{"id":55701,"date":"2020-01-26T00:00:00","date_gmt":"2020-01-25T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/nuzhno-li-nam-ozero-dannyh-a-chto-delat-s-hranilishhem-dannyh"},"modified":"2020-02-18T14:03:50","modified_gmt":"2020-02-18T11:03:50","slug":"nuzhno-li-nam-ozero-dannyh-a-chto-delat-s-hranilishhem-dannyh","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/nuzhno-li-nam-ozero-dannyh-a-chto-delat-s-hranilishhem-dannyh","title":{"rendered":"Avem nevoie de un lac de date? Ce facem cu un depozit de date?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Aceasta este o traducere a articolului meu de pe medium \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/medium.com\/rock-your-data\/getting-started-with-data-lake-4bb13643f9\">Introducere \u00een Data Lake<\/a><\/noindex>, care s-a dovedit a fi destul de popular, probabil din cauza simplit\u0103\u021bii sale. A\u0219adar, am decis s\u0103 o scriu \u00een limba rom\u00e2n\u0103 \u0219i s\u0103 o completez pu\u021bin, astfel \u00eenc\u00e2t o persoan\u0103 obi\u0219nuit\u0103, care nu este specialist \u00een domeniul datelor, s\u0103 \u00een\u021beleag\u0103 ce este un depozit de date (DW), ce este un lac de date (Data Lake) \u0219i cum coexist\u0103 acestea. <\/p>\n<p>De ce am vrut s\u0103 scriu despre lacul de date? Lucrez cu date \u0219i analiz\u0103 de peste 10 ani, iar acum lucrez cu date mari la Amazon Alexa AI \u00een Cambridge, care este \u00een Boston, de\u0219i locuiesc \u00een Victoria, pe insula Vancouver, \u0219i sunt adesea \u00een Boston, Seattle \u0219i Vancouver, iar uneori chiar \u0219i \u00een Moscova, prezent\u00e2nd la conferin\u021be. De asemenea, din c\u00e2nd \u00een c\u00e2nd scriu, dar scriu \u00een principal \u00een englez\u0103, \u0219i am scris deja <noindex><a rel=\"nofollow\" href=\"https:\/\/www.amazon.com\/Dmitry-Anoshin\/e\/B01A5PVT2M\">c\u00e2teva c\u0103r\u021bi<\/a><\/noindex>, de asemenea, am nevoia de a \u00eemp\u0103rt\u0103\u0219i tendin\u021bele analitice din America de Nord, \u0219i uneori scriu \u00een <noindex><a rel=\"nofollow\" href=\"https:\/\/t.me\/rockyourdata\">Telegram<\/a><\/noindex>.<\/p>\n<p>\u00centotdeauna am lucrat cu depozitele de date, \u0219i din 2015 am \u00eenceput s\u0103 lucrez intens cu Amazon Web Services, \u0219i \u00een general am trecut la analiza \u00een cloud (AWS, Azure, GCP). Am observat evolu\u021bia solu\u021biilor de analiz\u0103 din 2007 \u0219i chiar am lucrat pentru vendorul de depozite de date Teradata, implement\u00e2ndu-le la Sberbank, atunci a ap\u0103rut Big Data cu Hadoop. Toat\u0103 lumea vorbea c\u0103 era depozitelor a trecut \u0219i acum totul era pe Hadoop, iar apoi au \u00eenceput s\u0103 vorbeasc\u0103 despre Data Lake, spun\u00e2nd din nou c\u0103 acum depozitul de date \u0219i-a \u00eencheiat existen\u021ba. Dar, din fericire (poate pentru unii din nefericire, care c\u00e2\u0219tigau mul\u021bi bani din configurarea Hadoop), depozitul de date nu a disp\u0103rut. <br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\n\u00cen acest articol vom examina ce este un lac de date. Articolul este destinat persoanelor care au pu\u021bin\u0103 experien\u021b\u0103 cu depozitele de date sau poate chiar deloc.<\/p>\n<p><img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/85b3eda809ce19fc33efb2fad4e93a41.