{"id":30043,"date":"2025-09-22T21:59:46","date_gmt":"2025-09-22T18:59:46","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=30043"},"modified":"2025-09-22T21:59:46","modified_gmt":"2025-09-22T18:59:46","slug":"buyuk-veri-big-data-analitigi-sistemlerinin-bir-referans-mimari-ile-basitlestirilmesi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/buyuk-veri-big-data-analitigi-sistemlerinin-bir-referans-mimari-ile-basitlestirilmesi\/","title":{"rendered":"B\u00fcy\u00fck Veri (Big Data) Analiti\u011fi Sistemlerinin Bir Referans Mimari ile Basitle\u015ftirilmesi"},"content":{"rendered":"<div class=\"ead-preview\"><div class=\"ead-document\" style=\"position: relative;width: 100%;height: 100%;border: none;min-height: 500px;\" data-pdf-src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/Buyuk-Veri-Big-Data-Analitigi-Sistemlerinin-Bir-Referans-Mimari-ile-Basitlestirilmesi.pdf\" data-viewer=\"browser\"><div class=\"ead-iframe-wrapper\"><iframe src=\"\/\/docs.google.com\/viewer?url=https%3A%2F%2Ffatihsoysal.com%2Fblog%2Fwp-content%2Fuploads%2F2025%2F09%2FBuyuk-Veri-Big-Data-Analitigi-Sistemlerinin-Bir-Referans-Mimari-ile-Basitlestirilmesi.pdf&amp;embedded=true&amp;hl=en\" title=\"Embedded Document\" class=\"ead-iframe\" style=\"width: 100%;height: 100%;border: none;min-height: 500px;visibility: hidden;\"><\/iframe><\/div>\t\t\t<div class=\"ead-document-loading\" style=\"width:100%;height:100%;position:absolute;left:0;top:0;z-index:10;\">\n\t\t\t\t<div class=\"ead-loading-wrap\">\n\t\t\t\t\t<div class=\"ead-loading-main\">\n\t\t\t\t\t\t<div class=\"ead-loading\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/plugins\/embed-any-document\/images\/loading.svg\" width=\"55\" height=\"55\" alt=\"Loader\">\n\t\t\t\t\t\t\t<span>Loading...<\/span>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div class=\"ead-loading-foot\">\n\t\t\t\t\t\t<div class=\"ead-loading-foot-title\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/plugins\/embed-any-document\/images\/EAD-logo.svg\" alt=\"EAD Logo\" width=\"36\" height=\"23\"\/>\n\t\t\t\t\t\t\t<span>Taking too long?<\/span>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<p>\n\t\t\t\t\t\t\t<div class=\"ead-document-btn ead-reload-btn\" role=\"button\">\n\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/plugins\/embed-any-document\/images\/reload.svg\" alt=\"Reload\" width=\"12\" height=\"12\"\/> Reload document\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<span>|<\/span>\n\t\t\t\t\t\t\t<a href=\"#\" class=\"ead-document-btn\" target=\"_blank\">\n\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/plugins\/embed-any-document\/images\/open.svg\" alt=\"Open\" width=\"12\" height=\"12\"\/> Open in new tab\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div><\/div>\n<p class=\"ds-markdown-paragraph\">\u0130nternet ve Nesnelerin \u0130nterneti (sensorler, ak\u0131ll\u0131 cihazlar vb.) gibi yayg\u0131n teknolojiler, veri toplama ve veri eri\u015filebilirli\u011finin boyutunu katlanarak art\u0131rm\u0131\u015ft\u0131r. Bu b\u00fcy\u00fck veri, yaln\u0131zca mevcut kurumsal analitik sistemlerin yap\u0131s\u0131n\u0131 zorlamakla kalmamakta, ayn\u0131 zamanda yeni bilgi ve rekabet avantaj\u0131 yaratmak i\u00e7in yeni f\u0131rsatlar sunmaktad\u0131r. \u0130\u015fletmeler, b\u00fcy\u00fck veri analiti\u011fi yeteneklerini uygulayarak ve i\u015fleterek bu f\u0131rsatlardan yararlanmaktad\u0131r. Facebook, LinkedIn, Twitter gibi sosyal a\u011f \u015firketleri ve Netflix gibi video ak\u0131\u015f \u015firketleri b\u00fcy\u00fck veri analiti\u011fi uygulam\u0131\u015f ve ard\u0131ndan ilgili literat\u00fcr\u00fc yay\u0131nlam\u0131\u015flard\u0131r. Ancak, bu kullan\u0131m senaryolar\u0131 basitle\u015ftirilmi\u015f ve tutarl\u0131 bir b\u00fcy\u00fck veri analiti\u011fi referans mimarisi sunmam\u0131\u015ft\u0131r ve halen s\u0131n\u0131rl\u0131 say\u0131da referans mimarisi bulunmaktad\u0131r. Bu makale, mevcut d\u00f6rt kullan\u0131m senaryosuna dayanarak bir referans mimarisi \u00f6nererek b\u00fcy\u00fck veri analiti\u011fini basitle\u015ftirmeyi ama\u00e7lamakta ve ard\u0131ndan bu mimariyi Amazon ve Google analitik hizmetleri ile do\u011frulamaktad\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\"><strong>Anahtar Kelimeler:<\/strong>\u00a0B\u00fcy\u00fck Veri, Analitik, Referans Mimarisi.