{"id":55327,"date":"2020-01-18T00:00:00","date_gmt":"2020-01-17T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/samye-vostrebovannye-navyki-v-professii-data-engineer"},"modified":"2020-02-18T14:03:26","modified_gmt":"2020-02-18T11:03:26","slug":"samye-vostrebovannye-navyki-v-professii-data-engineer","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/administrirovanie\/samye-vostrebovannye-navyki-v-professii-data-engineer","title":{"rendered":"Nej\u017e\u00e1dan\u011bj\u0161\u00ed dovednosti v profesi datov\u00e9ho in\u017een\u00fdra","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Podle statistice roku 2019<\/a><\/noindex>, data engineer je v sou\u010dasnosti profes\u00ed, na kterou roste popt\u00e1vka rychleji ne\u017e na ostatn\u00ed. Data engineer hraje v organizaci kriticky d\u016fle\u017eitou roli - vytv\u00e1\u0159\u00ed a udr\u017euje funk\u010dn\u00ed datov\u00e9 toky a datab\u00e1ze, kter\u00e9 se pou\u017e\u00edvaj\u00ed pro zpracov\u00e1n\u00ed, transformaci a ukl\u00e1d\u00e1n\u00ed dat. Jak\u00e9 dovednosti jsou pro z\u00e1stupce t\u00e9to profese kl\u00ed\u010dov\u00e9? Rozd\u00edl\u00ed se seznam od toho, co je po\u017eadov\u00e1no od data scientists? O tom v\u0161em se dozv\u00edte v m\u00e9m \u010dl\u00e1nku.<\/p>\n

Analyzoval jsem pracovn\u00ed nab\u00eddky na pozici data engineer v lednu 2020, abych pochopil, kter\u00e9 technologick\u00e9 dovednosti jsou nejv\u00edce \u017e\u00e1dan\u00e9. Pot\u00e9 jsem z\u00edskan\u00e9 v\u00fdsledky porovnal se statistiky pracovn\u00edk\u016f na pozici data scientist \u2013 objevily se n\u011bkter\u00e9 zaj\u00edmav\u00e9 rozd\u00edly.<\/p>\n

Obejdeme se bez dlouh\u00fdch \u00favod\u016f \u2013 zde je deset nej\u010dast\u011bji zmi\u0148ovan\u00fdch technologi\u00ed v textu pracovn\u00edch nab\u00eddek:<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Zm\u00ednky o technologi\u00edch v pracovn\u00edch nab\u00eddk\u00e1ch na pozici data engineer v roce 2020<\/i><\/p>\n

Poj\u010fme se na to pod\u00edvat.
\n<\/a><\/noindex><\/p>\n

Povinnosti data engineer<\/h4>\n

\nDnes m\u00e1 pr\u00e1ce, kterou vykon\u00e1vaj\u00ed data engineers, obrovsk\u00fd v\u00fdznam pro organizace \u2013 pr\u00e1v\u011b tito lid\u00e9 jsou odpov\u011bdn\u00ed za uchov\u00e1v\u00e1n\u00ed informac\u00ed a jejich \u00fapravu tak, aby s nimi ostatn\u00ed zam\u011bstnanci mohli pracovat. Data engineers buduj\u00ed datov\u00e9 toky, aby usnadnili \u010derp\u00e1n\u00ed dat bu\u010f v re\u00e1ln\u00e9m \u010dase nebo d\u00e1vkov\u011b, z n\u011bkolika zdroj\u016f. N\u00e1sledn\u011b datov\u00e9 toky prov\u00e1d\u011bj\u00ed operace extrakce, transformace a na\u010d\u00edt\u00e1n\u00ed (jin\u00fdmi slovy, ETL procesy), \u010d\u00edm\u017e \u010din\u00ed data vhodn\u011bj\u0161\u00ed pro dal\u0161\u00ed vyu\u017eit\u00ed. Nakonec data p\u0159ed\u00e1vaj\u00ed analytik\u016fm a data scientists pro hlub\u0161\u00ed zpracov\u00e1n\u00ed. Nakonec data kon\u010d\u00ed svou cestu na informa\u010dn\u00edch panelech, v reportech a modelech strojov\u00e9ho u\u010den\u00ed.<\/p>\n

