{"id":15078,"date":"2025-03-13T19:00:17","date_gmt":"2025-03-13T16:00:17","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/gercek-zamanli-analitik-icin-acik-kaynak-veritabanlari\/"},"modified":"2025-03-13T19:00:17","modified_gmt":"2025-03-13T16:00:17","slug":"gercek-zamanli-analitik-icin-acik-kaynak-veritabanlari","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/gercek-zamanli-analitik-icin-acik-kaynak-veritabanlari\/","title":{"rendered":"Ger\u00e7ek Zamanl\u0131 Analitik i\u00e7in A\u00e7\u0131k Kaynak Veritabanlar\u0131"},"content":{"rendered":"

<\/p>\n

Ger\u00e7ek Zamanl\u0131 Analitik i\u00e7in A\u00e7\u0131k Kaynak Veritabanlar\u0131<\/p>\n

G\u00fcn\u00fcm\u00fczde, i\u015fletmeler s\u00fcrekli artan miktarda veri \u00fcretiyor ve bu verilerden anl\u0131k kararlar almak i\u00e7in ger\u00e7ek zamanl\u0131 analiti\u011fe ihtiya\u00e7 duyuyor. Neyse ki, bu ihtiyac\u0131 kar\u015f\u0131lamak i\u00e7in bir\u00e7ok g\u00fc\u00e7l\u00fc ve a\u00e7\u0131k kaynakl\u0131 veritaban\u0131 mevcut. Bu makalede, ger\u00e7ek zamanl\u0131 analitik i\u00e7in en pop\u00fcler se\u00e7enekleri inceleyece\u011fiz, performanslar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131raca\u011f\u0131z ve size en uygun veritaban\u0131n\u0131 se\u00e7menizde yard\u0131mc\u0131 olaca\u011f\u0131z.<\/p>\n

Pop\u00fcler A\u00e7\u0131k Kaynak Veritabanlar\u0131<\/h2>\n

Ger\u00e7ek zamanl\u0131 analitik i\u00e7in pek \u00e7ok a\u00e7\u0131k kaynakl\u0131 veritaban\u0131 se\u00e7ene\u011fi bulunmaktad\u0131r. Bunlardan baz\u0131lar\u0131, \u00f6zellikle zaman serisi verileriyle y\u00fcksek performansl\u0131 analitik sunmalar\u0131yla \u00f6ne \u00e7\u0131kmaktad\u0131r. \u00d6rne\u011fin, TimescaleDB<\/strong>, PostgreSQL \u00fczerine kurulu, \u00f6l\u00e7eklenebilir ve g\u00fc\u00e7l\u00fc bir zaman serisi veritaban\u0131d\u0131r. Ayn\u0131 zamanda, InfluxDB<\/strong> da yayg\u0131n kullan\u0131lan bir ba\u015fka zaman serisi veritaban\u0131d\u0131r ve \u00f6zellikle IoT verileriyle \u00e7al\u0131\u015fmak i\u00e7in optimize edilmi\u015ftir. Bunlar\u0131n yan\u0131 s\u0131ra, Prometheus<\/strong>, genellikle izleme ve alarm sistemlerinde kullan\u0131lan bir zaman serisi veritaban\u0131d\u0131r. Son olarak, genel ama\u00e7l\u0131 ili\u015fkisel veritaban\u0131 olan PostgreSQL<\/strong>, uygun konfig\u00fcrasyonlarla ger\u00e7ek zamanl\u0131 analitik i\u00e7in kullan\u0131labilir, ancak \u00f6zelle\u015ftirilmi\u015f \u00e7\u00f6z\u00fcmler kadar h\u0131zl\u0131 olmayabilir.<\/p>\n

TimescaleDB: PostgreSQL \u00dczerine Kurulu G\u00fc\u00e7<\/h3>\n

TimescaleDB, PostgreSQL’in sa\u011flaml\u0131\u011f\u0131na ve \u00f6zelliklerine, zaman serisi verilerine y\u00f6nelik performans geli\u015ftirmeleri ekleyerek g\u00fcvenilir bir \u00e7\u00f6z\u00fcm sunuyor. Bu sayede, hem yap\u0131land\u0131r\u0131lm\u0131\u015f verilerinizi hem de zaman serisi verilerinizi ayn\u0131 veritaban\u0131 i\u00e7inde y\u00f6netebilirsiniz. Dahas\u0131, \u00f6l\u00e7eklenebilirlik olduk\u00e7a y\u00fcksektir; b\u00fcy\u00fck miktardaki veriyi sorunsuz bir \u015fekilde i\u015fleyebilir. Ba\u015fka bir deyi\u015fle, TimescaleDB, hem performans hem de kullan\u0131m kolayl\u0131\u011f\u0131 a\u00e7\u0131s\u0131ndan ideal bir se\u00e7enektir.<\/p>\n

InfluxDB: IoT Verileri \u0130\u00e7in Optimize Edilmi\u015f<\/h3>\n

InfluxDB, \u00f6zellikle IoT (Nesnelerin \u0130nterneti) verilerine odaklanm\u0131\u015ft\u0131r. Y\u00fcksek yazma performans\u0131 ve zaman serisi verilerinin h\u0131zl\u0131 sorgulamas\u0131yla bilinir. Ancak, InfluxDB’in di\u011fer veritabanlar\u0131na k\u0131yasla daha az SQL deste\u011fi sunmas\u0131 \u00f6nemlidir. Dolay\u0131s\u0131yla, veritaban\u0131n\u0131zla etkile\u015fim kurmak i\u00e7in farkl\u0131 bir yakla\u015f\u0131m benimsemeniz gerekebilir.<\/p>\n