<\/p>\n<h2>1 Giri\u015f<\/h2>\n<p class=\"ds-markdown-paragraph\">Bulut bili\u015fim ve Nesnelerin \u0130nterneti teknoljisinin (mobil, sens\u00f6rler vb. dijital veri toplama cihazlar\u0131) evrimi, veri toplama ve veri eri\u015filebilirli\u011finin \u00f6l\u00e7e\u011fini katlanarak art\u0131rm\u0131\u015ft\u0131r [1; 2]. Dijital teknoloji (sosyal a\u011f uygulamalar\u0131 vb.), insanlar\u0131n verilerle etkile\u015fimine olanak tan\u0131makta ve her zamankinden daha fazla veri payla\u015f\u0131lmaktad\u0131r [1]. Her sekt\u00f6rde, i\u015fletmeler m\u00fc\u015fterilerini daha iyi anlamak, \u00fcr\u00fcn ve hizmet farkl\u0131la\u015fmas\u0131 yaratmak ve ayn\u0131 zamanda maliyetleri d\u00fc\u015f\u00fcr\u00fcp verimlili\u011fi art\u0131rmak i\u00e7in veriyi i\u015f d\u00f6n\u00fc\u015f\u00fcm\u00fcn\u00fcn merkezine koymaya odaklanmaktad\u0131r [1; 2; 3]. Sonu\u00e7 olarak, veri b\u00fcy\u00fck veri ile birlikte temel itici g\u00fc\u00e7lerden biri haline gelmekte ve giderek karma\u015f\u0131kla\u015fan i\u015f analizleri ve analiti\u011fin merkezinde yer almaktad\u0131r [1]. B\u00f6ylece, veriye dayal\u0131 analitik i\u015fletmeler i\u00e7in ana ak\u0131m bir s\u00fcre\u00e7 haline gelmi\u015ftir [2]. [4] analiti\u011fi, &#8220;b\u00fcy\u00fck veriden analiz yapmak ve istihbarat elde etmek i\u00e7in kullan\u0131lan teknikler&#8221; olarak tan\u0131mlam\u0131\u015ft\u0131r. Biz analiti\u011fi, &#8220;b\u00fcy\u00fck verinin i\u00e7g\u00f6r\u00fc \u00fcretmek amac\u0131yla toplanmas\u0131, i\u015flenmesi, analizi (makine \u00f6\u011frenmesi modelleri, istatistikler vb.) ve g\u00f6rselle\u015ftirilmesi&#8221; olarak referans al\u0131yoruz.<\/p>\n<p class=\"ds-markdown-paragraph\">G\u00fcncel veri analiti\u011fi sistemleri b\u00fcy\u00fck veriyi yeterince i\u015fleyememekte, ayr\u0131ca b\u00fcy\u00fck veriyi i\u015fleyen mevcut sistemler esnek olmayan ve karma\u015f\u0131k platformlar, ara\u00e7lar ve bilgilerden olu\u015fmaktad\u0131r. Bunun \u00e7ok daha merkezi ancak esnek bir analitik altyap\u0131ya yeniden yap\u0131land\u0131r\u0131lmas\u0131 gerekmektedir [1]. Bunun nas\u0131l yap\u0131laca\u011f\u0131 sorunu, i\u015fletmelere ve ara\u015ft\u0131rma toplulu\u011funa bir meydan okuma sunmaktad\u0131r. Referans mimariler somut mimarilerin olu\u015fturulmas\u0131na yard\u0131mc\u0131 olabilir [19], ancak halen b\u00fcy\u00fck veri analiti\u011fi i\u00e7in yeterli say\u0131da ve tutarl\u0131 bir referans mimarisi bulunmamaktad\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\">Bu makalenin amac\u0131, b\u00fcy\u00fck veri analiti\u011fi sistemlerini basitle\u015ftirmenin ger\u00e7ekle\u015ftirilmesini ara\u015ft\u0131rmak ve ard\u0131ndan yeni bir \u00fcst d\u00fczey soyut referans mimarisi \u00f6nermektir. Sistemin mimari tasar\u0131m\u0131, temel al\u0131nan s\u00fcre\u00e7lerin, teknolojilerin ve hizmetlerin kullan\u0131m\u0131 gibi temel hususlar ve de\u011ferlendirmeler, bir b\u00fcy\u00fck veri analiti\u011fi sisteminin ger\u00e7ekle\u015ftirilmesi i\u00e7in \u00f6nemlidir [5]. Bu \u00e7al\u0131\u015fman\u0131n katk\u0131lar\u0131 \u015funlard\u0131r: a) Mevcut yay\u0131nlara dayal\u0131 olarak bir b\u00fcy\u00fck veri analiti\u011fi platformu tasarlamak i\u00e7in \u00fcst d\u00fczey ba\u011f\u0131ms\u0131z bir referans mimarisi sunmak, b) \u00d6nerilen referans mimarisini mevcut farkl\u0131 b\u00fcy\u00fck veri analiti\u011fi sistemleri ile do\u011frulamak.