{"id":40711,"date":"2026-02-28T18:40:55","date_gmt":"2026-02-28T15:40:55","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=40711"},"modified":"2026-02-28T18:40:55","modified_gmt":"2026-02-28T15:40:55","slug":"mevcut-sistemleri-kafka-connect-ile-entegre-etme-rehberi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/mevcut-sistemleri-kafka-connect-ile-entegre-etme-rehberi\/","title":{"rendered":"Mevcut Sistemleri Kafka Connect ile Entegre Etme Rehberi"},"content":{"rendered":"

<\/p>\n

Mevcut Sistemleri Kafka Connect ile Entegre Etme Rehberi<\/h2>\n

Giri\u015f<\/h3>\n

G\u00fcn\u00fcm\u00fcz\u00fcn h\u0131zla de\u011fi\u015fen dijital d\u00fcnyas\u0131nda, i\u015fletmelerin kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en b\u00fcy\u00fck zorluklardan biri, farkl\u0131 teknolojilerle geli\u015ftirilmi\u015f, \u00e7e\u015fitli veri formatlar\u0131nda bilgi \u00fcreten ve t\u00fcketen mevcut sistemleri birbirine entegre etmektir. Veritabanlar\u0131, eski uygulamalar, SaaS \u00e7\u00f6z\u00fcmleri, dosya sistemleri ve API’ler aras\u0131nda kesintisiz bir veri ak\u0131\u015f\u0131 sa\u011flamak, i\u015f zekas\u0131, ger\u00e7ek zamanl\u0131 analitik ve operasyonel verimlilik i\u00e7in kritik \u00f6neme sahiptir. Geleneksel entegrasyon y\u00f6ntemleri genellikle karma\u015f\u0131k, bak\u0131m\u0131 zor ve \u00f6l\u00e7eklenemeyen \u00f6zel kod \u00e7\u00f6z\u00fcmlerine veya pahal\u0131 ETL ara\u00e7lar\u0131na dayan\u0131r. Ancak, Apache Kafka’n\u0131n veri ak\u0131\u015f\u0131 platformu olarak y\u00fckseli\u015fiyle birlikte, bu entegrasyon paradigmas\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde de\u011fi\u015fmi\u015ftir.<\/p>\n

Apache Kafka, y\u00fcksek hacimli, d\u00fc\u015f\u00fck gecikmeli veri ak\u0131\u015flar\u0131n\u0131 y\u00f6netmek i\u00e7in g\u00fc\u00e7l\u00fc bir omurga sunarken, mevcut sistemlerle ba\u011flant\u0131 kurma ihtiyac\u0131 devam etmektedir. \u0130\u015fte tam bu noktada Kafka Connect devreye girer. Kafka Connect, Kafka ile harici sistemler aras\u0131nda \u00f6l\u00e7eklenebilir ve g\u00fcvenilir veri ak\u0131\u015flar\u0131 olu\u015fturmak i\u00e7in tasarlanm\u0131\u015f a\u00e7\u0131k kaynakl\u0131 bir \u00e7er\u00e7evedir. Bu makale, mevcut sistemlerinizi Kafka Connect kullanarak nas\u0131l entegre edece\u011finizi ad\u0131m ad\u0131m a\u00e7\u0131klayacak, temel bile\u015fenlerini, en iyi uygulamalar\u0131n\u0131 ve yayg\u0131n kullan\u0131m senaryolar\u0131n\u0131 detayland\u0131racakt\u0131r. Amac\u0131m\u0131z, i\u015fletmelerin veri entegrasyon s\u00fcre\u00e7lerini basitle\u015ftirmelerine, otomatikle\u015ftirmelerine ve modern veri mimarilerine ge\u00e7i\u015f yapmalar\u0131na yard\u0131mc\u0131 olmakt\u0131r.<\/p>\n

Kafka Connect Nedir?<\/h3>\n

Kafka Connect, Apache Kafka ekosisteminin \u00f6nemli bir par\u00e7as\u0131d\u0131r ve Kafka ile di\u011fer veri sistemleri aras\u0131nda veri aktar\u0131m\u0131n\u0131 basitle\u015ftirmek i\u00e7in tasarlanm\u0131\u015f bir \u00e7er\u00e7evedir. Temel olarak, Kafka Connect, \u00e7e\u015fitli kaynaklardan (veritabanlar\u0131, dosya sistemleri, mesaj kuyruklar\u0131, API’ler vb.) veri okumak ve Kafka’ya yazmak (Source Connectors) veya Kafka’dan veri okumak ve harici sistemlere yazmak (Sink Connectors) i\u00e7in haz\u0131r, yeniden kullan\u0131labilir konnekt\u00f6rler sunar.<\/p>\n