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n\u00cen imagine este lacul Bled, acesta este unul dintre lacurile mele favorite, de\u0219i am fost acolo doar o dat\u0103, dar l-am \u021binut minte toat\u0103 via\u021ba. Dar vom vorbi despre un alt tip de lac \u2014 lacul de date. Probabil mul\u021bi dintre voi a\u021bi auzit deja acest termen, dar o alt\u0103 defini\u021bie nu stric\u0103.<\/p>\n<p>\u00cen primul r\u00e2nd, iat\u0103 cele mai populare defini\u021bii ale Lacului de Date:<\/p>\n<blockquote><p>\u201eun sistem de stocare a tuturor tipurilor de date brute, care sunt disponibile pentru analiz\u0103 de c\u0103tre oricine din organiza\u021bie\u201d \u2014 Martin Fowler.<\/p><\/blockquote>\n<blockquote><p>\u00abDac\u0103 crede\u021bi c\u0103 un lac de date este o sticl\u0103 de ap\u0103 \u2014 purificat\u0103, ambalat\u0103 \u0219i por\u021bionat\u0103 pentru un consum convenabil, atunci lacul de date este un rezervor imens cu ap\u0103 \u00een forma sa natural\u0103. Utilizatorii pot umple ap\u0103 pentru ei \u00een\u0219i\u0219i, pot s\u0103 sape la ad\u00e2ncime, s\u0103 exploreze\u00bb \u2014 James Dickson. <\/p><\/blockquote>\n<p> Acum \u0219tim exact c\u0103 lacul de date se refer\u0103 la analitic\u0103, el ne permite s\u0103 stoc\u0103m volume mari de date \u00een forma lor ini\u021bial\u0103 \u0219i avem accesul necesar \u0219i convenabil la date.<\/p>\n<p>\u00cemi place adesea s\u0103 simplific lucrurile, dac\u0103 pot s\u0103 explic un termen complex \u00een cuvinte simple, \u00eenseamn\u0103 c\u0103 am \u00een\u021beles cum func\u021bioneaz\u0103 \u0219i pentru ce este nevoie. Ca atunci c\u00e2nd am fost pe iPhone \u00een galeria foto \u0219i mi-a venit ideea, e ca un lac de date real, am creat chiar un slide pentru conferin\u021be:<\/p>\n<p><img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/1956ddf4091ca00f6d86c33a6780d495.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nTotul este foarte simplu. Facem o fotografie cu telefonul, fotografia este salvat\u0103 pe telefon \u0219i poate fi salvat\u0103 \u00een iCloud (stocare de fi\u0219iere \u00een cloud). De asemenea, telefonul colecteaz\u0103 meta-date despre fotografie: ce este reprezentat, geoeticheta, ora. Ca rezultat, putem folosi interfa\u021ba convenabil\u0103 a iPhone-ului pentru a g\u0103si fotografia noastr\u0103 \u0219i \u00een plus, vedem indicatori, de exemplu, c\u00e2nd caut fotografii cu cuv\u00e2ntul foc (fire), g\u0103sesc 3 fotografii cu imaginea unui foc de tab\u0103r\u0103. Pentru mine, este ca un instrument de Business Intelligence, care func\u021bioneaz\u0103 foarte repede \u0219i precis. <\/p>\n<p>\u0218i, desigur, nu putem uita de securitate (autorizare \u0219i autentificare), altfel datele noastre ar putea ajunge cu u\u0219urin\u021b\u0103 \u00een acces deschis. Exist\u0103 foarte multe \u0219tiri despre mari corpora\u021bii \u0219i startup-uri ale c\u0103ror date au ajuns \u00een acces deschis din cauza neglijen\u021bei dezvoltatorilor \u0219i neaplicarea unor reguli simple.<\/p>\n<p>Chiar \u0219i o imagine simpl\u0103 ne ajut\u0103 s\u0103 \u00een\u021belegem ce este un lac de date, diferen\u021bele sale fa\u021b\u0103 de un depozit de date tradi\u021bional \u0219i principalele sale elemente:<\/p>\n<ol>\n<li><b>\u00cenc\u0103rcarea datelor <\/b>(Ingestion) \u2014 componenta cheie a lacului de date. Datele pot ajunge \u00een depozitul de date \u00een dou\u0103 moduri \u2014 batch (\u00eenc\u0103rcare cu intervale) \u0219i streaming (flux de date).