{"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\/ro\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza","title":{"rendered":"Data Engineer \u0219i Data Scientist: care este diferen\u021ba?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Profesiile Data Scientist \u0219i Data Engineer sunt adesea confundate. Fiecare companie are propria specificitate \u00een lucrul cu datele, obiective diferite pentru analiz\u0103 \u0219i o viziune distinct\u0103 asupra rolului fiec\u0103rui specialist, ceea ce implic\u0103 \u0219i cerin\u021be diferite.\u00a0<\/p>\n<p>Hai s\u0103 analiz\u0103m \u00een ce const\u0103 diferen\u021ba dintre ace\u0219ti speciali\u0219ti, ce probleme de afaceri rezolv\u0103, ce abilit\u0103\u021bi au \u0219i c\u00e2t c\u00e2\u0219tig\u0103. Materialul este extins, a\u0219a c\u0103 l-am \u00eemp\u0103r\u021bit \u00een dou\u0103 publica\u021bii.<\/p>\n<p>\u00cen prima articol, Elena Gera\u0219imova, \u0219efa facult\u0103\u021bii \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 \u0219i analiz\u0103<\/a><\/noindex>\u201d de la Netology, vorbe\u0219te despre diferen\u021ba dintre Data Scientist \u0219i Data Engineer \u0219i ce instrumente folosesc.<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>Cum se diferen\u021biaz\u0103 rolurile inginerilor \u0219i ale scientistilor<\/h2>\n<p>\nInginerul de date este un specialist care, pe de o parte, dezvolt\u0103, testeaz\u0103 \u0219i \u00eentre\u021bine infrastructura de lucru cu date: baze de date, stoc\u0103ri \u0219i sisteme de procesare \u00een mas\u0103. Pe de alt\u0103 parte, este cel care cur\u0103\u021b\u0103 \u0219i \u201earanjeaz\u0103\u201d datele pentru utilizarea de c\u0103tre anali\u0219ti \u0219i data scientists, adic\u0103 creeaz\u0103 conducte de procesare a datelor.<\/p>\n<p>Data Scientist creeaz\u0103 \u0219i antreneaz\u0103 modele predictive (dar nu numai) prin utilizarea algoritmilor de \u00eenv\u0103\u021bare automat\u0103 \u0219i re\u021bele neuronale, ajut\u00e2nd afacerile s\u0103 descopere modele ascunse, s\u0103 prognozeze evolu\u021bia evenimentelor \u0219i s\u0103 optimizeze procesele de afaceri cheie.<\/p>\n<p>Principala diferen\u021b\u0103 dintre Data Scientist \u0219i Data Engineer const\u0103 \u00een faptul c\u0103, de obicei, au obiective diferite. Am\u00e2ndoi lucreaz\u0103 pentru a asigura accesibilitatea \u0219i calitatea datelor. Dar Data Scientist g\u0103se\u0219te r\u0103spunsuri la \u00eentreb\u0103rile sale \u0219i verific\u0103 ipoteze \u00een ecosistemul de date (de exemplu, pe baza Hadoop), \u00een timp ce Data Engineer creeaz\u0103 un pipeline care sus\u021bine algoritmul de \u00eenv\u0103\u021bare automat\u0103 scris de data scientist \u00eentr-un cluster Spark \u00een cadrul aceluia\u0219i ecosistem.\u00a0<\/p>\n<p>Inginerul de date aduce valoare afacerii, lucr\u00e2nd \u00een echip\u0103. Sarcina sa este s\u0103 fie un link important \u00eentre diferi\u021bi participan\u021bi: de la dezvoltatori la consumatorii de rapoarte de afaceri, \u0219i s\u0103 \u00eembun\u0103t\u0103\u021beasc\u0103 productivitatea anali\u0219tilor \u2014 de la marketing \u0219i produs la BI.\u00a0<\/p>\n<p>Data Scientist, \u00een schimb, particip\u0103 activ la strategia companiei, la extragerea de insight-uri, la luarea deciziilor, la implementarea algoritmilor de automatizare, modelare \u0219i generarea de valoare din date.<br \/>\n<img decoding=\"async\" alt=\"Data Engineer \u0219i Data Scientist: care este diferen\u021ba?