Geleneksel entegrasyon y\u00f6ntemleri genellikle her entegrasyon noktas\u0131 i\u00e7in \u00f6zel kod yazmay\u0131 veya karma\u015f\u0131k ETL (Extract, Transform, Load) ara\u00e7lar\u0131n\u0131 kullanmay\u0131 gerektirir. Bu yakla\u015f\u0131mlar genellikle bak\u0131m a\u00e7\u0131s\u0131ndan zor, hataya a\u00e7\u0131k ve \u00f6l\u00e7eklenebilirlik sorunlar\u0131 ya\u015fatabilir. Kafka Connect ise bu sorunlar\u0131, standartla\u015ft\u0131r\u0131lm\u0131\u015f, da\u011f\u0131t\u0131k ve hata toleransl\u0131 bir yakla\u015f\u0131m sunarak \u00e7\u00f6zer.<\/p>\n

Kafka Connect’in ba\u015fl\u0131ca avantajlar\u0131 \u015funlard\u0131r:<\/p>\n

* \u00d6l\u00e7eklenebilirlik:<\/strong> Da\u011f\u0131t\u0131k bir mimariye sahiptir ve \u00e7ok say\u0131da konnekt\u00f6r ve g\u00f6revi paralel olarak \u00e7al\u0131\u015ft\u0131rabilir. \u0130\u015f y\u00fck\u00fc artt\u0131\u011f\u0131nda, yeni worker’lar eklenerek kolayca \u00f6l\u00e7eklenebilir.
\n* Hata Tolerans\u0131:<\/strong> Worker’lardan biri \u00e7\u00f6kerse, g\u00f6revleri otomatik olarak di\u011fer \u00e7al\u0131\u015fan worker’lara atan\u0131r ve veri kayb\u0131 olmadan i\u015flem devam eder.
\n* Haz\u0131r Konnekt\u00f6rler:<\/strong> Confluent Hub ve a\u00e7\u0131k kaynak toplulu\u011fu taraf\u0131ndan geli\u015ftirilen zengin bir konnekt\u00f6r ekosistemi sayesinde, \u00e7o\u011fu pop\u00fcler veri sistemi i\u00e7in haz\u0131r konnekt\u00f6rler bulunur. Bu, geli\u015ftirme s\u00fcresini ve maliyetini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.
\n* API Odakl\u0131 Y\u00f6netim:<\/strong> Konnekt\u00f6rler, REST API \u00fczerinden kolayca yap\u0131land\u0131r\u0131labilir, ba\u015flat\u0131labilir, durdurulabilir ve izlenebilir.
\n* Veri D\u00f6n\u00fc\u015f\u00fcm\u00fc Yetenekleri (SMTs):<\/strong> Single Message Transforms (SMTs) ad\u0131 verilen hafif d\u00f6n\u00fc\u015f\u00fcm yetenekleri sayesinde, veriler Kafka’ya girmeden veya Kafka’dan \u00e7\u0131kmadan \u00f6nce basit manip\u00fclasyonlara tabi tutulabilir.
\n* Schema Y\u00f6netimi:<\/strong> Schema Registry ile entegre \u00e7al\u0131\u015farak veri \u015femalar\u0131n\u0131 y\u00f6netebilir ve veri tutarl\u0131l\u0131\u011f\u0131n\u0131 sa\u011flayabilir.<\/p>\n

K\u0131sacas\u0131, Kafka Connect, veri entegrasyonunu bir “tak ve \u00e7al\u0131\u015ft\u0131r” deneyimine d\u00f6n\u00fc\u015ft\u00fcrerek, geli\u015ftiricilerin entegrasyon mant\u0131\u011f\u0131 yazmak yerine i\u015f de\u011feri yaratmaya odaklanmas\u0131n\u0131 sa\u011flar.<\/p>\n

Kafka Connect’in Temel Bile\u015fenleri<\/h3>\n

Kafka Connect \u00e7er\u00e7evesi, veri ak\u0131\u015flar\u0131n\u0131 y\u00f6netmek i\u00e7in bir dizi temel bile\u015fenden olu\u015fur. Bu bile\u015fenleri anlamak, entegrasyon \u00e7\u00f6z\u00fcmleri tasarlarken ve uygularken kritik \u00f6neme sahiptir.<\/p>\n

Konnekt\u00f6rler (Connectors)<\/h4>\n

Konnekt\u00f6rler, Kafka Connect’in kalbidir. Bunlar, belirli bir veri sistemini Kafka’ya veya Kafka’dan ba\u011flamak i\u00e7in kullan\u0131lan y\u00fcksek seviyeli soyutlamalard\u0131r. \u0130ki ana t\u00fcr\u00fc vard\u0131r:<\/p>\n