<\/p>\n<p class=\"ds-markdown-paragraph\">Makalenin yap\u0131s\u0131 \u015fu \u015fekildedir: \u0130lgili \u00e7al\u0131\u015fmalar ve literat\u00fcr incelemesi B\u00f6l\u00fcm 2&#8217;de sunulmu\u015ftur. Referans mimarisinin olu\u015fturulmas\u0131, b\u00fcy\u00fck veri kullan\u0131m senaryolar\u0131 ve LinkedIn b\u00fcy\u00fck veri kullan\u0131m senaryosunun referans mimarisiyle e\u015fle\u015ftirilmesi B\u00f6l\u00fcm 3&#8217;te sunulmu\u015ftur. Sayfa s\u0131n\u0131rlamas\u0131 nedeniyle di\u011fer kullan\u0131m senaryolar\u0131n\u0131n tamam\u0131 sunulmam\u0131\u015ft\u0131r. Son olarak, B\u00f6l\u00fcm 4&#8217;te k\u0131sa bir sonu\u00e7 verilmi\u015ftir.<\/p>\n<h2>2 \u0130lgili \u00c7al\u0131\u015fmalar<\/h2>\n<p class=\"ds-markdown-paragraph\">Bu b\u00f6l\u00fcmde, ge\u00e7mi\u015f yay\u0131nlara dayanarak mevcut b\u00fcy\u00fck veri analiti\u011fi kullan\u0131m senaryolar\u0131 ve ilgili referans mimarileri incelenmi\u015ftir.<\/p>\n<h3>B\u00fcy\u00fck Veri Analiti\u011fi Sistemi<\/h3>\n<p class=\"ds-markdown-paragraph\">Son birka\u00e7 y\u0131lda, Facebook, Twitter ve LinkedIn gibi sosyal a\u011f alan\u0131ndan Netflix gibi e\u011flence video ak\u0131\u015f \u015firketine kadar \u00e7e\u015fitli b\u00fcy\u00fck \u00f6l\u00e7ekli veriye dayal\u0131 analitik sistemler yay\u0131nlanm\u0131\u015ft\u0131r. Ayr\u0131ca, sa\u011fl\u0131k sekt\u00f6r\u00fc gibi di\u011fer end\u00fcstriler de b\u00fcy\u00fck veri analiti\u011fine olan b\u00fcy\u00fck ilgi ve f\u0131rsatlar\u0131n\u0131 tan\u0131m\u0131\u015f ve g\u00f6stermi\u015ftir. Facebook, LinkedIn ve Twitter, kullan\u0131c\u0131lar\u0131ndan ak\u0131\u015f verileri de dahil olmak \u00fczere veriler toplamaktad\u0131r [6; 9; 10; 11; 12]. Facebook ve LinkedIn genellikle web g\u00fcnl\u00fckleri gibi geleneksel verilerin yan\u0131 s\u0131ra ak\u0131\u015f verileriyle de ilgilenirken, Twitter \u00e7o\u011funlukla ak\u0131\u015f verileriyle ilgilenmektedir [6; 7; 8; 10; 11; 12]. Toplanan veriler daha sonra toplu veya ak\u0131\u015f i\u015fleme y\u00f6ntemleriyle kendi \u00f6zel i\u015flevsellikleriyle y\u00f6netilir. Derin Analitik, e\u011fitilmi\u015f modeller ve belirlenmi\u015f i\u015fler, Hadoop HDFS ile algoritma hizmeti gibi \u00e7e\u015fitli veri analizi ve toplula\u015ft\u0131rmalar\u0131, b\u00fcy\u00fck \u00f6l\u00e7\u00fcde k\u00fcmeleme ve da\u011f\u0131t\u0131k hesaplamalarda uygulanmaktad\u0131r. Ayr\u0131ca, Netflix kullan\u0131c\u0131 etkinliklerini toplamakta ve bunlar\u0131 \u00c7evrimi\u00e7i, Yak\u0131n \u00c7evrimi\u00e7i ve \u00c7evrimd\u0131\u015f\u0131 hesaplamalarda i\u015flemektedir [13; 14]. \u00c7evrimi\u00e7i veri analizi ile i\u015flenen \u00f6neriler kullan\u0131c\u0131lara sunulmaktad\u0131r.<\/p>\n<h3>B\u00fcy\u00fck Veri Analiti\u011fi Sistemleri i\u00e7in Referans Mimari<\/h3>\n<p class=\"ds-markdown-paragraph\">Hizmet odakl\u0131 mimari ve ilkeleri, kurumsal yaz\u0131l\u0131m alan\u0131nda yaz\u0131l\u0131m tasar\u0131m\u0131, mimarisi ve uygulamas\u0131n\u0131 kolayla\u015ft\u0131rmaktad\u0131r [16, 17]. Ayr\u0131ca, \u00e7e\u015fitli referans mimarileri yay\u0131nlanm\u0131\u015ft\u0131r. Gaister ve Avgeriou [18], ad\u0131m ad\u0131m bir s\u00fcrece dayal\u0131 bir yaz\u0131l\u0131m referans mimarisi \u00f6nermi\u015ftir. Alt\u0131 ad\u0131m i\u00e7erir; referans mimarisi i\u00e7in bir tip belirleme, tasar\u0131m stratejisi se\u00e7imi, verilerin ampirik olarak elde edilmesi, referans mimarisinin in\u015fas\u0131, de\u011fi\u015fkenli\u011fin sa\u011flanmas\u0131 ve de\u011ferlendirme. Angelov, Grefen ve Greefhorst [19] yaz\u0131l\u0131m referans mimarilerini sunmu\u015f ve bunun daha iyi mimari tasar\u0131m\u0131 kolayla\u015ft\u0131rd\u0131\u011f\u0131n\u0131 ve dolay\u0131s\u0131yla daha iyi ba\u015far\u0131ya yol a\u00e7aca\u011f\u0131n\u0131 iddia