{"id":90783,"date":"2020-08-06T01:42:19","date_gmt":"2020-08-05T23:42:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1"},"modified":"2020-08-06T01:42:19","modified_gmt":"2020-08-05T23:42:19","slug":"kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sk\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","title":{"rendered":"Ako BigQuery od Google demokratizoval anal\u00fdzu d\u00e1t. \u010cas\u0165 1","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><b><i>Ahoj, Habr! Pr\u00e1ve teraz je v OTUS otvoren\u00e1 registr\u00e1cia na nov\u00fd kurz <noindex><a rel=\"nofollow\" href=\"https:\/\/otus.pw\/wDZQ\/\">\u201eData Engineer\u201c<\/a><\/noindex>V predve\u010der spustenia kurzu sme pre v\u00e1s tradi\u010dne pripravili preklad zauj\u00edmav\u00e9ho materi\u00e1lu. <\/i><\/b><\/p>\n<p>\nKa\u017ed\u00fd de\u0148 nav\u0161t\u00edvi Twitter viac ako sto mili\u00f3nov \u013eud\u00ed, aby sa dozvedeli, \u010do sa deje vo svete, a diskutovali o tom. Ka\u017ed\u00fd tweet a ka\u017ed\u00e1 in\u00e1 akcia pou\u017e\u00edvate\u013ea generuj\u00fa udalos\u0165, ktor\u00e1 je dostupn\u00e1 na intern\u00fa anal\u00fdzu \u00fadajov v Twitteri. Stovky zamestnancov analyzuj\u00fa a vizualizuj\u00fa tieto \u00fadaje, pri\u010dom zlep\u0161ovanie ich sk\u00fasenosti je hlavn\u00fdm prioritou t\u00edmu Twitter Data Platform. <\/p>\n<p>Ver\u00edme, \u017ee pou\u017e\u00edvatelia s \u0161irok\u00fdm spektrom technick\u00fdch zru\u010dnost\u00ed by mali ma\u0165 mo\u017enos\u0165 n\u00e1js\u0165 d\u00e1ta a ma\u0165 pr\u00edstup k dobre funguj\u00facim n\u00e1strojom na anal\u00fdzu a vizualiz\u00e1ciu na b\u00e1ze SQL. To by umo\u017enilo celej novej skupine pou\u017e\u00edvate\u013eov s men\u0161\u00edmi technick\u00fdmi zru\u010dnos\u0165ami, vr\u00e1tane d\u00e1tov\u00fdch analytikov a produktov\u00fdch mana\u017e\u00e9rov, \u010derpa\u0165 inform\u00e1cie z \u00fadajov, \u010do by im umo\u017enilo lep\u0161ie rozumie\u0165 a vyu\u017e\u00edva\u0165 mo\u017enosti Twitteru. Takto demokratizujeme anal\u00fdzu \u00fadajov v Twitteri. <noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>S postupn\u00fdm zdokona\u013eovan\u00edm na\u0161ich n\u00e1strojov a mo\u017enost\u00ed pre intern\u00fa anal\u00fdzu \u00fadajov sme boli svedkami zlep\u0161enia slu\u017eby Twitter. Napriek tomu je st\u00e1le priestor na rast. S\u00fa\u010dasn\u00e9 n\u00e1stroje, ako Scalding, vy\u017eaduj\u00fa program\u00e1torsk\u00e9 zru\u010dnosti. N\u00e1stroje na anal\u00fdzu na b\u00e1ze SQL, ako Presto a Vertica, maj\u00fa probl\u00e9my s v\u00fdkonom v ve\u013ekom meradle. Taktie\u017e m\u00e1me probl\u00e9m s distrib\u00faciou \u00fadajov medzi viacer\u00fdmi syst\u00e9mami bez trval\u00e9ho pr\u00edstupu k nim.