* Source Konnekt\u00f6rler:<\/strong> Harici bir veri kayna\u011f\u0131ndan (\u00f6rne\u011fin, bir veritaban\u0131, dosya sistemi, mesaj kuyru\u011fu veya API) veri okur ve bu veriyi Kafka topic’lerine yazar. \u00d6rne\u011fin, bir JDBC Source Konnekt\u00f6r, bir ili\u015fkisel veritaban\u0131ndaki tablolar\u0131 izleyebilir ve de\u011fi\u015fiklikleri Kafka’ya aktarabilir.
\n* Sink Konnekt\u00f6rler:<\/strong> Kafka topic’lerinden veri okur ve bu veriyi harici bir veri sistemine (\u00f6rne\u011fin, bir ba\u015fka veritaban\u0131, veri ambar\u0131, arama motoru veya dosya sistemi) yazar. \u00d6rne\u011fin, bir HDFS Sink Konnekt\u00f6r, Kafka’dan gelen veriyi Hadoop Distributed File System (HDFS) \u00fczerine depolayabilir.<\/p>\n

G\u00f6revler (Tasks)<\/h4>\n

Her konnekt\u00f6r, i\u015fini bir veya daha fazla “g\u00f6rev”e (tasks) b\u00f6ler. G\u00f6revler, ger\u00e7ek veri aktar\u0131m\u0131n\u0131 ger\u00e7ekle\u015ftiren birimlerdir. Bir konnekt\u00f6r, yap\u0131land\u0131rmas\u0131na ve mevcut worker say\u0131s\u0131na g\u00f6re birden fazla g\u00f6rev olu\u015fturabilir. Bu, veri aktar\u0131m\u0131n\u0131n paralel olarak yap\u0131lmas\u0131n\u0131 sa\u011flar ve performans\u0131 art\u0131r\u0131r. \u00d6rne\u011fin, bir JDBC Source Konnekt\u00f6r, birden fazla tabloyu izlemek i\u00e7in ayr\u0131 g\u00f6revler atayabilir veya tek bir b\u00fcy\u00fck tabloyu birden fazla par\u00e7aya b\u00f6lerek paralel okuma yapabilir. G\u00f6revler, hata toleransl\u0131d\u0131r; bir g\u00f6rev ba\u015far\u0131s\u0131z olursa, Kafka Connect \u00e7er\u00e7evesi onu otomatik olarak yeniden ba\u015flat\u0131r veya ba\u015fka bir worker’a ta\u015f\u0131r.<\/p>\n

Worker’lar<\/h4>\n

Worker’lar, Kafka Connect’in \u00e7al\u0131\u015ft\u0131\u011f\u0131 JVM s\u00fcre\u00e7leridir. G\u00f6revleri bar\u0131nd\u0131r\u0131r ve y\u00fcr\u00fct\u00fcrler. \u0130ki modda \u00e7al\u0131\u015fabilirler:<\/p>\n

* Standalone Mod:<\/strong> Tek bir JVM s\u00fcrecinde \u00e7al\u0131\u015f\u0131r ve genellikle geli\u015ftirme veya test ortamlar\u0131 i\u00e7in kullan\u0131l\u0131r. \u00d6l\u00e7eklenebilirlik veya hata tolerans\u0131 sa\u011flamaz.
\n* Distributed Mod:<\/strong> \u00dcretim ortamlar\u0131 i\u00e7in tasarlanm\u0131\u015ft\u0131r. Birden fazla worker, bir k\u00fcme halinde birlikte \u00e7al\u0131\u015f\u0131r. Bu mod, g\u00f6revlerin worker’lar aras\u0131nda otomatik olarak da\u011f\u0131t\u0131lmas\u0131n\u0131, y\u00fck dengelemesini ve hata durumunda g\u00f6revlerin yeniden atanmas\u0131n\u0131 sa\u011flar. Worker’lar, Kafka topic’lerini kullanarak durumlar\u0131n\u0131 (offset’ler, konnekt\u00f6r yap\u0131land\u0131rmalar\u0131 vb.) payla\u015f\u0131r ve koordinasyon sa\u011flar.<\/p>\n

D\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler (Converters)<\/h4>\n

D\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler, Kafka Connect’in veri formatlar\u0131n\u0131 y\u00f6netmesine yard\u0131mc\u0131 olan bile\u015fenlerdir. Harici sistemlerden okunan veriyi Kafka’ya yazmadan \u00f6nce byte dizisine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr (serile\u015ftirir) ve Kafka’dan okunan byte dizilerini harici sisteme yazmadan \u00f6nce orijinal format\u0131na geri d\u00f6n\u00fc\u015ft\u00fcr\u00fcr (deserilele\u015ftirir). Yayg\u0131n d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler \u015funlard\u0131r:<\/p>\n