etmi\u015ftir. \u00c7ok boyutlu bir s\u0131n\u0131fland\u0131rma alan\u0131 ve be\u015f tip referans mimarisinden olu\u015fan bir yaz\u0131l\u0131m referans mimarileri i\u00e7in \u00e7er\u00e7eve sunmu\u015flard\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\">B\u00fcy\u00fck veri ba\u011flam\u0131nda s\u0131n\u0131rl\u0131 say\u0131da mimari \u00f6nerilmi\u015ftir. Paakkonen ve Pakkala [5], yay\u0131nlanm\u0131\u015f b\u00fcy\u00fck veri kullan\u0131m senaryolar\u0131 ve ili\u015fkili ticari \u00fcr\u00fcnlere dayal\u0131 olarak bir referans mimarisi ve teknolojilerin, \u00fcr\u00fcnlerin ve hizmetlerin s\u0131n\u0131fland\u0131rmas\u0131n\u0131 sunmu\u015ftur. [20] taraf\u0131ndan bir b\u00fcy\u00fck veri mimarisi \u00e7er\u00e7evesi i\u00e7in b\u00fcy\u00fck veri ya\u015fam d\u00f6ng\u00fcs\u00fc ve altyap\u0131s\u0131n\u0131n \u00fcst d\u00fczey bir tan\u0131m\u0131 sunulmu\u015ftur. Schmidt ve Mohring [21], bulut alan\u0131nda b\u00fcy\u00fck veri i\u015flem hatt\u0131 uygulamak i\u00e7in bir hizmet ve da\u011f\u0131t\u0131m modeli \u00f6nermi\u015ftir. Kurumsal veri trafi\u011findeki farkl\u0131 b\u00fcy\u00fcme modellerini desteklemek i\u00e7in SQL ve NewSQL veritabanlar\u0131n\u0131n entegrasyonu i\u00e7in referans mimarileri \u00f6nerilmi\u015ftir [22]. Ayr\u0131ca, bellek i\u00e7i depolama sistemi ve da\u011f\u0131t\u0131k g\u00f6rev y\u00fcr\u00fctme analizi i\u00e7in bir b\u00fcy\u00fck veri mimarisi \u00f6nerilmi\u015f ve do\u011frulanm\u0131\u015ft\u0131r [23]. B\u00fcy\u00fck veri y\u00f6netimini veri \u00fcretiminden ve anlamsal t\u00fcketimden ay\u0131ran ba\u015fka bir katmanl\u0131 mimari (SOLID) de \u00f6nerilmi\u015ftir [24]. Tahmine dayal\u0131 ve ger\u00e7ek zamanl\u0131 analitik i\u00e7in, yap\u0131land\u0131r\u0131lm\u0131\u015f tarihsel ve zamansal veriler i\u00e7in genelle\u015ftirilmi\u015f bir yaz\u0131l\u0131m mimarisi \u00f6nerilmi\u015ftir [25].<\/p>\n<p class=\"ds-markdown-paragraph\">Literat\u00fcr incelemesi, b\u00fcy\u00fck veri analiti\u011fi ba\u011flam\u0131nda s\u0131n\u0131rl\u0131 say\u0131da referans mimarisi oldu\u011funu ve somut veya tutarl\u0131 bir referans mimarisinin eksikli\u011fini g\u00f6stermektedir. Bu durum, b\u00fcy\u00fck veri analiti\u011fi i\u00e7in referans mimarisi konusunda daha fazla ara\u015ft\u0131rma ihtiyac\u0131n\u0131 ortaya koymaktad\u0131r.<\/p>\n<h2>3 B\u00fcy\u00fck Veri Analiti\u011fi i\u00e7in Referans Mimari<\/h2>\n<p class=\"ds-markdown-paragraph\">Referans mimarisi, somut mimarilerin olu\u015fturulmas\u0131na ve ayr\u0131ca bir b\u00fcy\u00fck veri analiti\u011fi sistemindeki tipik ilgili i\u015flevsellik ve veri ak\u0131\u015flar\u0131n\u0131n genel mimarisinin anla\u015f\u0131lmas\u0131na yard\u0131mc\u0131 olabilir [19]. Bir referans mimarisi ayn\u0131 zamanda mevcut b\u00fcy\u00fck veri sistemlerini analiz etmek, veri analizi s\u00fcre\u00e7lerinin ve teknolojilerinin s\u0131n\u0131fland\u0131r\u0131lmas\u0131n\u0131n temelini sa\u011flamak i\u00e7in de kullan\u0131\u015fl\u0131d\u0131r. S\u00fcre\u00e7leri, teknolojileri ve hizmetleri gruplara (bile\u015fenlere) ay\u0131rmak, sistem s\u00fcre\u00e7leri ve i\u015flevlerinin ger\u00e7ekle\u015ftirilmesi ile ilgili karar vermeyi daha da kolayla\u015ft\u0131r\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\">Tablo 1&#8217;de g\u00f6sterilen b\u00fcy\u00fck veri analiti\u011fi kullan\u0131m senaryolar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131nda, b\u00fcy\u00fck veri kullan\u0131m\u0131n\u0131n temel unsurlar\u0131n\u0131 be\u015f bile\u015fene ay\u0131rd\u0131k: Veri Kayna\u011f\u0131; Veri Entegrasyonu; Veri Analizi ve Toplula\u015ft\u0131rma; Aray\u00fcz ve G\u00f6rselle\u015ftirme; ve \u0130\u015f ve Model Belirtimi.