<\/p>\n<p>Minul\u00fd rok sme ozn\u00e1mili <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.twitter.com\/engineering\/en_us\/topics\/infrastructure\/2018\/a-new-collaboration-with-google-cloud.html\">nov\u00e9 spolupr\u00e1cu s Google<\/a><\/noindex>, v r\u00e1mci ktorej pren\u00e1\u0161ame \u010dasti na\u0161ej <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.twitter.com\/engineering\/en_us\/topics\/infrastructure\/2019\/the-start-of-a-journey-into-the-cloud.html\">infrastrukt\u00fary \u00fadajov<\/a><\/noindex> na Google Cloud Platform (GCP). Dosiahli sme z\u00e1ver, \u017ee n\u00e1stroje Google Cloud <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/products\/big-data\/\">Ve\u013ek\u00e9 \u00fadaje<\/a><\/noindex> m\u00f4\u017eu pom\u00f4c\u0165 na\u0161im iniciat\u00edvam na demokratiz\u00e1ciu anal\u00fdzy, vizualiz\u00e1cie a strojov\u00e9ho u\u010denia v Twitteri:<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/\">BigQuery:<\/a><\/noindex>: \u00falo\u017eisko firemn\u00fdch \u00fadajov s SQL motorom na b\u00e1ze <noindex><a rel=\"nofollow\" href=\"https:\/\/ai.google\/research\/pubs\/pub36632\">Dremel<\/a><\/noindex>, ktor\u00fd je zn\u00e1my r\u00fdchlos\u0165ou, jednoduchos\u0165ou a zvl\u00e1da <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery-ml\/docs\/bigqueryml-intro\">strojov\u00e9 u\u010denie<\/a><\/noindex>.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/datastudio.google.com\/overview\">Data Studio:<\/a><\/noindex> n\u00e1stroj na vizualiz\u00e1ciu ve\u013ek\u00fdch \u00fadajov s funkciami spolupr\u00e1ce, ako v Google Docs.<\/li>\n<\/ul>\n<p>\nZ tohto \u010dl\u00e1nku sa dozviete o na\u0161ich sk\u00fasenostiach s t\u00fdmito n\u00e1strojmi: \u010do sme urobili, \u010domu sme sa nau\u010dili a \u010do budeme robi\u0165 \u010falej. Teraz sa zameriame na d\u00e1vkov\u00fa a interakt\u00edvnu analytiku. Analytiku v re\u00e1lnom \u010dase prediskutujeme v nasleduj\u00facom \u010dl\u00e1nku.<\/p>\n<h2>Hist\u00f3ria \u00falo\u017e\u00edsk \u00fadajov v Twitteri<\/h2>\n<p>\nPredt\u00fdm, ne\u017e sa hlb\u0161ie ponor\u00edme do BigQuery, stoj\u00ed za to stru\u010dne zhrn\u00fa\u0165 hist\u00f3riu \u00falo\u017eiska d\u00e1t na Twitteri. V roku 2011 sa anal\u00fdza d\u00e1t na Twitteri vykon\u00e1vala prostredn\u00edctvom Vertica a Hadoop. Na vytvorenie MapReduce \u00faloh v Hadoop-e sme pou\u017e\u00edvali Pig. V roku 2012 sme nahradili Pig za Scalding, ktor\u00fd mal Scala API s v\u00fdhodami, ako je mo\u017enos\u0165 vytv\u00e1ra\u0165 zlo\u017eit\u00e9 pipelines a jednoduchos\u0165 testovania. Napriek tomu mnoh\u00fdm d\u00e1tov\u00fdm analytikom a produktov\u00fdm mana\u017e\u00e9rom, ktor\u00ed sa c\u00edtili pohodlnej\u0161ie pri pr\u00e1ci s SQL, to bola dos\u0165 strm\u00e1 krivka u\u010denia. Pribli\u017ene v roku 2016 sme za\u010dali pou\u017e\u00edva\u0165 Presto ako SQL rozhranie pre d\u00e1ta Hadoop. Spark pon\u00fakal Python rozhranie, ktor\u00e9 z neho robilo dobr\u00fa vo\u013ebu pre ad hoc anal\u00fdzu d\u00e1t a strojov\u00e9 u\u010denie.<\/p>\n<p>Od roku 2018 sme pou\u017e\u00edvali nasleduj\u00face n\u00e1stroje na anal\u00fdzu a vizualiz\u00e1ciu d\u00e1t:<\/p>\n<ul>\n<li>Scalding pre produk\u010dn\u00e9 pipelines<\/li>\n<li>Scalding a Spark pre ad hoc anal\u00fdzu d\u00e1t a strojov\u00e9 u\u010denie<\/li>\n<li>Vertica a Presto pre ad hoc a interakt\u00edvnu SQL anal\u00fdzu <\/li>\n<li>Druid pre mal\u00fa interakt\u00edvnu, experiment\u00e1lnu a n\u00edzkolaten\u010dn\u00fa pr\u00edstup k metrik\u00e1m \u010dasov\u00fdch radov<\/li>\n<li>Tableau, Zeppelin a Pivot pre vizualiz\u00e1ciu d\u00e1t<\/li>\n<\/ul>\n<p>\nZistili sme, \u017ee hoci tieto n\u00e1stroje pon\u00fakaj\u00fa ve\u013emi siln\u00e9 mo\u017enosti, stret\u00e1vali sme sa s probl\u00e9mami pri realiz\u00e1cii pr\u00edstupnosti t\u00fdchto mo\u017enost\u00ed pre \u0161ir\u0161ie publikum na Twitteri. Expanding our platform through Google Cloud, we focus on simplifying our analytical tools for all of Twitter.