Hledal jsem informace, kter\u00e9 by umo\u017enily u\u010dinit z\u00e1v\u011br o tom, kter\u00e9 technologie jsou v pr\u00e1ci data engineer v sou\u010dasnosti nejv\u00edce \u017e\u00e1dan\u00e9.<\/p>\n

Metody<\/h4>\n

\nShroma\u017e\u010foval jsem informace ze t\u0159\u00ed pracovn\u00edch port\u00e1l\u016f - SimplyHired<\/a><\/noindex>, Indeed<\/a><\/noindex> a Monster<\/a><\/noindex> a zkoumal, kter\u00e1 kl\u00ed\u010dov\u00e1 slova se objevuj\u00ed v kontextu \u201edata engineer\u201c v textu pracovn\u00edch nab\u00eddek ur\u010den\u00fdch pro obyvatele USA. Pro tento \u00fakol jsem pou\u017e\u00edval dv\u011b Python knihovny - Requests<\/a><\/noindex> a Beautiful Soup<\/a><\/noindex>Do kl\u00ed\u010dov\u00fdch slov jsem zahrnul jak ta, kter\u00e1 byla v p\u0159edchoz\u00edm seznamu pro anal\u00fdzu pracovn\u00edch nab\u00eddek na pozici datov\u00e9ho v\u011bdce, tak ta, kter\u00e1 jsem vybral ru\u010dn\u011b p\u0159i \u010dten\u00ed inzer\u00e1t\u016f na pr\u00e1ci pro datov\u00e9 in\u017een\u00fdry. LinkedIn nebyl zahrnut mezi zdroje, proto\u017ee jsem tam byl po minul\u00e9m pokusu o sb\u011br dat zablokov\u00e1n.<\/p>\n

Pro ka\u017ed\u00e9 kl\u00ed\u010dov\u00e9 slovo jsem spo\u010d\u00edtal procento v\u00fdskyt\u016f z celkov\u00e9ho po\u010dtu text\u016f na ka\u017ed\u00e9m z web\u016f zvl\u00e1\u0161\u0165 a pot\u00e9 jsem vypo\u010d\u00edtal pr\u016fm\u011brnou hodnotu ze t\u0159\u00ed zdroj\u016f.<\/p>\n

V\u00fdsledky<\/h4>\n

\nN\u00ed\u017ee je uvedeno t\u0159icet technick\u00fdch term\u00edn\u016f z oblasti datov\u00e9ho in\u017een\u00fdrstv\u00ed s nejvy\u0161\u0161\u00edmi ukazateli na v\u0161ech t\u0159ech pracovn\u00edch webech.<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

A zde jsou stejn\u00e9 \u00fadaje, ale se\u0159azen\u00e9 v tabulce:<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Poj\u010fme na to postupn\u011b.<\/p>\n

P\u0159ehled v\u00fdsledk\u016f<\/b><\/p>\n

Jak SQL, tak Python se objevuj\u00ed ve v\u00edce ne\u017e dvou t\u0159etin\u00e1ch zkouman\u00fdch pracovn\u00edch nab\u00eddek. Pr\u00e1v\u011b tyto dv\u011b technologie m\u00e1 smysl studovat jako prvn\u00ed. CouchDB<\/a><\/noindex> \u2013 velmi popul\u00e1rn\u00ed programovac\u00ed jazyk pou\u017e\u00edvan\u00fd pro pr\u00e1ci s daty, tvorbu webov\u00fdch str\u00e1nek a psan\u00ed skript\u016f. SQL<\/a><\/noindex> se roz\u0161i\u0159uje jako Structured Query Language (jazyk strukturovan\u00fdch dotaz\u016f); p\u0159edpokl\u00e1d\u00e1 standard realizovan\u00fd skupinou jazyk\u016f a pou\u017e\u00edv\u00e1 se pro extrakci dat z rela\u010dn\u00edch datab\u00e1z\u00ed. Objevil se ji\u017e d\u00e1vno a prok\u00e1zal vysokou odolnost.<\/p>\n