<\/p>\n<table>\n<thead data-start=\"72\" data-end=\"230\">\n<tr data-start=\"72\" data-end=\"230\">\n<th data-start=\"72\" data-end=\"118\" data-col-size=\"sm\"><strong data-start=\"74\" data-end=\"117\">B\u00fcy\u00fck Veri Analiti\u011fi Kullan\u0131m Senaryosu<\/strong><\/th>\n<th data-start=\"118\" data-end=\"137\" data-col-size=\"md\"><strong data-start=\"120\" data-end=\"136\">Veri Kayna\u011f\u0131<\/strong><\/th>\n<th data-start=\"137\" data-end=\"161\" data-col-size=\"md\"><strong data-start=\"139\" data-end=\"160\">Veri Entegrasyonu<\/strong><\/th>\n<th data-start=\"161\" data-end=\"189\" data-col-size=\"md\"><strong data-start=\"163\" data-end=\"188\">Veri Analizi, Toplama<\/strong><\/th>\n<th data-start=\"189\" data-end=\"230\" data-col-size=\"md\"><strong data-start=\"191\" data-end=\"228\">Model, \u0130\u015f Aray\u00fcz\u00fc, G\u00f6rselle\u015ftirme<\/strong><\/th>\n<\/tr>\n<\/thead>\n<\/table>\n<div>\n<table>\n<tbody>\n<tr data-start=\"396\" data-end=\"626\">\n<td data-start=\"396\" data-end=\"411\" data-col-size=\"sm\"><strong data-start=\"398\" data-end=\"410\">Facebook<\/strong><\/td>\n<td data-start=\"411\" data-end=\"439\" data-col-size=\"md\">MySQL, Web Sunucu Loglar\u0131<\/td>\n<td data-start=\"439\" data-end=\"505\" data-col-size=\"md\">Veritaban\u0131 d\u00f6k\u00fcm\u00fc kopyas\u0131, Scribe, Hadoop ham veri, Replikasyon<\/td>\n<td data-start=\"505\" data-end=\"538\" data-col-size=\"md\">K\u00fcp, Derin Analiz, Hive i\u015fleri<\/td>\n<td data-start=\"538\" data-end=\"626\" data-col-size=\"md\">\u0130\u015fler veritaban\u0131nda saklan\u0131r, Microstrategy g\u00f6rselle\u015ftirme ve kullan\u0131c\u0131 uygulamalar\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div>\n<table>\n<tbody>\n<tr data-start=\"627\" data-end=\"892\">\n<td data-start=\"627\" data-end=\"642\" data-col-size=\"sm\"><strong data-start=\"629\" data-end=\"641\">LinkedIn<\/strong><\/td>\n<td data-start=\"642\" data-end=\"678\" data-col-size=\"md\">Oracle, Kullan\u0131c\u0131 Aktivite Verisi<\/td>\n<td data-start=\"678\" data-end=\"757\" data-col-size=\"md\">Oracle (Snapshot), Kafka, Hadoop, Veri temizleme, Tekille\u015ftirme, Replikasyon<\/td>\n<td data-start=\"757\" data-end=\"807\" data-col-size=\"md\">Derin Analiz, Hive, Pig, MR, Voldemort, Avatara<\/td>\n<td data-start=\"807\" data-end=\"892\" data-col-size=\"md\">Azkaban \u00c7er\u00e7evesi, G\u00f6rselle\u015ftirme uygulamalar\u0131 (kurumsal), LinkedIn kullan\u0131c\u0131lar\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div>\n<table>\n<tbody>\n<tr data-start=\"893\" data-end=\"1160\">\n<td data-start=\"893\" data-end=\"907\" data-col-size=\"sm\"><strong data-start=\"895\" data-end=\"906\">Twitter<\/strong><\/td>\n<td data-start=\"907\" data-end=\"948\" data-col-size=\"md\">Firehose (tweetler), Updater, Sorgular<\/td>\n<td data-start=\"948\" data-end=\"1047\" data-col-size=\"md\">Tokenizasyon, \u0130statistiksel sorgular, Firehose\u2019un filtrelenmesi ve ki\u015fiselle\u015ftirilmesi, S\u0131ralama<\/td>\n<td data-start=\"1047\" data-end=\"1113\" data-col-size=\"md\">Hadoop HDFS (s\u0131ralama algoritmas\u0131, analiz), \u00d6nbellek veritaban\u0131<\/td>\n<td data-start=\"1113\" data-end=\"1160\" data-col-size=\"md\">S\u0131ralama algoritmas\u0131, Twitter kullan\u0131c\u0131lar\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div>\n<table>\n<tbody>\n<tr data-start=\"1161\" data-end=\"1432\">\n<td data-start=\"1161\" data-end=\"1175\" data-col-size=\"sm\"><strong data-start=\"1163\" data-end=\"1174\">Netflix<\/strong><\/td>\n<td data-start=\"1175\" data-end=\"1224\" data-col-size=\"md\">Ak\u0131\u015f verisi (Kullan\u0131c\u0131lar ve \u00e7evrimi\u00e7i servis)<\/td>\n<td data-start=\"1224\" data-end=\"1271\" data-col-size=\"md\">Chukwa, Ak\u0131\u015f sinyalleri, Manhattan \u00c7er\u00e7evesi<\/td>\n<td data-start=\"1271\" data-end=\"1330\" data-col-size=\"md\">Derin Analiz: \u00c7evrimd\u0131\u015f\u0131\/\u00c7evrimi\u00e7i, Yak\u0131n ger\u00e7ek zamanl\u0131<\/td>\n<td data-start=\"1330\" data-end=\"1432\" data-col-size=\"md\">Algoritmalar, Makine \u00d6\u011frenmesi, Pig, Netflix kullan\u0131c\u0131lar\u0131, G\u00f6rselle\u015ftirme uygulamalar\u0131 (kurumsal)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<ol start=\"1\">\n<li>\n<p class=\"ds-markdown-paragraph\">a.