<\/p>\n<h2>\u00dalo\u017eisko d\u00e1t BigQuery od Google <\/h2>\n<p>\nNieko\u013eko t\u00edmov na Twitteri u\u017e zahrnulo BigQuery do niektor\u00fdch zo svojich produk\u010dn\u00fdch pipelines. Vyu\u017eit\u00edm ich sk\u00fasenost\u00ed sme za\u010dali hodnoti\u0165 mo\u017enosti BigQuery pre v\u0161etky scen\u00e1re pou\u017eitia Twitteru. Na\u0161\u00edm cie\u013eom bolo pon\u00faknu\u0165 BigQuery celej spolo\u010dnosti a z\u00e1rove\u0148 ho \u0161tandardizova\u0165 a udr\u017eiava\u0165 v r\u00e1mci s\u00fapravy n\u00e1strojov Data Platform. To bolo zlo\u017eitou \u00falohou z mnoh\u00fdch d\u00f4vodov. Museli sme vyvin\u00fa\u0165 infra\u0161trukt\u00faru na spo\u013eahliv\u00fa akcept\u00e1ciu ve\u013ek\u00fdch objemov d\u00e1t, podpori\u0165 spr\u00e1vu d\u00e1t na \u00farovni celej spolo\u010dnosti, zabezpe\u010di\u0165 riadne riadenie pr\u00edstupu a ochranu s\u00fakromia klientov. Taktie\u017e sme museli vytvori\u0165 syst\u00e9my pre distrib\u00faciu zdrojov, monitorovanie a \u00fa\u010dtovanie, aby t\u00edmy mohli efekt\u00edvne vyu\u017e\u00edva\u0165 BigQuery.<\/p>\n<p>V novembri 2018 sme uviedli alfa verziu BigQuery a Data Studia pre cel\u00fa spolo\u010dnos\u0165. Zamestnancom Twitter sme pon\u00fakli niektor\u00e9 z na\u0161ich naj\u010dastej\u0161ie pou\u017e\u00edvan\u00fdch tabuliek s vy\u010disten\u00fdmi osobn\u00fdmi \u00fadajmi. BigQuery pou\u017e\u00edvalo viac ako 250 pou\u017e\u00edvate\u013eov z r\u00f4znych t\u00edmov, vr\u00e1tane in\u017einierstva, financi\u00ed a marketingu. V poslednej dobe vykon\u00e1vali pribli\u017ene 8 000 dopytov, spracov\u00e1vaj\u00fac pribli\u017ene 100 PB mesa\u010dne, bez zapo\u010d\u00edtania napl\u00e1novan\u00fdch dopytov. Z\u00edskav\u0161i ve\u013emi pozit\u00edvne sp\u00e4tn\u00e9 v\u00e4zby, rozhodli sme sa pokra\u010dova\u0165 a pon\u00faknu\u0165 BigQuery ako hlavn\u00fd zdroj pre interakciu s d\u00e1tami v Twittri.<\/p>\n<p>Tu je sch\u00e9ma vysoko\u00farov\u0148ovej architekt\u00fary n\u00e1\u0161ho \u00falo\u017eiska d\u00e1t Google BigQuery. <\/p>\n<p><img decoding=\"async\" alt=\"Ako BigQuery od Google demokratizoval anal\u00fdzu d\u00e1t. \u010cas\u0165 1\" src=\"\/wp-content\/uploads\/2020\/08\/6a3e0b43752ebbac641c43519c8c2fba.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nKop\u00edrujeme d\u00e1ta z lok\u00e1lnych klastrov Hadoop do Google Cloud Storage (GCS) pomocou intern\u00e9ho n\u00e1stroja Cloud Replicator. Potom pou\u017e\u00edvame Apache Airflow na vytv\u00e1ranie potrub\u00ed, ktor\u00e9 pou\u017e\u00edvaj\u00fa \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reference\/bq-cli-reference#bq_load\">bq_load<\/a><\/noindex>na na\u010d\u00edtanie d\u00e1t z GCS do BigQuery. Pou\u017e\u00edvame Presto na dotazovanie s\u00faborov Parquet alebo Thrift-LZO v GCS. BQ Blaster je intern\u00fd n\u00e1stroj Scalding na na\u010d\u00edtanie s\u00faborov HDFS Vertica a Thrift-LZO do BigQuery.