<\/li>\n<li><b>Stocare de fi\u0219iere<\/b> (Storage) \u2014 componenta principal\u0103 a Lacului de Date. Este necesar ca depozitul s\u0103 fie u\u0219or scalabil, extrem de fiabil \u0219i s\u0103 aib\u0103 un cost sc\u0103zut. De exemplu, \u00een AWS este S3.<\/li>\n<li><b>Catalog \u0219i C\u0103utare<\/b> (Catalog and Search) \u2014 pentru a evita Mla\u0219tina de Date (atunci c\u00e2nd \u00eenghesuim toate datele \u00eentr-un singur loc \u0219i apoi devine imposibil s\u0103 lucr\u0103m cu ele), trebuie s\u0103 cre\u0103m un strat de metadate pentru clasificarea datelor, astfel \u00eenc\u00e2t utilizatorii s\u0103 poat\u0103 g\u0103si u\u0219or informa\u021biile necesare pentru analiz\u0103. \u00cen plus, se pot utiliza solu\u021bii suplimentare pentru c\u0103utare, cum ar fi ElasticSearch. C\u0103utarea ajut\u0103 utilizatorul s\u0103 g\u0103seasc\u0103 datele necesare printr-o interfa\u021b\u0103 prietenoas\u0103.<\/li>\n<li><b>Prelucrare<\/b> (Process) \u2014 acest pas r\u0103spunde de prelucrarea \u0219i transformarea datelor. Putem transforma datele, modifica structurile acestora, cur\u0103\u021ba \u0219i multe altele. <\/li>\n<li><b>Securitate<\/b> (Security) \u2014 este important s\u0103 aloc\u0103m timp pentru designul solu\u021biei de securitate. De exemplu, criptarea datelor \u00een timpul stoc\u0103rii, prelucr\u0103rii \u0219i \u00eenc\u0103rc\u0103rii. Este important s\u0103 folosim metode de autentificare \u0219i autorizare. \u00cen concluzie, este nevoie de un instrument de audit.<\/li>\n<\/ol>\n<p>\nDin punct de vedere practic, putem caracteriza lacul de date prin trei atribute:<\/p>\n<ol>\n<li><b>Colecta\u021bi \u0219i stoca\u021bi orice.<\/b> \u2014 lacul de date con\u021bine toate informa\u021biile, at\u00e2t date brute \u0219i neprelucrate pe orice perioad\u0103 de timp, c\u00e2t \u0219i date prelucrate\/cur\u0103\u021bate.<\/li>\n<li><b>Analiz\u0103 profund\u0103<\/b> \u2014 lacul de date permite utilizatorilor s\u0103 exploreze \u0219i s\u0103 analizeze datele.<\/li>\n<li><b>Acces flexibil<\/b> \u2014 lacul de date ofer\u0103 un acces flexibil pentru diverse tipuri de date \u0219i pentru diferite scenarii.<\/li>\n<\/ol>\n<p>\nAcum putem discuta despre diferen\u021ba dintre un depozit de date \u0219i un lac de date. De obicei, oamenii \u00eentreab\u0103:<\/p>\n<ul>\n<li>Dar ce face depozitul de date?<\/li>\n<li>\u00cenlocuim depozitul de date cu lacul de date sau \u00eel extindem?<\/li>\n<li>Putem totu\u0219i s\u0103 ne descurc\u0103m f\u0103r\u0103 lacul de date?<\/li>\n<\/ul>\n<p>\n\u00cen scurt, nu exist\u0103 un r\u0103spuns clar. Totul depinde de situa\u021bia concret\u0103, abilit\u0103\u021bile din echip\u0103 \u0219i buget. De exemplu, migrarea unui depozit de date pe Oracle \u00een AWS \u0219i crearea unui lac de date de c\u0103tre filiala Amazon \u2014 Woot \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/aws.amazon.com\/blogs\/big-data\/our-data-lake-story-how-woot-com-built-a-serverless-data-lake-on-aws\/\">Povestea noastr\u0103 despre lacul de date: Cum a construit Woot.com un lac de date f\u0103r\u0103 server pe AWS<\/a><\/noindex>. <\/p>\n<p>Pe de alt\u0103 parte, furnizorul Snowflake afirm\u0103 c\u0103 nu mai trebuie s\u0103 te g\u00e2nde\u0219ti la lacul de date, deoarece platforma lor de date (p\u00e2n\u0103 \u00een 2020 era depozit de date) \u00ee\u021bi permite s\u0103 combini at\u00e2t lacul de date, c\u00e2t \u0219i depozitul de date. Am lucrat pu\u021bin cu Snowflake, \u0219i este \u00eentr-adev\u0103r un produs unic care poate face asta. Pre\u021bul este o alt\u0103 problem\u0103.