\u00a0<em>Veri Kayna\u011f\u0131<\/em>: Toplanacak verilerin orijinal kayna\u011f\u0131n\u0131 ifade eder. Veriler ili\u015fkisel veri veya NoSQL gibi geleneksel veritabanlar\u0131nda saklanabilir. Veriler yap\u0131land\u0131r\u0131lm\u0131\u015f, yap\u0131land\u0131r\u0131lmam\u0131\u015f, yar\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f veya ak\u0131\u015f verisi olabilir.<\/p>\n<\/li>\n<li>\n<p class=\"ds-markdown-paragraph\">b.\u00a0<em>Veri Entegrasyonu<\/em>: Verinin Toplanmas\u0131, \u0130\u015flenmesi ve Y\u00fcklenmesini i\u00e7erir. Toplama, verileri (birden fazla) kaynaktan depolama veya analiz i\u00e7in alma i\u015flemidir. Anl\u0131k G\u00f6r\u00fcnt\u00fc gibi bunun i\u00e7in \u00e7e\u015fitli teknikler vard\u0131r. Veri \u0130\u015fleme, kaynak verilerin Y\u00fckleme a\u015famas\u0131na ge\u00e7meden \u00f6nce i\u015flenmesi i\u00e7in gereken y\u00fcr\u00fctmeleri kapsar. Veri \u0130\u015fleme, veri temizleme, \u00e7o\u011faltma, filtreleme, algoritma hizmeti gibi i\u015flevselli\u011fi temsil eder. Daha sonra Veri Y\u00fckleme y\u00fcr\u00fct\u00fclebilir, yani toplanan ve i\u015flenen veriler art\u0131k Hadoop HDFS gibi bir veri deposuna y\u00fcklenir. \u00dc\u00e7 \u00f6\u011feyi (Veri Toplama, \u0130\u015fleme ve Y\u00fckleme) bir bile\u015fende gruplad\u0131k \u00e7\u00fcnk\u00fc her bir \u00f6\u011fe birbirine yak\u0131ndan ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, veri toplama, verileri temizleme veya bi\u00e7imlendirme ve ayr\u0131ca verileri bir depoya y\u00fckleme i\u00e7in veri i\u015fleme gerektirir. \u00dcst d\u00fczey mimaride, bunun birbirine yak\u0131ndan ili\u015fkili \u00f6\u011feler aras\u0131ndaki mant\u0131ksal ba\u011flant\u0131y\u0131 ve ak\u0131\u015f\u0131 iyile\u015ftirdi\u011fine ve dolay\u0131s\u0131yla daha net ve etkili bir genel mimari sa\u011flad\u0131\u011f\u0131na inan\u0131yoruz.<\/p>\n<\/li>\n<li>\n<p class=\"ds-markdown-paragraph\">c.\u00a0<em>Veri Analizi ve Toplula\u015ft\u0131rma<\/em>: Veri analizi, ilgili veri analizi g\u00f6revlerini ve s\u00fcre\u00e7lerini ifade ederken, Toplula\u015ft\u0131rma, analiz sonu\u00e7lar\u0131n\u0131 saklayan veri depolamay\u0131 (\u00e7ok boyutlu dahil) ifade eder.<\/p>\n<\/li>\n<li>\n<p class=\"ds-markdown-paragraph\">d.\u00a0<em>Aray\u00fcz ve G\u00f6rselle\u015ftirme<\/em>: Son kullan\u0131c\u0131lar\u0131 (yani ge\u00e7ici analiz, sorgular vb.) ve ayr\u0131ca dashboard&#8217;lar gibi g\u00f6rselle\u015ftirme tabanl\u0131 uygulamalar\u0131 temsil eder.<\/p>\n<\/li>\n<li>\n<p class=\"ds-markdown-paragraph\">e.\u00a0<em>Model ve \u0130\u015f Belirtimi<\/em>: Bu, makine \u00f6\u011frenmesi, e\u011fitilmi\u015f modeller, i\u015flerin belirtimleri ve zamanlamas\u0131 ile bunlar\u0131n depolama alanlar\u0131n\u0131 kapsar.<\/p>\n<\/li>\n<\/ol>\n<p class=\"ds-markdown-paragraph\">Ayr\u0131ca, b\u00fcy\u00fck veri kullan\u0131mlar\u0131n\u0131 modellemek i\u00e7in kendi e\u015fleme notasyonlar\u0131m\u0131z\u0131 (veri ak\u0131\u015f\u0131, entegrasyon, i\u015fleme, analiz ve aray\u00fcze dayal\u0131) geli\u015ftirdik. Birincil niyetimiz, bir b\u00fcy\u00fck veri sistemini basitle\u015ftirmek ve genel mimarisini etkili bir \u015fekilde anlamakt\u0131. Ek olarak, b\u00fcy\u00fck veri analiti\u011fi literat\u00fcr ve kullan\u0131m senaryolar\u0131 \u00fczerine yapt\u0131\u011f\u0131m\u0131z \u00e7al\u0131\u015fma, e\u015fleme notasyonlar\u0131nda iyile\u015ftirme ihtiyac\u0131 oldu\u011funu ortaya \u00e7\u0131kard\u0131. B\u00f6ylece, Tablo 2&#8217;de sunulan kendi e\u015fleme notasyonlar\u0131m\u0131z\u0131 olu\u015fturduk ve ard\u0131ndan \u015eekil 1&#8217;de sunulan bir referans mimarisi \u00fcrettik.