O Spark se zmi\u0148uje p\u0159ibli\u017en\u011b polovina pracovn\u00edch nab\u00eddek. Apache Spark<\/a><\/noindex> \u2013 je to \"integrovan\u00fd analytick\u00fd motor pro zpracov\u00e1n\u00ed velk\u00fdch dat se zabudovan\u00fdmi moduly pro streamov\u00e1n\u00ed, SQL, strojov\u00e9 u\u010den\u00ed a zpracov\u00e1n\u00ed graf\u016f\". Je obzvl\u00e1\u0161\u0165 popul\u00e1rn\u00ed u t\u011bch, kdo pracuj\u00ed s velk\u00fdmi datab\u00e1zemi.<\/p>\n

AWS se objevuje p\u0159ibli\u017en\u011b v 45 % text\u016f pracovn\u00edch nab\u00eddek. Je to cloudov\u00e1 v\u00fdpo\u010detn\u00ed platforma vyr\u00e1b\u011bn\u00e1 firmou Amazon; m\u00e1 nejv\u011bt\u0161\u00ed pod\u00edl na trhu mezi v\u0161emi cloudov\u00fdmi platformami.
\nN\u00e1sleduj\u00ed Java a Hadoop \u2013 t\u011bsn\u011b nad 40 % na kus. Javy<\/a><\/noindex> \u2013 \u0161iroce pou\u017e\u00edvan\u00fd, v boji prov\u011b\u0159en\u00fd jazyk, kter\u00fd v pr\u016fzkumu v\u00fdvoj\u00e1\u0159\u016f na Stack Overflow 2019 obsadil des\u00e1t\u00e9 m\u00edsto mezi jazyky bud\u00edc\u00edmi u program\u00e1tor\u016f hr\u016fzu. Naopak Python se um\u00edstil na druh\u00e9m m\u00edst\u011b mezi jazyky, kter\u00e9 maj\u00ed nejvy\u0161\u0161\u00ed oblibu. Jazyk Java ovl\u00e1d\u00e1 Oracle a v\u0161e, co o n\u011bm pot\u0159eba v\u011bd\u011bt, lze pochopit z tohoto sn\u00edmku ofici\u00e1ln\u00ed str\u00e1nky z ledna 2020.<\/a><\/noindex> Jako bych proj\u00ed\u017ed\u011bl strojem \u010dasu<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed
\n
\nApache Hadoop<\/i>
\nApache Hadoop<\/a><\/noindex> pou\u017e\u00edv\u00e1 programov\u00fd model MapReduce s klastrov\u00fdmi servery pro velk\u00e1 data. V posledn\u00ed dob\u011b se od tohoto modelu za\u010d\u00edn\u00e1 \u010d\u00edm d\u00e1l \u010dast\u011bji ustupovat.<\/p>\n

D\u00e1le vid\u00edme Hive, Scala, Kafka a NoSQL \u2013 ka\u017ed\u00e1 z t\u011bchto technologi\u00ed se objevuje ve \u010dtvrtin\u011b zadan\u00fdch nab\u00eddek pr\u00e1ce. Apache Hive je datov\u00fd sklad, kter\u00fd \"usnad\u0148uje \u010dten\u00ed, psan\u00ed a spr\u00e1vu velk\u00fdch datov\u00fdch sad um\u00edst\u011bn\u00fdch v distribuovan\u00fdch \u00falo\u017ei\u0161t\u00edch pomoc\u00ed SQL.\" Scala<\/a><\/noindex> \u2013 programovac\u00ed jazyk, kter\u00fd se aktivn\u011b vyu\u017e\u00edv\u00e1 p\u0159i pr\u00e1ci s velk\u00fdmi daty. P\u0159edev\u0161\u00edm na Scala byl vytvo\u0159en Spark. V ji\u017e zm\u00edn\u011bn\u00e9m \u017eeb\u0159\u00ed\u010dku jazyk\u016f, kter\u00e9 vzbuzuj\u00ed strach, se Scala um\u00edstila na jeden\u00e1ct\u00e9m m\u00edst\u011b. Apache Kafka<\/a><\/noindex> \u2013 distribuovan\u00e1 platforma pro zpracov\u00e1n\u00ed streamovan\u00fdch zpr\u00e1v. Je velmi popul\u00e1rn\u00ed jako prost\u0159edek pro p\u0159enos dat.<\/p>\n

