnjacima \u2014 sa \u0161irokim kompetencijama u susjednim podru\u010djima, jer se alati kontinuirano pojednostavljuju.<\/p>\n<h2>Za\u0161to Data Engineer i Data Scientist trebaju raditi zajedno<\/h2>\n<p>\nRadom u uskoj suradnji s in\u017eenjerima, Data Scientist-i se mogu usredoto\u010diti na istra\u017eiva\u010dki dio, stvaraju\u0107i radne algoritme strojnog u\u010denja.<br \/>\nA in\u017eenjeri se mogu fokusirati na skalabilnost, ponovnu iskoristivost podataka i osigurati da cjevovodi za unos i izlaz podataka u svakom pojedinom projektu odgovaraju globalnoj arhitekturi. <\/p>\n<p>Takva podjela odgovornosti osigurava dosljednost djelovanja izme\u0111u grupa stru\u010dnjaka koji rade na razli\u010ditim projektima strojnog u\u010denja.\u00a0<\/p>\n<p>Suradnja poma\u017ee u\u010dinkovito razvijati nove proizvode. Brzina i kvaliteta posti\u017eu se zahvaljuju\u0107i ravnote\u017ei izme\u0111u stvaranja usluge za sve (globalno skladi\u0161te ili integracija nadzornih plo\u010da) i implementacije svake konkretne potrebe ili projekta (usko specijalizirani cjevovod, povezivanje vanjskih izvora).\u00a0<\/p>\n<p>Uska suradnja s Data Scientist-ima i analiti\u010darima poma\u017ee in\u017eenjerima u razvoju analiti\u010dkih i istra\u017eiva\u010dkih vje\u0161tina za pisanje kvalitetnijeg koda. Pobolj\u0161ava se razmjena znanja izme\u0111u korisnika skladi\u0161ta i jezera podataka, \u0161to \u010dini projekte fleksibilnijima i osigurava odr\u017eivije dugoro\u010dne rezultate.<\/p>\n<p>U tvrtkama koje imaju za cilj razvijanje kulture rada s podacima i uspostavljanje poslovnih procesa temeljenih na njima, Data Scientist-i i Data Engineer-i se me\u0111usobno dopunjuju i stvaraju cjelovit sustav analize podataka.\u00a0<\/p>\n<p>U sljede\u0107em materijalu raspravljamo o tome koje obrazovanje trebaju imati Data Engineer-i i Data Scientist-i, koje vje\u0161tine trebaju razvijati i kako je organizirano tr\u017ei\u0161te.<\/p>\n<h2>Od uredni\u0161tva Netologije<\/h2>\n<p>\nAko razmi\u0161ljate o zanimanju Data Engineer ili Data Scientist, pozivamo vas da istra\u017eite programe na\u0161ih te\u010dajeva:<\/p>\n<ul>\n<li>Zanimanje \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\u017eenjer<\/a><\/noindex>\u00bb.\u00a0\u00a0<\/li>\n<li>Zanimanje \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>Izvor: <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 5.0.4 - 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\/hr\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza\" \/>\n\t\t<meta 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