<\/p>\n<p>\u00cen concluzie, opinia mea personal\u0103 este c\u0103 avem \u00een continuare nevoie de un depozit de date ca surs\u0103 principal\u0103 pentru raportarea noastr\u0103, iar tot ce nu se \u00eencadreaz\u0103 este p\u0103strat \u00een lacul de date. Toat\u0103 rolul analiticii este de a oferi acces u\u0219or afacerii pentru luarea deciziilor. Indiferent cum ar fi, utilizatorii de business lucreaz\u0103 mai eficient cu un depozit de date dec\u00e2t cu un lac de date; de exemplu, \u00een Amazon exist\u0103 Redshift (depozit de date analitic) \u0219i exist\u0103 Redshift Spectrum\/Athena (interfa\u021ba SQL pentru lacul de date din S3 bazat pe Hive\/Presto). Acelea\u0219i lucruri se aplic\u0103 \u0219i altor depozite de date analitice moderne.<\/p>\n<p>S\u0103 analiz\u0103m arhitectura tipic\u0103 a unui depozit de date:<\/p>\n<p><img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/a2015f7f5e28e8a671699682105e011e.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nAceasta este o solu\u021bie clasic\u0103. Avem surse de date, iar prin ETL\/ELT copiem datele \u00een depozitul de date analitic \u0219i le conect\u0103m la solu\u021bia de Business Intelligence (preferata mea este Tableau, dar care este a ta?). <\/p>\n<p>Aceast\u0103 solu\u021bie are urm\u0103toarele dezavantaje:<\/p>\n<ul>\n<li>Opera\u021biile ETL\/ELT necesit\u0103 timp \u0219i resurse.<\/li>\n<li>\u00cen general, memoria pentru stocarea datelor \u00een depozitul de date analitic nu este ieftin\u0103 (de exemplu, Redshift, BigQuery, Teradata), deoarece trebuie s\u0103 achizi\u021bion\u0103m un \u00eentreg cluster.<\/li>\n<li>Utilizatorii de business au acces la datele curate \u0219i adesea agregate \u0219i nu au posibilitatea de a ob\u021bine date brute.<\/li>\n<\/ul>\n<p>\nDesigur, totul depinde de cazul dumneavoastr\u0103. Dac\u0103 nu ave\u021bi probleme cu depozitul de date, atunci nu ave\u021bi nevoie de un lac de date. Dar c\u00e2nd apar probleme cu lipsa de spa\u021biu, putere sau costul devine un factor cheie, atunci se poate lua \u00een considerare op\u021biunea unui lac de date. De aceea, lacul de date este foarte popular. Iat\u0103 un exemplu de arhitectur\u0103 a lacului de date:<br \/>\n<img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/c952e2202a6ce2c8d7d91215aa5390cc.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nFolosind abordarea lacului de date, \u00eenc\u0103rc\u0103m datele brute \u00een lacul nostru de date (batch sau streaming), apoi proces\u0103m datele dup\u0103 necesitate. Lacul de date permite utilizatorilor de business s\u0103 \u00ee\u0219i creeze propriile transform\u0103ri de date (ETL\/ELT) sau s\u0103 analizeze datele \u00een solu\u021biile de Business Intelligence (dac\u0103 exist\u0103 driverul necesar).<\/p>\n<blockquote><p>Scopul oric\u0103rei solu\u021bii analitice este de a servi utilizatorilor de business. Prin urmare, trebuie \u00eentotdeauna s\u0103 lucr\u0103m \u00een func\u021bie de cerin\u021bele afacerii. (La Amazon, acesta este unul dintre principiile \u2014 working backwards).<\/p><\/blockquote>\n<p> Lucr\u00e2nd at\u00e2t cu depozitul de date, c\u00e2t \u0219i cu lacul de date, putem compara cele dou\u0103 solu\u021bii:<\/p>\n<p><img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/07b62ac9838438a0b28caf1b22b2dccd.