* JSON Converter:<\/strong> Veriyi JSON format\u0131nda serile\u015ftirir\/deserile\u015ftirir. JsonConverter<\/code> ve JsonSchemaConverter<\/code> (Schema Registry ile birlikte) olmak \u00fczere iki \u00e7e\u015fidi vard\u0131r.
\n* Avro Converter:<\/strong> Schema Registry ile birlikte Avro format\u0131n\u0131 kullanarak veriyi serile\u015ftirir\/deserile\u015ftirir. Bu, veri \u015femas\u0131 y\u00f6netimi ve evrimi i\u00e7in g\u00fc\u00e7l\u00fc bir \u00e7\u00f6z\u00fcm sunar.
\n* String Converter:<\/strong> Veriyi basit bir string olarak serile\u015ftirir\/deserile\u015ftirir.
\n* ByteArray Converter:<\/strong> Veriyi oldu\u011fu gibi byte dizisi olarak b\u0131rak\u0131r.<\/p>\n

Tek Mesaj D\u00f6n\u00fc\u015f\u00fcmleri (Single Message Transforms – SMTs)<\/h4>\n

SMT’ler, verinin Kafka’ya girmeden veya Kafka’dan \u00e7\u0131kmadan \u00f6nce tek tek mesajlar \u00fczerinde hafif d\u00f6n\u00fc\u015f\u00fcmler yapmaya olanak tan\u0131r. Bunlar, veriye basit d\u00fczenlemeler (alanlar\u0131 yeniden adland\u0131rma, filtreleme, alanlar\u0131 \u00e7\u0131karma, tarih format\u0131n\u0131 de\u011fi\u015ftirme vb.) uygulamak i\u00e7in idealdir. SMT’ler, ayr\u0131 bir ak\u0131\u015f i\u015fleme motoruna (\u00f6rne\u011fin, Kafka Streams veya ksqlDB) ihtiya\u00e7 duymadan, konnekt\u00f6r seviyesinde h\u0131zl\u0131 ve kolay veri manip\u00fclasyonu sa\u011flar. \u00d6rne\u011fin, bir Source Konnekt\u00f6rden gelen mesajdaki gereksiz bir alan\u0131 kald\u0131rmak veya bir zaman damgas\u0131 alan\u0131n\u0131n format\u0131n\u0131 de\u011fi\u015ftirmek i\u00e7in SMT’ler kullan\u0131labilir.<\/p>\n

Bu bile\u015fenler, Kafka Connect’i g\u00fc\u00e7l\u00fc, esnek ve g\u00fcvenilir bir entegrasyon arac\u0131 haline getirir.<\/p>\n

Entegrasyon S\u00fcreci: Ad\u0131m Ad\u0131m Yakla\u015f\u0131m<\/h3>\n

Mevcut sistemleri Kafka Connect ile entegre etmek, iyi planlanm\u0131\u015f ve sistematik bir yakla\u015f\u0131m gerektirir. \u0130\u015fte bu s\u00fcrecin ad\u0131mlar\u0131:<\/p>\n

Ad\u0131m 1: \u0130htiya\u00e7 Analizi ve Planlama<\/h4>\n

Entegrasyon s\u00fcrecine ba\u015flamadan \u00f6nce kapsaml\u0131 bir ihtiya\u00e7 analizi yapmak \u00e7ok \u00f6nemlidir.<\/p>\n

* Hangi Sistemler Entegre Edilecek?<\/strong>
\n * Veritabanlar\u0131 (ili\u015fkisel, NoSQL)
\n * Dosya sistemleri (yerel, HDFS, S3)
\n * Mesaj kuyruklar\u0131 (JMS, MQ)
\n * SaaS uygulamalar\u0131 (Salesforce, ServiceNow vb.)
\n * \u00d6zel API’ler veya eski uygulamalar
\n* Veri Ak\u0131\u015f Y\u00f6n\u00fc:<\/strong> Veri, kaynaktan Kafka’ya m\u0131 (Source Konnekt\u00f6r) yoksa Kafka’dan hedefe mi (Sink Konnekt\u00f6r) akacak? Her iki y\u00f6n de gerekli mi?
\n* Veri Hacmi ve H\u0131z\u0131:<\/strong> Entegre edilecek sistemlerden ne kadar veri ak\u0131\u015f\u0131 bekleniyor? Ger\u00e7ek zamanl\u0131 m\u0131, toplu (batch) mu olacak? Bu, konnekt\u00f6r yap\u0131land\u0131rmas\u0131n\u0131 ve Kafka k\u00fcmesi boyutland\u0131rmas\u0131n\u0131 etkileyecektir.
\n* Veri Formatlar\u0131 ve D\u00f6n\u00fc\u015f\u00fcmleri:<\/strong> Kaynak sistemdeki veri format\u0131 nedir? Kafka’da hangi formatta depolanacak (JSON, Avro, String)? Hedef sistem hangi format\u0131 bekliyor? Herhangi bir karma\u015f\u0131k d\u00f6n\u00fc\u015f\u00fcm (SMT’ler yeterli mi, yoksa Kafka Streams\/ksqlDB gerekli mi?) gerekiyor mu?
\n* G\u00fcvenlik Gereksinimleri:<\/strong> Kaynak\/hedef sistemlere eri\u015fim i\u00e7in kimlik do\u011frulama ve yetkilendirme mekanizmalar\u0131 nelerdir? Kafka Connect ve Kafka aras\u0131ndaki ileti\u015fim nas\u0131l g\u00fcvence alt\u0131na al\u0131nacak (SSL\/TLS, SASL)?
\n* Mevcut Altyap\u0131 De\u011ferlendirmesi:<\/strong> Mevcut Kafka k\u00fcmesi yeterli mi? Kafka Connect worker’lar\u0131 i\u00e7in donan\u0131m veya sanal makine kaynaklar\u0131 mevcut mu? A\u011f yap\u0131land\u0131rmas\u0131 uygun mu?<\/p>\n