NoSQL datab\u00e1ze<\/a><\/noindex> se vytv\u00e1\u0159ej\u00ed jako alternativa k SQL. Li\u0161\u00ed se t\u00edm, \u017ee nejsou rela\u010dn\u00ed, nejsou strukturovan\u00e9 a maj\u00ed horizont\u00e1ln\u00ed \u0161k\u00e1lovatelnost. NoSQL si z\u00edskal ur\u010ditou popularitu, av\u0161ak intenzivn\u00ed nad\u0161en\u00ed pro tento p\u0159\u00edstup, kter\u00e9 vedlo k prorock\u00fdm tvrzen\u00edm, \u017ee nahrad\u00ed SQL jako dominantn\u00ed paradigmu \u00falo\u017ei\u0161t\u011b, se zd\u00e1, \u017ee u\u017e pomalu pominulo.<\/p>\n

Srovn\u00e1n\u00ed s term\u00edny v nab\u00eddk\u00e1ch pr\u00e1ce data scientist<\/h4>\n

\nZde je t\u0159icet technologick\u00fdch term\u00edn\u016f, kter\u00e9 jsou nejroz\u0161\u00ed\u0159en\u011bj\u0161\u00ed mezi zam\u011bstnavateli v oblasti data science. Tento seznam jsem z\u00edskal stejn\u00fdm zp\u016fsobem, jak\u00fd jsem popisoval v\u00fd\u0161e pro data engineering.<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Zmi\u0148ov\u00e1n\u00ed technologi\u00ed v nab\u00eddk\u00e1ch pr\u00e1ce na pozici data scientist v roce 2020<\/i><\/p>\n

Pokud hovo\u0159\u00edme o celkov\u00e9m po\u010dtu, ve srovn\u00e1n\u00ed s d\u0159\u00edve zkouman\u00fdm souborem bylo nab\u00eddek v\u00edce o 28% (12 013 oproti 9396). Pod\u00edvejme se, kter\u00e9 technologie se v nab\u00eddk\u00e1ch pro data scientists objevuj\u00ed m\u00e9n\u011b ne\u017e pro data engineers.<\/p>\n

V\u00edce popul\u00e1rn\u00ed v data engineering<\/b><\/p>\n

Na grafu n\u00ed\u017ee jsou zobrazeny kl\u00ed\u010dov\u00e1 slova se st\u0159edn\u00edm rozd\u00edlem hodnot v\u011bt\u0161\u00edm ne\u017e 10% nebo men\u0161\u00edm ne\u017e -10%.<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Nejv\u011bt\u0161\u00ed rozd\u00edly ve frekvenci kl\u00ed\u010dov\u00fdch slov mezi data engineerem a data scientistem<\/i><\/p>\n

Nejv\u00fdrazn\u011bj\u0161\u00ed n\u00e1r\u016fst vykazuje AWS: v data engineering se objevuje o 25% \u010dast\u011bji ne\u017e v data science (p\u0159ibli\u017en\u011b 45% a 20% z celkov\u00e9ho po\u010dtu nab\u00eddek pr\u00e1ce p\u0159\u00edslu\u0161n\u011b). Rozd\u00edl je znateln\u00fd!<\/p>\n

Zde jsou stejn\u00e9 \u00fadaje v m\u00edrn\u011b jin\u00e9m zobrazen\u00ed \u2013 na grafu jsou v\u00fdsledky pro stejn\u00e9 kl\u00ed\u010dov\u00e9 slovo v nab\u00eddk\u00e1ch na pozici data engineer a data scientist um\u00edst\u011bny vedle sebe.<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Nejv\u011bt\u0161\u00ed rozd\u00edly ve frekvenci kl\u00ed\u010dov\u00fdch slov mezi data engineerem a data scientistem<\/i><\/p>\n

Dal\u0161\u00ed v\u00fdznamn\u00fd skok jsem zaznamenal u Spark \u2013 datov\u00ed in\u017een\u00fd\u0159i \u010dasto pracuj\u00ed s velk\u00fdmi daty. Kafka<\/a><\/noindex> rovn\u011b\u017e vzrostl o 20 %, co\u017e znamen\u00e1 t\u00e9m\u011b\u0159 \u010dty\u0159n\u00e1sobn\u00fd n\u00e1r\u016fst ve srovn\u00e1n\u00ed s v\u00fdsledky pro pozice datov\u00fdch v\u011bdc\u016f. P\u0159enos dat je jednou z kl\u00ed\u010dov\u00fdch povinnost\u00ed datov\u00e9ho in\u017een\u00fdra. Nakonec se po\u010det zm\u00ednek o data engineeringu zv\u00fd\u0161il o 15 % v oblasti Java, NoSQL, Redshift, SQL a Hadoop.<\/p>\n