Prometheus: \u0130zleme ve Alarm Sistemleri i\u00e7in<\/h3>\n

Prometheus, ba\u015fta izleme ve alarm sistemleri i\u00e7in tasarlanm\u0131\u015ft\u0131r. Y\u00fcksek performansl\u0131 ve \u00f6l\u00e7eklenebilir bir zaman serisi veritaban\u0131d\u0131r. Ancak, genel ama\u00e7l\u0131 bir veritaban\u0131 olarak kullan\u0131lmas\u0131 i\u00e7in pek uygun de\u011fildir. K\u0131sacas\u0131, izleme ihtiya\u00e7lar\u0131n\u0131z i\u00e7in m\u00fckemmel bir \u00e7\u00f6z\u00fcm sunar. <\/p>\n

PostgreSQL: \u00c7ok Y\u00f6nl\u00fc, Ancak \u00d6zelle\u015ftirme Gerektiriyor<\/h3>\n

PostgreSQL, genel ama\u00e7l\u0131 bir ili\u015fkisel veritaban\u0131d\u0131r ve do\u011fru yap\u0131land\u0131rma ile ger\u00e7ek zamanl\u0131 analitik i\u00e7in kullan\u0131labilir. Ancak, TimescaleDB veya InfluxDB gibi \u00f6zelle\u015ftirilmi\u015f \u00e7\u00f6z\u00fcmler kadar performansl\u0131 olmayabilir. Bu nedenle, ger\u00e7ek zamanl\u0131 analitik i\u00e7in \u00f6ncelikli se\u00e7ene\u011finiz olmamal\u0131d\u0131r.<\/p>\n

Veritaban\u0131 Se\u00e7imi Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n

En uygun veritaban\u0131n\u0131 se\u00e7erken, birka\u00e7 fakt\u00f6r\u00fc g\u00f6z \u00f6n\u00fcnde bulundurman\u0131z \u00f6nemlidir. Bunlar aras\u0131nda, veri miktar\u0131n\u0131z, sorgu gereksinimleri, teknik uzmanl\u0131\u011f\u0131n\u0131z ve b\u00fct\u00e7eniz yer al\u0131r. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck miktarlarda veriyle \u00e7al\u0131\u015f\u0131yorsan\u0131z ve SQL ile rahatsan\u0131z, PostgreSQL yeterli olabilir. Ancak, b\u00fcy\u00fck miktarda zaman serisi verisiyle \u00e7al\u0131\u015f\u0131yorsan\u0131z, TimescaleDB veya InfluxDB daha iyi bir se\u00e7im olabilir. Her veritaban\u0131n\u0131n g\u00fc\u00e7l\u00fc ve zay\u0131f y\u00f6nleri farkl\u0131 oldu\u011fundan, ihtiya\u00e7lar\u0131n\u0131za en uygun olan\u0131 dikkatlice de\u011ferlendirmelisiniz. Daha fazla bilgi i\u00e7in fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz.<\/p>\n

Sonu\u00e7 olarak, ger\u00e7ek zamanl\u0131 analitik i\u00e7in bir\u00e7ok a\u00e7\u0131k kaynakl\u0131 veritaban\u0131 se\u00e7ene\u011fi bulunmaktad\u0131r. Do\u011fru veritaban\u0131n\u0131 se\u00e7mek, i\u015finizin ba\u015far\u0131s\u0131 i\u00e7in \u00e7ok \u00f6nemlidir. Bu makaledeki bilgiler, size do\u011fru karar\u0131 vermenizde yard\u0131mc\u0131 olacakt\u0131r. Daha detayl\u0131 ara\u015ft\u0131rma yapmak i\u00e7in, her bir veritaban\u0131n\u0131n resmi dok\u00fcmanlar\u0131na da g\u00f6z atabilirsiniz.<\/p>\n

Ba\u015fka bir \u00f6nemli nokta ise, veritaban\u0131 se\u00e7iminden sonra, verinizi verimli bir \u015fekilde y\u00f6netmek ve sorgulamak i\u00e7in do\u011fru ara\u00e7lar\u0131 se\u00e7meniz ve bu ara\u00e7lar\u0131 kullanabilmenizdir. Bu da, ba\u015far\u0131l\u0131 bir ger\u00e7ek zamanl\u0131 analitik altyap\u0131s\u0131n\u0131n kurulmas\u0131 i\u00e7in olduk\u00e7a \u00f6nemlidir.<\/p>\n

\u0130ngilizce Kaynak<\/a><\/p>\n

Anahtar Kelimeler:<\/h3>\n

#Etiketler: Ger\u00e7ek Zamanl\u0131 Analitik, A\u00e7\u0131k Kaynak Veritabanlar\u0131, Zaman Serisi Veritaban\u0131, TimescaleDB, InfluxDB, Prometheus, PostgreSQL, Veritaban\u0131 Se\u00e7imi, Performans Kar\u015f\u0131la\u015ft\u0131rmas\u0131, IoT, SQL<\/p>\n

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