Ad\u0131m 2: Kafka Connect Kurulumu<\/h4>\n

Kafka Connect, genellikle da\u011f\u0131t\u0131k modda (distributed mode) kurulur ve \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r.<\/p>\n

* \u00d6nko\u015fullar:<\/strong>
\n * Java Development Kit (JDK) 8 veya \u00fczeri.
\n * \u00c7al\u0131\u015f\u0131r durumda bir Apache Kafka k\u00fcmesi (Kafka broker’lar\u0131 ve Zookeeper).
\n* Da\u011f\u0131t\u0131k Mod Kurulumu:<\/strong>
\n 1. Kafka Connect Paketini \u0130ndirme:<\/strong> Apache Kafka paketiyle birlikte gelir.
\n 2. Yap\u0131land\u0131rma Dosyas\u0131 Olu\u015fturma:<\/strong> config\/connect-distributed.properties<\/code> dosyas\u0131n\u0131 kopyalay\u0131n ve d\u00fczenleyin. \u00d6nemli ayarlar \u015funlard\u0131r:
\n * bootstrap.servers<\/code>: Kafka broker’lar\u0131n\u0131z\u0131n listesi.
\n * group.id<\/code>: Kafka Connect k\u00fcmenizin benzersiz ad\u0131.
\n * key.converter<\/code>, value.converter<\/code>: Veri serile\u015ftirme\/deserile\u015ftirme i\u00e7in kullan\u0131lacak d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler (\u00f6rne\u011fin, org.apache.kafka.connect.json.JsonConverter<\/code> veya io.confluent.connect.avro.AvroConverter<\/code>).
\n * offset.storage.topic<\/code>, config.storage.topic<\/code>, status.storage.topic<\/code>: Kafka Connect’in durumunu, yap\u0131land\u0131rmas\u0131n\u0131 ve offset’lerini depolamak i\u00e7in kullanaca\u011f\u0131 topic’ler. Bu topic’lerin replikasyon fakt\u00f6r\u00fc y\u00fcksek olmal\u0131 ve yeterli b\u00f6l\u00fcm say\u0131s\u0131 olmal\u0131d\u0131r.
\n * plugin.path<\/code>: Konnekt\u00f6r JAR dosyalar\u0131n\u0131n bulundu\u011fu dizinlerin virg\u00fclle ayr\u0131lm\u0131\u015f listesi. Bu, Kafka Connect’in yeni konnekt\u00f6rleri bulmas\u0131n\u0131 sa\u011flar.
\n 3. Ba\u015flatma:<\/strong> Kafka Connect worker’lar\u0131n\u0131 a\u015fa\u011f\u0131daki komutla ba\u015flat\u0131n:<\/p>\n