<\/p>\n<p>V nasleduj\u00facich sekci\u00e1ch budeme diskutova\u0165 o na\u0161om pr\u00edstupe a poznatkoch v oblasti pou\u017eite\u013enosti, v\u00fdkonu, spr\u00e1vy d\u00e1t, funk\u010dnosti syst\u00e9mu a n\u00e1kladov.<\/p>\n<h4>Jednoduchos\u0165 pou\u017e\u00edvania<\/h4>\n<p>\nZistili sme, \u017ee pou\u017e\u00edvate\u013eom bolo \u013eahk\u00e9 za\u010da\u0165 s BigQuery, preto\u017ee nevy\u017eadovalo in\u0161tal\u00e1ciu softv\u00e9ru a pou\u017e\u00edvatelia k nemu mohli pristupova\u0165 prostredn\u00edctvom intuit\u00edvneho webov\u00e9ho rozhrania. Napriek tomu sa pou\u017e\u00edvatelia museli obozn\u00e1mi\u0165 s niektor\u00fdmi funkciami GCP a jeho konceptami, vr\u00e1tane tak\u00fdch zdrojov, ako s\u00fa projekty, s\u00fabory d\u00e1t a tabu\u013eky. Vyvinuli sme vzdel\u00e1vacie materi\u00e1ly a tutori\u00e1ly, aby sme pomohli pou\u017e\u00edvate\u013eom za\u010da\u0165. S nadobudnut\u00edm z\u00e1kladn\u00e9ho porozumenia sa pou\u017e\u00edvate\u013eom stalo jednoduch\u00e9 navigova\u0165 v s\u00faboroch d\u00e1t, prezera\u0165 sch\u00e9my a d\u00e1ta tabuliek, vykon\u00e1va\u0165 jednoduch\u00e9 dopyty a vizualizova\u0165 v\u00fdsledky v Data Studio.<\/p>\n<p>Na\u0161\u00edm cie\u013eom pri vkladan\u00ed d\u00e1t do BigQuery bolo poskytn\u00fa\u0165 hladk\u00e9 na\u010d\u00edtanie s\u00faborov d\u00e1t HDFS alebo GCS jedn\u00fdm kliknut\u00edm my\u0161i. Zva\u017eovali sme <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/composer\/\">Cloud Composer<\/a><\/noindex> (spravovan\u00fd Airflow), ale nemohli sme ho pou\u017ei\u0165 kv\u00f4li na\u0161emu modelu bezpe\u010dnosti \u201eDomain Restricted Sharing\u201c (viac inform\u00e1ci\u00ed n\u00e1jdete v sekcii \u201eSpr\u00e1va d\u00e1t\u201c ni\u017e\u0161ie). Experimentovali sme s Google Data Transfer Service (DTS) na organizovanie za\u0165a\u017eovac\u00edch \u00faloh BigQuery. Zatia\u013e \u010do DTS sa r\u00fdchlo nastavilo, nebolo flexibiln\u00e9 na vytv\u00e1ranie potrub\u00ed so z\u00e1vislos\u0165ami. Pre na\u0161u alfa verziu sme vytvorili vlastn\u00e9 prostredie Apache Airflow v GCE a pripravujeme ho na prev\u00e1dzku v produkcii a na podporu viacer\u00fdch zdrojov d\u00e1t, ako je Vertica.<\/p>\n<p>Na transform\u00e1ciu d\u00e1t v BigQuery pou\u017e\u00edvaj\u00fa u\u017e\u00edvatelia jednoduch\u00e9 SQL d\u00e1tov\u00e9 potrubia, pri\u010dom pou\u017e\u00edvaj\u00fa napl\u00e1novan\u00e9 dotazy. Pre zlo\u017eit\u00e9 viacer\u00e9 potrubia so z\u00e1vislos\u0165ami pl\u00e1nujeme pou\u017ei\u0165 bu\u010f na\u0161u vlastn\u00fa infra\u0161trukt\u00faru Airflow, alebo Cloud Composer spolu s <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/dataflow\/\">Cloud Dataflow<\/a><\/noindex>.