<\/p>\n<p class=\"ds-markdown-paragraph\">Notasyon 1, 2 ve 3 &#8216;veri kayna\u011f\u0131&#8217;, &#8216;veri entegrasyonu&#8217;, &#8216;veri analizi ve toplula\u015ft\u0131rma&#8217;, &#8216;i\u015f modeli belirtimi&#8217;nde bulunur. Notasyon 4 ve 5 &#8216;aray\u00fcz ve g\u00f6rselle\u015ftirme&#8217;de kullan\u0131l\u0131r. Notasyon 6 ve 7 &#8216;i\u015f ve model belirtimi&#8217;nde kullan\u0131l\u0131r. Ve Notasyon 8 ve 9, Notasyon 1-7&#8217;nin elemanlar\u0131 aras\u0131ndaki ba\u011flant\u0131lard\u0131r.<\/p>\n<h3>LinkedIn \u00d6rne\u011finin E\u015fle\u015ftirilmesi<\/h3>\n<p class=\"ds-markdown-paragraph\">LinkedIn&#8217;de, kullan\u0131c\u0131 etkinlik verileri toplan\u0131r ve veritaban\u0131 anl\u0131k g\u00f6r\u00fcnt\u00fcleri ile aktar\u0131l\u0131r. Etkinlik verileri, LinkedIn hizmetlerinin kullan\u0131m\u0131n\u0131 ifade eder ve ak\u0131\u015f etkinlikleri olarak ele al\u0131n\u0131r. Da\u011f\u0131t\u0131lm\u0131\u015f bir yay\u0131nlama\/abonelik mesajla\u015fma sistemi olan Kafka, ak\u0131\u015f etkinliklerinin toplanmas\u0131 i\u00e7in kullan\u0131l\u0131r [7].<\/p>\n<p class=\"ds-markdown-paragraph\">Kafka ile \u00fcreticiler, olaylar\u0131 bir brokere raporlar ve t\u00fcketiciler verileri kendi h\u0131zlar\u0131nda okur [7]. Kafka&#8217;n\u0131n etkinlik verileri, birle\u015ftirme, yineleme kald\u0131rma gibi daha fazla i\u015fleme g\u00f6revleri i\u00e7in Hadoop ETL k\u00fcmesine ta\u015f\u0131n\u0131r. Hadoop ETL k\u00fcmesindeki veriler, \u00fcretim ve geli\u015ftirme k\u00fcmelerine kopyalan\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\">\u0130\u015f y\u00fckleri tipik olarak \u015fu \u015fekilde ger\u00e7ekle\u015ftirilir; ilk deneyler Geli\u015ftirme k\u00fcmesinde y\u00fcr\u00fct\u00fcl\u00fcr ve ard\u0131ndan ba\u015far\u0131l\u0131 inceleme ve testin ard\u0131ndan \u00fcretim k\u00fcmesine aktar\u0131l\u0131r. \u00dcretim k\u00fcmesinden gelen analiz sonu\u00e7lar\u0131 hem \u00e7evrimd\u0131\u015f\u0131 (hata ay\u0131klama) hem de \u00e7evrimi\u00e7i veritaban\u0131nda saklan\u0131r. Ayr\u0131ca, bu sonu\u00e7lar Kafka k\u00fcmesinde tekrar kullan\u0131labilir [7].<\/p>\n<p class=\"ds-markdown-paragraph\">Azkaban, \u00e7e\u015fitli i\u015f t\u00fcrlerini desteklemesi nedeniyle i\u015f y\u00fcklerinin zamanlanmas\u0131 i\u00e7in kullan\u0131l\u0131r. Azkaban, Hadoop i\u015flerini y\u00fcr\u00fctmek i\u00e7in LinkedIn&#8217;de olu\u015fturulmu\u015f bir toplu i\u015f ak\u0131\u015f\u0131 zamanlay\u0131c\u0131s\u0131d\u0131r. Her Hadoop ortam\u0131nda bir Azkaban \u00f6rne\u011fi \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r. Zamanlanm\u0131\u015f Azkaban i\u015f y\u00fckleri MapReduce, Pig, kabuk beti\u011fi veya Hive i\u015fleri olabilir. LinkedIn&#8217;de olu\u015fturulan \u00f6l\u00e7eklenebilir ve h\u0131zl\u0131 bir OLAP sunum sistemi olan Avatara, verileri analiz etmek ve i\u015flemek i\u00e7in kullan\u0131l\u0131r ve ard\u0131ndan bu veriler Voldemort veritabanlar\u0131nda saklan\u0131r [8].<\/p>\n<p class=\"ds-markdown-paragraph\">LinkedIn b\u00fcy\u00fck veri analiti\u011fini, \u00fcretti\u011fimiz referans mimarisiyle e\u015fle\u015ftirdik ve bu e\u015fleme \u015eekil 2&#8217;de sunulmu\u015ftur.