\" src=\"\/wp-content\/uploads\/2020\/04\/1ce3e892eb7a354b106dc2f6398ec5e7.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nLucrul cu datele se supune principiului GIGO (garbage in \u2014 garbage out): dac\u0103 anali\u0219tii \u0219i data scientist-ii se confrunt\u0103 cu date neprelucrate \u0219i poten\u021bial incorecte, rezultatele, chiar \u0219i cu cele mai sofisticate algoritmi de analiz\u0103, vor fi gre\u0219ite.\u00a0<\/p>\n<p>Inginerii de date rezolv\u0103 aceast\u0103 problem\u0103 construind pipeline-uri pentru prelucrarea, cur\u0103\u021barea \u0219i transformarea datelor, permi\u021b\u00e2nd data scientist-ului s\u0103 lucreze deja cu date de calitate.\u00a0<\/p>\n<p>Pe pia\u021b\u0103 exist\u0103 multe instrumente pentru lucrul cu datele, care acoper\u0103 fiecare etap\u0103: de la apari\u021bia datelor p\u00e2n\u0103 la prezentarea pe un dashboard pentru consiliul de administra\u021bie. Este important ca decizia de utilizare a acestora s\u0103 fie luat\u0103 de inginer, \u2014 nu pentru c\u0103 este la mod\u0103, ci pentru c\u0103 cu adev\u0103rat va ajuta ceilal\u021bi participan\u021bi la proces.\u00a0<\/p>\n<p>\u00centr-un mod simplificat: dac\u0103 compania trebuie s\u0103 integreze BI cu ETL \u2014 \u00eenc\u0103rcarea datelor \u0219i actualiz\u0103rile rapoartelor, iat\u0103 o funda\u021bie legacy tipic\u0103 cu care se va confrunta un Inginer de Date (bine, dac\u0103 \u00een echip\u0103 va fi \u0219i un arhitect).<\/p>\n<p><b>Responsabilit\u0103\u021bile Inginerului de Date<\/b><\/p>\n<ul>\n<li>Dezvoltarea, construirea \u0219i \u00eentre\u021binerea infrastructurii pentru gestionarea datelor.<\/li>\n<li>Gestionarea erorilor \u0219i crearea de pipeline-uri de prelucrare a datelor fiabile.<\/li>\n<li>Transformarea datelor nestructurate din diverse surse dinamice \u00eentr-un format necesar pentru anali\u0219ti.<\/li>\n<li>Oferirea de recomand\u0103ri pentru \u00eembun\u0103t\u0103\u021birea consisten\u021bei \u0219i calit\u0103\u021bii datelor.<\/li>\n<li>Asigurarea \u0219i \u00eentre\u021binerea arhitecturii datelor utilizate de data scientist-ii \u0219i anali\u0219tii de date.<\/li>\n<li>Prelucrarea \u0219i stocarea datelor \u00eentr-un mod secven\u021bial \u0219i eficient \u00eentr-cluster distribuit format din zeci sau sute de servere.<\/li>\n<li>Evaluarea compromisurilor tehnice ale instrumentelor pentru crearea unor arhitecturi simple, dar fiabile, capabile s\u0103 reziste la defec\u021biuni.<\/li>\n<li>Monitorizarea \u0219i \u00eentre\u021binerea fluxurilor de date \u0219i a sistemelor asociate (configurarea monitoriz\u0103rii \u0219i alertelor).<\/li>\n<\/ul>\n<p>\nExist\u0103 o alt\u0103 specializare \u00een cadrul traseului Inginerului de Date \u2014 inginerul ML. \u00cen termeni simpli, ace\u0219ti ingineri se specializeaz\u0103 \u00een implementarea modelelor de \u00eenv\u0103\u021bare automat\u0103 pentru utilizarea \u00een mediu comercial. Adesea, modelul provenit de la data scientist este parte a unei cercet\u0103ri \u0219i poate s\u0103 nu func\u021bioneze \u00een condi\u021bii reale.<\/p>\n<p><b>Responsabilit\u0103\u021bile Data Scientist-ului<\/b><\/p>\n<ul>\n<li>Extrac\u021bia caracteristicilor din date pentru aplicarea algoritmilor de \u00eenv\u0103\u021bare automat\u0103.<\/li>\n<li>Utilizarea diverselor instrumente de \u00eenv\u0103\u021bare automat\u0103 pentru a prezice \u0219i clasifica modele \u00een date.