<\/p>\n<h4>V\u00fdkon<\/h4>\n<p>\nBigQuery je navrhnut\u00fd na SQL dotazy v\u0161eobecn\u00e9ho pou\u017eitia, ktor\u00e9 spracov\u00e1vaj\u00fa ve\u013ek\u00e9 objemy d\u00e1t. Nie je ur\u010den\u00fd na dotazy s n\u00edzkou latenciou a vysokou priepustnos\u0165ou, ktor\u00e9 s\u00fa potrebn\u00e9 pre transak\u010dn\u00e9 datab\u00e1zy, ani na anal\u00fdzu \u010dasov\u00fdch radov s n\u00edzkou latenciou, realizovan\u00fa <noindex><a rel=\"nofollow\" href=\"https:\/\/druid.apache.org\/\">Apache Druid<\/a><\/noindex>. Pre interakt\u00edvne analytick\u00e9 dotazy na\u0161i u\u017e\u00edvatelia o\u010dak\u00e1vaj\u00fa dobu odozvy men\u0161iu ako jednu min\u00fatu. Museli sme navrhn\u00fa\u0165 pou\u017eitie BigQuery tak, aby sp\u013a\u0148alo tieto o\u010dak\u00e1vania. Na zabezpe\u010denie predv\u00eddate\u013en\u00e9ho v\u00fdkonu pre na\u0161ich u\u017e\u00edvate\u013eov sme vyu\u017eili funk\u010dnos\u0165 BigQuery dostupn\u00fa pre klientov na fixnej platbe, ktor\u00e1 umo\u017e\u0148uje vlastn\u00edkom projektov rezervova\u0165 minim\u00e1lne sloty pre svoje dotazy. <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/docs\/slots\">Slot<\/a><\/noindex> BigQuery je jednotka v\u00fdpo\u010dtovej sily potrebn\u00e1 na vykon\u00e1vanie SQL dotazov. <\/p>\n<p>Analyzovali sme viac ako 800 po\u017eiadaviek, ktor\u00e9 spracov\u00e1vaj\u00fa pribli\u017ene 1 TB d\u00e1t, a zistili sme, \u017ee priemern\u00fd \u010das spracovania bol 30 sek\u00fand. Taktie\u017e sme zistili, \u017ee v\u00fdkon silne z\u00e1vis\u00ed od vyu\u017e\u00edvania na\u0161ich slotov v r\u00f4znych projektoch a \u00faloh\u00e1ch. Museli sme jasne oddeli\u0165 na\u0161e produk\u010dn\u00e9 a ad hoc rezervy slotov, aby sme udr\u017eali v\u00fdkon pre v\u00fdrobn\u00e9 scen\u00e1re pou\u017eitia a interakt\u00edvnu anal\u00fdzu. To malo v\u00fdrazn\u00fd vplyv na n\u00e1\u0161 dizajn pre rezerv\u00e1ciu slotov a hierarchiu projektov.<\/p>\n<p><i>O spr\u00e1ve d\u00e1t, funk\u010dnosti a n\u00e1kladoch syst\u00e9mov sa porozpr\u00e1vame u\u017e onedlho v druhej \u010dasti prekladu, a teraz poz\u00fdvame v\u0161etk\u00fdch, ktor\u00ed maj\u00fa z\u00e1ujem, na <noindex><a rel=\"nofollow\" href=\"https:\/\/otus.pw\/wDZQ\/\">bezplatn\u00fd \u017eiv\u00fd webin\u00e1r<\/a><\/noindex>, v r\u00e1mci ktor\u00e9ho sa dozviete podrobnosti o kurze a budete ma\u0165 mo\u017enos\u0165 kl\u00e1s\u0165 ot\u00e1zky n\u00e1\u0161mu expertovi \u2014 Egorovi Mate\u0161ukovi (Senior Data Engineer, MaximaTelecom). <\/i><\/p>\n<p><\/p>\n<h2>\u010c\u00edta\u0165 \u010falej:<\/h2>\n<p><\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/501380\/\">Data Build Tool alebo \u010do maj\u00fa spolo\u010dn\u00e9 D\u00e1tov\u00fd Sklad a Smoothie<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/502324\/\">Ponorenie do Delta Lake: n\u00faten\u00e9 aplikovanie a evol\u00facia sch\u00e9my<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/503132\/\">Vysokor\u00fdchlostn\u00fd Apache Parquet na Pythone s Apache Arrow<\/a><\/noindex><\/li>\n<\/ul>\n<p>Zdroj: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/513780\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! \u041f\u0440\u044f\u043c\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0432 OTUS \u043e\u0442\u043a\u0440\u044b\u0442 \u043d\u0430\u0431\u043e\u0440 \u043d\u0430 \u043d\u043e\u0432\u044b\u0439 \u043f\u043e\u0442\u043e\u043a \u043a\u0443\u0440\u0441\u0430 \u00abData Engineer\u00bb. \u0412 \u043f\u0440\u0435\u0434\u0434\u0432\u0435\u0440\u0438\u0438 \u0441\u0442\u0430\u0440\u0442\u0430 \u043a\u0443\u0440\u0441\u0430 \u043c\u044b \u0442\u0440\u0430\u0434\u0438\u0446\u0438\u043e\u043d\u043d\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u043b\u0438 \u0434\u043b\u044f \u0432\u0430\u0441 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0433\u043e \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u0430. \u041a\u0430\u0436\u0434\u044b\u0439 \u0434\u0435\u043d\u044c \u0431\u043e\u043b\u0435\u0435 \u0441\u0442\u0430 \u043c\u0438\u043b\u043b\u0438\u043e\u043d\u043e\u0432 \u0447\u0435\u043b\u043e\u0432\u0435\u043a \u043f\u043e\u0441\u0435\u0449\u0430\u044e\u0442 Twitter, \u0447\u0442\u043e\u0431\u044b \u0443\u0437\u043d\u0430\u0442\u044c, \u0447\u0442\u043e \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u0432 \u043c\u0438\u0440\u0435, \u0438 \u043e\u0431\u0441\u0443\u0434\u0438\u0442\u044c \u044d\u0442\u043e. \u041a\u0430\u0436\u0434\u044b\u0439 \u0442\u0432\u0438\u0442 \u0438 \u043b\u044e\u0431\u043e\u0435 \u0434\u0440\u0443\u0433\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u044e\u0442 \u0441\u043e\u0431\u044b\u0442\u0438\u0435, \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e\u0435 \u0434\u043b\u044f \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u0435\u0433\u043e [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":90784,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-90783","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\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! \u041f\u0440\u044f\u043c\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0432 OTUS \u043e\u0442\u043a\u0440\u044b\u0442 \u043d\u0430\u0431\u043e\u0440 \u043d\u0430 \u043d\u043e\u0432\u044b\u0439 \u043f\u043e\u0442\u043e\u043a \u043a\u0443\u0440\u0441\u0430 \u00abData Engineer\u00bb.\" \/>\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\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1\" \/>\n\t\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.4\" \/>\n\t\t<meta property=\"og:locale\" content=\"sk_SK\" \/>\n\t\t<meta property=\"og:site_name\" content=\"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"\ud83e\udd47\u041a\u0430\u043a BigQuery \u043e\u0442 Google \u0434\u0435\u043c\u043e\u043a\u0440\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043b \u0430\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445. \u0427\u0430\u0441\u0442\u044c 1 | ProHoster\" \/>\n\t\t<meta property=\"og:description\" content=\"\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! \u041f\u0440\u044f\u043c\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0432 OTUS \u043e\u0442\u043a\u0440\u044b\u0442 \u043d\u0430\u0431\u043e\u0440 \u043d\u0430 \u043d\u043e\u0432\u044b\u0439 \u043f\u043e\u0442\u043e\u043a \u043a\u0443\u0440\u0441\u0430 \u00abData Engineer\u00bb.\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/sk\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2020-08-05T23:42:19+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2020-08-05T23:42:19+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One SEO -->\n\n","aioseo_head_json":{"title":"\ud83e\udd47Ako BigQuery od Google demokratizoval anal\u00fdzu d\u00e1t. \u010cas\u0165 1 | ProHoster","description":"Ahoj, Habr! Pr\u00e1ve teraz je v OTUS otvoren\u00fd z\u00e1pis