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-30044\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1.png\" alt=\"\" width=\"733\" height=\"430\" srcset=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1.png 733w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-300x176.png 300w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-680x399.png 680w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-500x293.png 500w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-400x235.png 400w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-200x117.png 200w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/1-50x29.png 50w\" sizes=\"(max-width: 733px) 100vw, 733px\" \/><\/p>\n<p class=\"ds-markdown-paragraph\" style=\"text-align: center;\"><strong>\u015eekil 1: B\u00fcy\u00fck Veri Analiti\u011fi Referans Mimarisi<\/strong><\/p>\n<p class=\"ds-markdown-paragraph\" style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-30045\" src=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2.png\" alt=\"\" width=\"734\" height=\"450\" srcset=\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2.png 734w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-300x184.png 300w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-680x417.png 680w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-500x307.png 500w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-400x245.png 400w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-200x123.png 200w, https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2025\/09\/2-50x31.png 50w\" sizes=\"(max-width: 734px) 100vw, 734px\" \/><br \/>\n<strong>\u015eekil 2: LinkedIn B\u00fcy\u00fck Veri Analiti\u011fi Referans Mimarisinin E\u015fle\u015ftirilmesi<\/strong><\/p>\n<h2>4 Sonu\u00e7<\/h2>\n<p class=\"ds-markdown-paragraph\">Amac\u0131m\u0131z b\u00fcy\u00fck veri analiti\u011fi sistemlerini basitle\u015ftirmekti, bu nedenle Facebook, LinkedIn, Twitter ve Netflix&#8217;teki b\u00fcy\u00fck veri kullan\u0131m senaryolar\u0131n\u0131 ara\u015ft\u0131rd\u0131k ve kar\u015f\u0131la\u015ft\u0131rd\u0131k ve ard\u0131ndan bir b\u00fcy\u00fck veri analiti\u011fi sistemini basitle\u015ftirmek ve genel mimarisini etkili bir \u015fekilde anlamak i\u00e7in kendi e\u015fleme notasyonlar\u0131m\u0131zla bir referans mimarisi olu\u015fturduk. Kar\u015f\u0131la\u015ft\u0131rma tablomuz Tablo 1&#8217;de bulunabilir ve referans mimarimiz \u015eekil 1&#8217;de sunulmu\u015ftur. Ayr\u0131ca, LinkedIn Kullan\u0131m Senaryosu Referans Mimarisini e\u015fle\u015ftirdik ve \u00fcrettik, bu da \u015eekil 2&#8217;de sunulmu\u015ftur. Sayfa s\u0131n\u0131rlamas\u0131 nedeniyle di\u011fer kullan\u0131m senaryolar\u0131 \u00fczerindeki \u00e7al\u0131\u015fmalar\u0131m\u0131z\u0131 sunmad\u0131k. \u00dcretti\u011fimiz e\u015flemelere dayanarak, referans mimarisinin bu geni\u015f yelpazedeki durumlar\u0131 kapsayabildi\u011fi ve tutarl\u0131 bir sonu\u00e7 \u00fcretti\u011fi a\u00e7\u0131kt\u0131r. Mimarinin end\u00fcstriyel uygulamas\u0131 ve de\u011ferlendirilmesi, ek anlay\u0131\u015f ve ge\u00e7erlilik sa\u011flayacakt\u0131r.<\/p>\n<p class=\"ds-markdown-paragraph\"><strong>Te\u015fekk\u00fcr.<\/strong>\u00a0Bu ara\u015ft\u0131rma k\u0131smen EU H2020 FIRST projesi, Hibe No. 734599 taraf\u0131ndan desteklenmi\u015ftir.<\/p>\n","protected":false},"excerpt":{"rendered":"\u0130nternet ve Nesnelerin \u0130nterneti (sensorler, ak\u0131ll\u0131 cihazlar vb.) gibi yayg\u0131n teknolojiler, veri toplama ve veri eri\u015filebilirli\u011finin boyutunu katlanarak&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1470],"tags":[1471,1393,1498],"class_list":{"0":"post-30043","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-akademik-ve-bilimsel","7":"tag-analitik","8":"tag-buyuk-veri","9":"tag-referans-mimarisi","10":"cs-entry","11":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>B\u00fcy\u00fck Veri (Big Data) Analiti\u011fi Sistemlerinin Bir Referans Mimari ile Basitle\u015ftirilmesi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\n<meta name=\"description\" content=\"\u0130nternet ve Nesnelerin \u0130nterneti (sensorler, ak\u0131ll\u0131 cihazlar vb.) gibi yayg\u0131n teknolojiler, veri toplama ve veri eri\u015filebilirli\u011finin boyutunu katlanarak art\u0131rm\u0131\u015ft\u0131r. 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