bin\/connect-distributed.sh config\/connect-distributed.properties<\/code><\/pre>\n
        Birden fazla worker ba\u015flatarak bir k\u00fcme olu\u015fturabilirsiniz.<\/p>\n
Ad\u0131m 3: Uygun Konnekt\u00f6r\u00fcn Se\u00e7ilmesi<\/h4>\n
Do\u011fru konnekt\u00f6r\u00fc se\u00e7mek, ba\u015far\u0131l\u0131 bir entegrasyonun temelidir.<\/p>\n
*   Confluent Hub ve A\u00e7\u0131k Kaynak Konnekt\u00f6rler:<\/strong> Confluent Hub (https:\/\/www.confluent.io\/hub\/) ve GitHub gibi platformlar, \u00e7o\u011fu pop\u00fcler veri sistemi i\u00e7in haz\u0131r konnekt\u00f6rler sunar.
\n*   Yayg\u0131n Konnekt\u00f6r T\u00fcrleri:<\/strong>
\n    *   Veritabanlar\u0131 i\u00e7in:<\/strong>
\n        *   JDBC Source\/Sink Konnekt\u00f6r:<\/strong> \u0130li\u015fkisel veritabanlar\u0131 (PostgreSQL, MySQL, Oracle, SQL Server vb.) ile genel entegrasyon i\u00e7in. Source, tablolar\u0131 sorgulayarak veya de\u011fi\u015fim g\u00fcnl\u00fcklerini izleyerek veri al\u0131r. Sink, Kafka’dan gelen veriyi tablolara yazar.
\n        *   Debezium Konnekt\u00f6rleri (Source):<\/strong> Ger\u00e7ek zamanl\u0131 CDC (Change Data Capture) i\u00e7in tasarlanm\u0131\u015ft\u0131r. Veritaban\u0131n\u0131n i\u015flem g\u00fcnl\u00fcklerini (transaction logs) okuyarak t\u00fcm de\u011fi\u015fiklikleri (insert, update, delete) yakalar ve Kafka’ya aktar\u0131r. Y\u00fcksek veri b\u00fct\u00fcnl\u00fc\u011f\u00fc ve d\u00fc\u015f\u00fck gecikme sunar.
\n    *   Dosya Sistemleri i\u00e7in:<\/strong>
\n        *   File Stream Source\/Sink Konnekt\u00f6r:<\/strong> Yerel dosya sistemindeki dosyalardan okuma veya dosyalara yazma. Genellikle test veya basit log toplama senaryolar\u0131 i\u00e7in kullan\u0131l\u0131r.
\n        *   HDFS Sink Konnekt\u00f6r, S3 Sink Konnekt\u00f6r:<\/strong> Kafka’dan gelen veriyi Hadoop Distributed File System (HDFS) veya Amazon S3 gibi bulut depolama alanlar\u0131na yazmak i\u00e7in.
\n    *   Mesaj Kuyruklar\u0131 i\u00e7in:<\/strong> JMS Source\/Sink, MQ Source\/Sink.
\n    *   Arama Motorlar\u0131 i\u00e7in:<\/strong> Elasticsearch Sink Konnekt\u00f6r.
\n    *   Veri Ambarlar\u0131\/G\u00f6ller i\u00e7in:<\/strong> Snowflake Sink, BigQuery Sink, Delta Lake Sink.
\n*   Konnekt\u00f6r De\u011ferlendirme Kriterleri:<\/strong>
\n    *   Kararl\u0131l\u0131k ve Olgunluk:<\/strong> \u00dcretim ortam\u0131nda kullan\u0131labilir mi?
\n    *   Topluluk Deste\u011fi:<\/strong> Aktif bir toplulu\u011fu var m\u0131? G\u00fcncellemeler d\u00fczenli mi?
\n    *   Performans ve \u00d6l\u00e7eklenebilirlik:<\/strong> Y\u00fcksek veri hacimlerini i\u015fleyebilir mi?
\n    *   \u00d6zellikler:<\/strong> \u0130htiya\u00e7 duyulan t\u00fcm \u00f6zellikleri (\u00f6rne\u011fin, CDC, idempotent yazma, \u015fema y\u00f6netimi) destekliyor mu?
\n    *   G\u00fcvenlik:<\/strong> Kimlik do\u011frulama ve yetkilendirme se\u00e7enekleri var m\u0131?<\/p>\n
Ad\u0131m 4: Konnekt\u00f6r Yap\u0131land\u0131rmas\u0131<\/h4>\n
Konnekt\u00f6rler, Kafka Connect REST API’si arac\u0131l\u0131\u011f\u0131yla JSON format\u0131nda yap\u0131land\u0131r\u0131l\u0131r ve y\u00f6netilir.<\/p>\n
*   Ortak Yap\u0131land\u0131rma Parametreleri:<\/strong>
\n    *   name<\/code>: Konnekt\u00f6r\u00fcn benzersiz ad\u0131.
\n    *   connector.class<\/code>: Kullan\u0131lacak konnekt\u00f6r s\u0131n\u0131f\u0131n\u0131n tam ad\u0131 (\u00f6rne\u011fin, io.confluent.connect.jdbc.JdbcSourceConnector<\/code>).
\n    *   tasks.max<\/code>: Bu konnekt\u00f6r i\u00e7in \u00e7al\u0131\u015ft\u0131r\u0131lacak maksimum g\u00f6rev say\u0131s\u0131. Daha fazla g\u00f6rev, daha fazla paralellik anlam\u0131na gelir.