na nov\u00fd tok kurzu \u201eData Engineer\u201c.","canonical_url":"https:\/\/prohoster.info\/sk\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sk_SK","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47\u041a\u0430\u043a BigQuery \u043e\u0442 Google \u0434\u0435\u043c\u043e\u043a\u0440\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043b \u0430\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445. \u0427\u0430\u0441\u0442\u044c 1 | ProHoster","og:description":"\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! \u041f\u0440\u044f\u043c\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0432 OTUS \u043e\u0442\u043a\u0440\u044b\u0442 \u043d\u0430\u0431\u043e\u0440 \u043d\u0430 \u043d\u043e\u0432\u044b\u0439 \u043f\u043e\u0442\u043e\u043a \u043a\u0443\u0440\u0441\u0430 \u00abData Engineer\u00bb.","og:url":"https:\/\/prohoster.info\/sk\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2020-08-05T23:42:19+00:00","article:modified_time":"2020-08-05T23:42:19+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":{"post_id":"90783","title":null,"description":null,"keywords":null,"keyphrases":null,"primary_term":null,"canonical_url":null,"og_title":null,"og_description":null,"og_object_type":"default","og_image_type":"default","og_image_url":null,"og_image_width":null,"og_image_height":null,"og_image_custom_url":null,"og_image_custom_fields":null,"og_video":null,"og_custom_url":null,"og_article_section":null,"og_article_tags":null,"twitter_use_og":false,"twitter_card":"default","twitter_image_type":"default","twitter_image_url":null,"twitter_image_custom_url":null,"twitter_image_custom_fields":null,"twitter_title":null,"twitter_description":null,"schema":{"blockGraphs":[],"customGraphs":[],"default":{"data":{"Article":[],"Course":[],"Dataset":[],"FAQPage":[],"Movie":[],"Person":[],"Product":[],"ProductReview":[],"Car":[],"Recipe":[],"Service":[],"SoftwareApplication":[],"WebPage":[]},"graphName":"","isEnabled":true},"graphs":[]},"schema_type":null,"schema_type_options":null,"pillar_content":false,"robots_default":true,"robots_noindex":false,"robots_noarchive":false,"robots_nosnippet":false,"robots_nofollow":false,"robots_noimageindex":false,"robots_noodp":false,"robots_notranslate":false,"robots_max_snippet":null,"robots_max_videopreview":null,"robots_max_imagepreview":"large","priority":null,"frequency":null,"local_seo":null,"seo_analyzer_scan_date":null,"breadcrumb_settings":null,"limit_modified_date":false,"reviewed_by":null,"ai":null,"created":"2021-02-28 12:42:25","updated":"2022-10-03 03:00:18","focus_keyword":null,"additional_keywords":null,"truseo_locale":null},"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/posts\/90783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/comments?post=90783"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/posts\/90783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/media\/90784"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/media?parent=90783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/categories?post=90783"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/sk\/wp-json\/wp\/v2\/tags?post=90783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}