<\/li>\n<li>\u00cembun\u0103t\u0103\u021birea performan\u021bei \u0219i preciziei algoritmilor de \u00eenv\u0103\u021bare automat\u0103 prin ajustarea fin\u0103 \u0219i optimizarea acestora.<\/li>\n<li>Formarea de ipoteze \u201eputernice\u201d conforme cu strategia companiei, care trebuie verificate.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>At\u00e2t Data Engineer, c\u00e2t \u0219i Data Scientist contribuie semnificativ la dezvoltarea culturii de lucru cu datele, ceea ce permite companiei s\u0103 ob\u021bin\u0103 profituri suplimentare sau s\u0103 reduc\u0103 costurile.<\/p><\/blockquote>\n<p><\/p>\n<h2>Cu ce limbaje \u0219i instrumente lucreaz\u0103 inginerii \u0219i oamenii de \u0219tiin\u021b\u0103?<\/h2>\n<p>\nAst\u0103zi a\u0219tept\u0103rile de la speciali\u0219tii \u00een prelucrarea datelor s-au schimbat. \u00cen trecut, inginerii scriau interog\u0103ri SQL mari, scriau manual MapReduce \u0219i procesau datele cu ajutorul unor instrumente precum Informatica ETL, Pentaho ETL, Talend.\u00a0<\/p>\n<p>\u00cen 2020, un specialist nu poate s\u0103 se descurce f\u0103r\u0103 cuno\u0219tin\u021be de Python \u0219i instrumente moderne de calcul (de exemplu, Airflow), precum \u0219i f\u0103r\u0103 \u00een\u021belegerea principiilor de func\u021bionare a platformelor cloud (utilizarea acestora pentru economisirea costurilor la \u201ehardware\u201d, respect\u00e2nd principiile de securitate).<\/p>\n<p>SAP, Oracle, MySQL, Redis sunt instrumente tradi\u021bionale pentru inginerii de date \u00een companii mari. Acestea sunt bune, dar costul licen\u021belor este at\u00e2t de ridicat \u00eenc\u00e2t este logic s\u0103 \u00eenve\u021bi s\u0103 lucrezi cu ele doar \u00een proiecte industriale. Totu\u0219i, exist\u0103 o alternativ\u0103 gratuit\u0103 sub form\u0103 de Postgres \u2013 este gratuit \u0219i potrivit nu doar pentru \u00eenv\u0103\u021bare.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer \u0219i Data Scientist: care este diferen\u021ba?\" src=\"\/wp-content\/uploads\/2020\/04\/8a002b86ee3a18d5b232c5232a62dd3e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nIstoric, cererea pentru Java \u0219i Scala a fost frecvent\u0103, de\u0219i pe m\u0103sur\u0103 ce tehnologiile \u0219i abord\u0103rile evolueaz\u0103, aceste limbaje sunt l\u0103sate pe plan secund.<\/p>\n<p>Cu toate acestea, Big Data hardcore: Hadoop, Spark \u0219i restul parcului de instrumente nu mai sunt o cerin\u021b\u0103 obligatorie pentru inginerii de date, ci o varietate de instrumente pentru rezolvarea problemelor ce nu pot fi solu\u021bionate prin ETL tradi\u021bional.\u00a0<\/p>\n<blockquote><p>\u00cen trend sunt serviciile care permit utilizarea instrumentelor f\u0103r\u0103 a cunoa\u0219te limbajul \u00een care sunt scrise (de exemplu, Hadoop f\u0103r\u0103 cuno\u0219tin\u021be de Java), precum \u0219i furnizarea de servicii gata f\u0103cute pentru procesarea datelor \u00een flux (recunoa\u0219terea vocii sau imaginilor \u00een videoclipuri).<\/p><\/blockquote>\n<p>\nSolu\u021biile industriale de la SAS \u0219i SPSS sunt populare, iar Tableau, Rapidminer, Stata \u0219i Julia sunt, de asemenea, folosite pe scar\u0103 larg\u0103 de data scientist pentru sarcini locale.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer \u0219i Data Scientist: care este diferen\u021ba?\" src=\"\/wp-content\/uploads\/2020\/04\/cf18b6b4fe45d9286f2ec6d14418d28c.