M\u00e9n\u011b popul\u00e1rn\u00ed v data engineeringu<\/b><\/p>\n

Nyn\u00ed se pod\u00edvejme, kter\u00e9 technologie jsou m\u00e9n\u011b popul\u00e1rn\u00ed v nab\u00eddk\u00e1ch pro datov\u00e9 in\u017een\u00fdry.
\nNejv\u011bt\u0161\u00ed pokles ve srovn\u00e1n\u00ed s oblast\u00ed datov\u00e9 v\u011bdy nastal u R<\/a><\/noindex>: vyskytoval se v p\u0159ibli\u017en\u011b 56 % nab\u00eddek, zde \u2013 pouze 17 %. Imponuj\u00edc\u00ed. R je programovac\u00ed jazyk, kter\u00fd si z\u00edskal popularitu mezi v\u011bdci a statistiky a obsazuje osm\u00e9 m\u00edsto v \u017eeb\u0159\u00ed\u010dku nejd\u011bsiv\u011bj\u0161\u00edch jazyk\u016f.<\/p>\n

SAS<\/a><\/noindex> se tak\u00e9 v nab\u00eddk\u00e1ch na pozici datov\u00e9ho in\u017een\u00fdra objevuje v\u00fdrazn\u011b m\u00e9n\u011b \u2013 rozd\u00edl \u010din\u00ed 14 %. SAS je propriet\u00e1rn\u00ed jazyk zam\u011b\u0159en\u00fd na pr\u00e1ci se statistikou a daty. Zaj\u00edmav\u00fd bod: soud\u011b podle v\u00fdsledk\u016f m\u00e9ho v\u00fdzkumu nab\u00eddek pro datov\u00e9 v\u011bdce<\/a><\/noindex>, v posledn\u00ed dob\u011b v\u00fdrazn\u011b ztratil na pozic\u00edch \u2013 v\u00edce ne\u017e jak\u00e1koli jin\u00e1 technologie.<\/p>\n

Popul\u00e1rn\u00ed jak v data engineeringu, tak v datov\u00e9 v\u011bd\u011b<\/b><\/p>\n

Je t\u0159eba poznamenat, \u017ee osm z deseti prvn\u00edch pozic v obou sad\u00e1ch se shoduje. SQL, Python, Spark, AWS, Java, Hadoop, Hive a Scala se dostaly do des\u00edtky jak pro oblast data engineeringu, tak pro datovou v\u011bdu. Na n\u00ed\u017ee uveden\u00e9m grafu m\u016f\u017eete vid\u011bt patn\u00e1ct nejpopul\u00e1rn\u011bj\u0161\u00edch technologi\u00ed u zam\u011bstnavatel\u016f datov\u00fdch in\u017een\u00fdr\u016f a vedle nich jejich ukazatel v nab\u00eddk\u00e1ch pro datov\u00e9 v\u011bdce.<\/p>\n

\"Nej\u017e\u00e1dan\u011bj\u0161\u00ed<\/p>\n

Doporu\u010den\u00ed<\/h4>\n

\nPokud chcete pracovat v oblasti data engineeringu, doporu\u010dil bych v\u00e1m osvojit si n\u00e1sleduj\u00edc\u00ed technologie \u2013 uv\u00e1d\u00edm je v po\u0159ad\u00ed p\u0159ibli\u017en\u00e9 priority.<\/p>\n

Nau\u010dte se SQL. Sklo\u0148uji v\u00e1s k PostgreSQL, proto\u017ee m\u00e1 otev\u0159en\u00fd k\u00f3d, je velmi popul\u00e1rn\u00ed v komunit\u011b a je v r\u016fstov\u00e9 f\u00e1zi. Jak pou\u017e\u00edvat jazyk, se m\u016f\u017eete dozv\u011bd\u011bt z knihy My Memorable SQL \u2013 jej\u00ed pilotn\u00ed verze je dostupn\u00e1 zde<\/a><\/noindex>.<\/p>\n