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nConcluzia principal\u0103 este c\u0103 stocarea datelor nu concureaz\u0103 \u00een niciun fel cu lacul de date, ci mai degrab\u0103 \u00eel completeaz\u0103. Dar este decizia dumneavoastr\u0103 s\u0103 determina\u021bi ce se potrive\u0219te cel mai bine \u00een cazul dumneavoastr\u0103. \u00centotdeauna este interesant s\u0103 \u00eencerca\u021bi personal \u0219i s\u0103 trasa\u021bi concluzii corecte.<\/p>\n<p>A\u0219 dori, de asemenea, s\u0103 vorbesc despre unul dintre cazurile \u00een care am \u00eenceput s\u0103 folosesc abordarea lacului de date. Totul este destul de banal, am \u00eencercat s\u0103 folosesc un instrument ELT (avem Matillion ETL) \u0219i Amazon Redshift, solu\u021bia mea a func\u021bionat, dar nu se \u00eencadra \u00een cerin\u021be.<\/p>\n<p>A trebuit s\u0103 iau jurnalele web, s\u0103 le transform \u0219i s\u0103 le agreg, pentru a oferi date pentru dou\u0103 cazuri:<\/p>\n<ol>\n<li>Echipa de marketing dorea s\u0103 analizeze activitatea robo\u021bilor pentru SEO<\/li>\n<li>IT-ul dorea s\u0103 urm\u0103reasc\u0103 metricile de func\u021bionare ale site-urilor<\/li>\n<\/ol>\n<p>\nFoarte simple, ni\u0219te jurnale foarte simple. Iat\u0103 un exemplu:<\/p>\n<pre><code class=\"plaintext\">https 2018-07-02T22:23:00.186641Z app\/my-loadbalancer\/50dc6c495c0c9188 \n192.168.131.39:2817 10.0.0.1:80 0.086 0.048 0.037 200 200 0 57 \n\"GET https:\/\/www.example.com:443\/ HTTP\/1.1\" \"curl\/7.46.0\" ECDHE-RSA-AES128-GCM-SHA256 TLSv1.2 \narn:aws:elasticloadbalancing:us-east-2:123456789012:targetgroup\/my-targets\/73e2d6bc24d8a067\n\"Root=1-58337281-1d84f3d73c47ec4e58577259\" \"www.example.com\" \"arn:aws:acm:us-east-2:123456789012:certificate\/12345678-1234-1234-1234-123456789012\"\n1 2018-07-02T22:22:48.364000Z \"authenticate,forward\" \"-\" \"-\"<\/code><\/pre>\n<p>\nUn fi\u0219ier c\u00e2nt\u0103rea \u00eentre 1-4 megabytes.<\/p>\n<p>Dar a fost o dificultate. Aveam 7 domenii \u00een \u00eentreaga lume \u0219i, \u00eentr-o zi, se creau 7000 de fi\u0219iere. Acesta nu este un volum foarte mare, \u00een total 50 de gigabytes. Dar dimensiunea cluster-ului nostru Redshift era, de asemenea, mic\u0103 (4 noduri). \u00cenc\u0103rcarea tradi\u021bional\u0103 a unui fi\u0219ier dura aproximativ un minut. A\u0219adar, problema nu se rezolva direct. \u0218i aceasta a fost ocazia c\u00e2nd am decis s\u0103 folosesc abordarea lacului de date. Solu\u021bia ar\u0103ta aproximativ a\u0219a:<\/p>\n<p><img decoding=\"async\" alt=\"Avem nevoie de un lac de date? Ce facem cu un depozit de date?\" src=\"\/wp-content\/uploads\/2020\/01\/21ca33e82da56f83b3deab4c32eb2345.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nEste destul de simpl\u0103 (vreau s\u0103 subliniez c\u0103 avantajul lucrului \u00een cloud este simplitatea). Am folosit:<\/p>\n<ul>\n<li>AWS Elastic Map Reduce (Hadoop) ca putere de calcul <\/li>\n<li>AWS S3 ca stocare de fi\u0219iere cu posibilitatea de criptare a datelor \u0219i restric\u021bionarea accesului<\/li>\n<li>Spark ca putere de calcul InMemory \u0219i PySpark pentru logic\u0103 \u0219i transformarea datelor<\/li>\n<li>Parquet ca rezultat al muncii Spark<\/li>\n<li>AWS Glue Crawler ca colector de metadate despre noile date \u0219i parti\u021bii<\/li>\n<li>Redshift Spectrum ca interfa\u021b\u0103 SQL pentru lacul de date pentru utilizatorii existen\u021bi Redshift<\/li>\n<\/ul>\n<p>\nCel mai mic cluster