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nPosibilitatea de a construi singuri pipeline-uri a ap\u0103rut pentru anali\u0219ti \u0219i data scientist doar \u00een urm\u0103 cu c\u00e2\u021biva ani: de exemplu, acum este posibil s\u0103 direc\u021bionezi datele c\u0103tre un stocare bazat\u0103 pe PostgreSQL cu scripturi relativ simple.\u00a0<\/p>\n<p>De obicei, utilizarea canalelor \u0219i a structurilor de date integrate r\u0103m\u00e2ne \u00een responsabilitatea inginerilor de date. Dar ast\u0103zi, mai mult ca niciodat\u0103, tendin\u021ba speciali\u0219tilor \u00een T - cu competen\u021be largi \u00een domenii adiacente - este puternic\u0103, deoarece instrumentele devin constant mai simple.<\/p>\n<h2>De ce trebuie s\u0103 colaboreze Data Engineer \u0219i Data Scientist<\/h2>\n<p>\nColabor\u00e2nd str\u00e2ns cu inginerii, Data Scientist pot s\u0103 se concentreze pe partea de cercetare, cre\u00e2nd algoritmi de \u00eenv\u0103\u021bare automat\u0103 gata de utilizare.<br \/>\n\u00cen acela\u0219i timp, inginerii se pot concentra pe scalabilitate, reutilizarea datelor \u0219i asigurarea c\u0103 pipeline-urile de intrare \u0219i ie\u0219ire a datelor \u00een fiecare proiect respectiv se conformeaz\u0103 arhitecturii globale. <\/p>\n<p>Aceast\u0103 \u00eemp\u0103r\u021bire a responsabilit\u0103\u021bilor asigur\u0103 coeren\u021ba ac\u021biunilor \u00eentre grupurile de speciali\u0219ti care lucreaz\u0103 la diferite proiecte de \u00eenv\u0103\u021bare automat\u0103.\u00a0<\/p>\n<p>Cooperarea ajut\u0103 la crearea eficient\u0103 a unor produse noi. Viteza \u0219i calitatea sunt realizate printr-un echilibru \u00eentre crearea unui serviciu pentru to\u021bi (stocare global\u0103 sau integrarea tablourilor de bord) \u0219i implementarea fiec\u0103rei nevoi sau proiecte specifice (pipeline specializat, conectarea la surse externe).\u00a0<\/p>\n<p>Lucrul \u00eendeaproape cu data scientist \u0219i anali\u0219ti ajut\u0103 inginerii s\u0103 \u00ee\u0219i dezvolte abilit\u0103\u021bile analitice \u0219i de cercetare pentru a scrie cod de o calitate mai bun\u0103. Se \u00eembun\u0103t\u0103\u021be\u0219te schimbul de cuno\u0219tin\u021be \u00eentre utilizatorii de stocare \u0219i lacuri de date, ceea ce face proiectele mai flexibile \u0219i asigur\u0103 rezultate mai durabile pe termen lung.<\/p>\n<p>\u00cen companiile care \u00ee\u0219i propun s\u0103 dezvolte o cultur\u0103 a muncii cu datele \u0219i s\u0103 construiasc\u0103 procesele de afaceri pe baza acestora, Data Scientist \u0219i Data Engineer se completeaz\u0103 reciproc \u0219i creeaz\u0103 un sistem complet de analiz\u0103 a datelor.\u00a0<\/p>\n<p>\u00cen materialul urm\u0103tor, vom discuta despre ce educa\u021bie ar trebui s\u0103 aib\u0103 Data Engineer \u0219i Data Scientist, ce abilit\u0103\u021bi trebuie s\u0103 dezvolte \u0219i cum este structurat pia\u021ba.<\/p>\n<h2>De la redac\u021bia Netology<\/h2>\n<p>\nDac\u0103 sunte\u021bi interesat de profesia de Data Engineer sau Data Scientist, v\u0103 invit\u0103m s\u0103 explora\u021bi programele cursurilor noastre:<\/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\">Data Engineer<\/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\">Data Scientist<\/a><\/noindex>\u00bb.<\/li>\n<\/ul>\n<p>Sursa: <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 5.0.2.1 - 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\/ro\/blog\/administrirovanie\/data-engineer-i-data-scientist-kakaya-voobshhe-raznicza\" \/>\n\t<meta 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