Osvojte si Python, i kdy\u017e ne na t\u00e9 nejt\u011b\u017e\u0161\u00ed \u00farovni. Kniha My Memorable Python je ur\u010dena pr\u00e1v\u011b pro za\u010d\u00e1te\u010dn\u00edky. M\u016f\u017eete si ji zakoupit na Amazon<\/a><\/noindex>, elektronickou kopii nebo fyzickou, na va\u0161\u00ed volb\u011b, nebo si ji st\u00e1hnout ve form\u00e1tu pdf nebo epub na t\u00e9to str\u00e1nce<\/a><\/noindex>.<\/p>\n

Jakmile se sezn\u00e1m\u00edte s Pythonem, p\u0159ejd\u011bte k pandas \u2013 Python knihovn\u011b, kter\u00e1 se pou\u017e\u00edv\u00e1 p\u0159i \u010di\u0161t\u011bn\u00ed a zpracov\u00e1n\u00ed dat. Pokud se zam\u011b\u0159ujete na pr\u00e1ci ve firm\u011b, kde je psan\u00ed v Pythonu nezbytn\u00e9 (co\u017e je v\u011bt\u0161ina), m\u016f\u017eete si b\u00fdt jisti, \u017ee znalost pandas se p\u0159edpokl\u00e1d\u00e1 jako standard. Pr\u00e1v\u011b dokon\u010duji \u00favodn\u00ed p\u0159\u00edru\u010dku pro pr\u00e1ci s pandas \u2013 m\u016f\u017eete se p\u0159ihl\u00e1sit<\/a><\/noindex>, abyste nezme\u0161kali datum vyd\u00e1n\u00ed.<\/p>\n

Osvojte si AWS. Pokud se chcete st\u00e1t data in\u017een\u00fdrem, bez cloudov\u00e9 platformy se neobejdete, a AWS je nejpopul\u00e1rn\u011bj\u0161\u00ed z nich. Velmi mi pomohly kurzy Linux Academy<\/a><\/noindex>, kdy\u017e jsem se u\u010dil data engineering na Google Cloud<\/a><\/noindex>, mysl\u00edm, \u017ee i pro AWS tam najdete dobr\u00e9 materi\u00e1ly.<\/p>\n

Pokud jste ji\u017e zvl\u00e1dli cel\u00fd tento seznam a chcete se je\u0161t\u011b v\u00edce zviditelnit v o\u010d\u00edch zam\u011bstnavatel\u016f jako data in\u017een\u00fdr, doporu\u010duji p\u0159idat Apache Spark pro pr\u00e1ci s velk\u00fdmi daty. I kdy\u017e m\u016fj pr\u016fzkum pracovn\u00edch nab\u00eddek uk\u00e1zal pokles z\u00e1jmu, u data in\u017een\u00fdr\u016f se st\u00e1le objevuje t\u00e9m\u011b\u0159 v ka\u017ed\u00e9 druh\u00e9 nab\u00eddce.<\/p>\n

Nakonec<\/h4>\n

\nDouf\u00e1m, \u017ee tento p\u0159ehled nej\u017e\u00e1dan\u011bj\u0161\u00edch technologi\u00ed pro data in\u017een\u00fdra byl pro v\u00e1s u\u017eite\u010dn\u00fd. Pokud v\u00e1s zaj\u00edm\u00e1, jak to vypad\u00e1 s pracovn\u00edmi nab\u00eddkami pro analytiky, p\u0159e\u010dt\u011bte si m\u016fj dal\u0161\u00ed \u010dl\u00e1nek<\/a><\/noindex>. P\u0159eji \u00fasp\u011b\u0161n\u00e9 in\u017een\u00fdrstv\u00ed!
\n
Zdroj: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0421\u043e\u0433\u043b\u0430\u0441\u043d\u043e \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0435 2019 \u0433\u043e\u0434\u0430, data engineer \u043d\u0430 \u0434\u0430\u043d\u043d\u044b\u0439 \u043c\u043e\u043c\u0435\u043d\u0442 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u0435\u0439, \u0441\u043f\u0440\u043e\u0441 \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0440\u0430\u0441\u0442\u0435\u0442 \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u0432\u0441\u0435\u0445 \u043f\u0440\u043e\u0447\u0438\u0445. 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