\n    *   key.converter<\/code>, value.converter<\/code>: Konnekt\u00f6re \u00f6zg\u00fc d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler. Genellikle worker yap\u0131land\u0131rmas\u0131ndaki genel d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fclerle ayn\u0131d\u0131r, ancak gerekti\u011finde ge\u00e7ersiz k\u0131l\u0131nabilir.
\n    *   key.converter.schemas.enable<\/code>, value.converter.schemas.enable<\/code>: \u015eema bilgilerinin mesaj ba\u015fl\u0131\u011f\u0131na dahil edilip edilmeyece\u011fi. Avro kullan\u0131l\u0131yorsa true<\/code> olmal\u0131d\u0131r.
\n    *   transforms<\/code>: Uygulanacak SMT’lerin listesi.<\/p>\n
*   Source Konnekt\u00f6r \u00d6rne\u011fi (JDBC Source):<\/strong> Bir PostgreSQL veritaban\u0131ndaki orders<\/code> tablosundaki de\u011fi\u015fiklikleri Kafka’ya aktarma.<\/p>\n
{\n      \"name\": \"jdbc-orders-source\",\n      \"config\": {\n        \"connector.class\": \"io.confluent.connect.jdbc.JdbcSourceConnector\",\n        \"tasks.max\": \"1\",\n        \"connection.url\": \"jdbc:postgresql:\/\/localhost:5432\/mydb\",\n        \"connection.user\": \"dbuser\",\n        \"connection.password\": \"dbpassword\",\n        \"topic.prefix\": \"postgres-\",\n        \"mode\": \"incrementing\",\n        \"incrementing.column.name\": \"id\",\n        \"table.whitelist\": \"orders\",\n        \"poll.interval.ms\": \"5000\",\n        \"value.converter\": \"org.apache.kafka.connect.json.JsonConverter\",\n        \"value.converter.schemas.enable\": \"false\"\n      }\n    }<\/code><\/pre>\n
    *   mode<\/code>: incrementing<\/code> (art\u0131r\u0131ml\u0131 s\u00fctunu izle), timestamp<\/code> (zaman damgas\u0131 s\u00fctununu izle), bulk<\/code> (t\u00fcm tabloyu periyodik olarak \u00e7ek), query<\/code> (\u00f6zel bir sorguyu periyodik olarak \u00e7al\u0131\u015ft\u0131r).
\n    *   incrementing.column.name<\/code>: Art\u0131r\u0131ml\u0131 mod i\u00e7in kullan\u0131lacak s\u00fctun.
\n    *   table.whitelist<\/code>: \u0130zlenecek tablolar\u0131n virg\u00fclle ayr\u0131lm\u0131\u015f listesi.
\n    *   poll.interval.ms<\/code>: Veritaban\u0131n\u0131 ne s\u0131kl\u0131kla sorgulayaca\u011f\u0131n\u0131 belirler.<\/p>\n
*   Sink Konnekt\u00f6r \u00d6rne\u011fi (JDBC Sink):<\/strong> Kafka’daki app-events<\/code> topic’inden gelen veriyi bir veritaban\u0131ndaki events<\/code> tablosuna yazma.<\/p>\n
{\n      \"name\": \"jdbc-events-sink\",\n      \"config\": {\n        \"connector.class\": \"io.confluent.connect.jdbc.JdbcSinkConnector\",\n        \"tasks.max\": \"1\",\n        \"connection.url\": \"jdbc:postgresql:\/\/localhost:5432\/mydb\",\n        \"connection.user\": \"dbuser\",\n        \"connection.password\": \"dbpassword\",\n        \"topics\": \"app-events\",\n        \"auto.create\": \"true\",\n        \"auto.evolve\": \"true\",\n        \"insert.mode\": \"upsert\",\n        \"pk.mode\": \"record_value\",\n        \"pk.fields\": \"event_id\",\n        \"value.converter\": \"org.apache.kafka.connect.json.JsonConverter\",\n        \"value.converter.schemas.enable\": \"false\"\n      }\n    }<\/code><\/pre>\n
    *   topics<\/code>: Dinlenecek Kafka topic’lerinin virg\u00fclle ayr\u0131lm\u0131\u015f listesi.
\n    *   auto.create<\/code>: Hedef tablonun otomatik olu\u015fturulup olu\u015fturulmayaca\u011f\u0131.
\n    *   auto.evolve<\/code>: Hedef tablonun \u015femas\u0131n\u0131n otomatik olarak g\u00fcncellenip g\u00fcncellenmeyece\u011fi.
\n    *   insert.mode<\/code>: insert<\/code>, upsert<\/code>, update<\/code>.
\n    *   pk.mode<\/code>: Birincil anahtar\u0131n nas\u0131l belirlenece\u011fi (kafka_key<\/code>, record_key<\/code>, record_value<\/code>, none<\/code>).
\n    *   pk.fields<\/code>: Birincil anahtar olarak kullan\u0131lacak alanlar.<\/p>\n