EMR+Spark a procesat \u00eentreaga serie de fi\u0219iere \u00een 30 de minute. Exist\u0103 \u0219i alte cazuri pentru AWS, \u00een special multe legate de Alexa, unde exist\u0103 o cantitate foarte mare de date. <\/p>\n<p>Recent am aflat un dezavantaj al lacului de date - acesta este GDPR. Problema este c\u0103, atunci c\u00e2nd clientul cere \u0219tergerea acestuia, iar datele se afl\u0103 \u00eentr-unul dintre fi\u0219iere, nu putem folosi Data Manipulation Language \u0219i opera\u021bia DELETE ca \u00een baza de date.<\/p>\n<p>Sper c\u0103 articolul a clarificat diferen\u021ba dintre un depozit de date \u0219i un lac de date. Dac\u0103 \u021bi-a fost interesant, pot traduce \u0219i alte articole de-ale mele sau articolele unor profesioni\u0219ti pe care \u00eei citesc. De asemenea, pot vorbi despre solu\u021biile cu care lucrez \u0219i arhitectura acestora.<br \/>\n<br \/>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/485180\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u042d\u0442\u043e \u0441\u0442\u0430\u0442\u044c\u044f \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043c\u043e\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0438 \u043d\u0430 medium \u2014 Getting Started with Data Lake, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043e\u043a\u0430\u0437\u0430\u043b\u0430\u0441\u044c \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u043d\u043e\u0439, \u043d\u0430\u0432\u0435\u0440\u043d\u043e\u0435 \u0438\u0437-\u0437\u0430 \u0441\u0432\u043e\u0435\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0442\u044b. \u041f\u043e\u044d\u0442\u043e\u043c\u0443 \u044f \u0440\u0435\u0448\u0438\u043b \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0435\u0435 \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435 \u0438 \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u044c, \u0447\u0442\u043e\u0431\u044b \u043f\u0440\u043e\u0441\u0442\u043e\u043c\u0443 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0443, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043d\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u043e\u043c \u043f\u043e \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0441\u0442\u0430\u043b\u043e \u043f\u043e\u043d\u044f\u0442\u043d\u043e, \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u0445\u0440\u0430\u043d\u0438\u043b\u0438\u0449\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 (DW), \u0430 \u0447\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u043e\u0437\u0435\u0440\u043e \u0434\u0430\u043d\u043d\u044b\u0445 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-55701","post","type-post","status-publish","format-standard","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=\"\u042d\u0442\u043e \u0441\u0442\u0430\u0442\u044c\u044f \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043c\u043e\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0438 \u043d\u0430 medium \u2014 Getting Started with Data Lake.\" \/>\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\/nuzhno-li-nam-ozero-dannyh-a-chto-delat-s-hranilishhem-dannyh\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.2.1\" \/>\n\t\t<meta property=\"og:locale\" content=\"ro_RO\" \/>\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\u041d\u0443\u0436\u043d\u043e \u043b\u0438 \u043d\u0430\u043c \u043e\u0437\u0435\u0440\u043e \u0434\u0430\u043d\u043d\u044b\u0445? 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