*   SMT Kullan\u0131m\u0131 \u00d6rne\u011fi:<\/strong> Bir mesajdan belirli bir alan\u0131 \u00e7\u0131karma ve bir zaman damgas\u0131 alan\u0131n\u0131 yeniden bi\u00e7imlendirme.<\/p>\n
{\n      \"name\": \"my-source-connector-with-smt\",\n      \"config\": {\n        \"connector.class\": \"...\",\n        \"topics\": \"...\",\n        \"transforms\": \"ExtractTimestamp, RenameField\",\n        \"transforms.ExtractTimestamp.type\": \"org.apache.kafka.connect.transforms.TimestampConverter$Value\",\n        \"transforms.ExtractTimestamp.field\": \"eventTime\",\n        \"transforms.ExtractTimestamp.target.type\": \"string\",\n        \"transforms.ExtractTimestamp.format\": \"yyyy-MM-dd HH:mm:ss\",\n        \"transforms.RenameField.type\": \"org.apache.kafka.connect.transforms.ReplaceField$Value\",\n        \"transforms.RenameField.exclude\": \"unnecessary_field\"\n      }\n    }<\/code><\/pre>\n
Ad\u0131m 5: Konnekt\u00f6r\u00fc Ba\u015flatma ve \u0130zleme<\/h4>\n
Konnekt\u00f6r yap\u0131land\u0131rmas\u0131 haz\u0131r oldu\u011funda, Kafka Connect REST API arac\u0131l\u0131\u011f\u0131yla ba\u015flat\u0131labilir.<\/p>\n
*   Konnekt\u00f6r\u00fc Ba\u015flatma:<\/strong><\/p>\n
curl -X POST -H \"Content-Type: application\/json\" --data \"@connector-config.json\" http:\/\/localhost:8083\/connectors<\/code><\/pre>\n
    Burada connector-config.json<\/code>, yukar\u0131daki JSON yap\u0131land\u0131rmas\u0131n\u0131 i\u00e7eren bir dosyad\u0131r. localhost:8083<\/code>, Kafka Connect worker REST API’sinin varsay\u0131lan adresidir.
\n*   Durum Kontrol\u00fc:<\/strong><\/p>\n
curl http:\/\/localhost:8083\/connectors\/jdbc-orders-source\/status<\/code><\/pre>\n
    Bu komut, konnekt\u00f6r\u00fcn ve g\u00f6revlerinin durumunu (RUNNING, PAUSED, FAILED) g\u00f6sterir.
\n*   Konnekt\u00f6rleri Listeleme:<\/strong><\/p>\n
curl http:\/\/localhost:8083\/connectors<\/code><\/pre>\n
*   Log Takibi:<\/strong> Worker log’lar\u0131n\u0131 (genellikle logs\/connect.log<\/code> veya konsol \u00e7\u0131kt\u0131s\u0131) d\u00fczenli olarak kontrol edin. Hatalar veya uyar\u0131lar burada g\u00f6r\u00fcnecektir.
\n*   \u0130zleme Metrikleri:<\/strong> Kafka Connect, JMX metrikleri sa\u011flar. Bu metrikler, konnekt\u00f6rlerin ve g\u00f6revlerin performans\u0131n\u0131 (i\u015flenen mesaj say\u0131s\u0131, gecikme, hata oranlar\u0131) izlemek i\u00e7in Prometheus\/Grafana gibi ara\u00e7larla entegre edilebilir. Confluent Control Center gibi ticari ara\u00e7lar da kapsaml\u0131 izleme ve y\u00f6netim yetenekleri sunar.<\/p>\n
Ad\u0131m 6: Hata Y\u00f6netimi ve \u00d6l\u00e7eklendirme<\/h4>\n
\u00dcretim ortamlar\u0131nda kararl\u0131 ve g\u00fcvenilir bir entegrasyon i\u00e7in hata y\u00f6netimi ve \u00f6l\u00e7eklendirme stratejileri kritik \u00f6neme sahiptir.<\/p>\n
*   Hata Tolerans\u0131:<\/strong>
\n    *   Dead Letter Queue (DLQ):<\/strong> Kafka Connect, i\u015flenemeyen mesajlar\u0131 (\u00f6rne\u011fin, \u015fema uyumsuzluklar\u0131, format hatalar\u0131) \u00f6zel bir “dead letter topic”e y\u00f6nlendirebilir. Bu, ana veri ak\u0131\u015f\u0131n\u0131n kesintiye u\u011framadan devam etmesini sa\u011flarken, hatal\u0131 mesajlar\u0131n daha sonra incelenmesine olanak tan\u0131r. errors.tolerance=all<\/code> ve errors.deadletterqueue.topic<\/code> gibi ayarlar kullan\u0131l\u0131r.
\n    *   errors.log.enable<\/code>:<\/strong> Hatalar\u0131n log’a yaz\u0131l\u0131p yaz\u0131lmayaca\u011f\u0131n\u0131 kontrol eder.
\n    *   errors.log.include.messages<\/code>:<\/strong> Hatal\u0131 mesajlar\u0131n log’a dahil edilip edilmeyece\u011fini kontrol eder.
\n*   \u00d6l\u00e7eklendirme:<\/strong>
\n    *   Yatay \u00d6l\u00e7ekleme:<\/strong> \u0130\u015f y\u00fck\u00fc artt\u0131\u011f\u0131nda, Kafka Connect k\u00fcmesine yeni worker’lar ekleyebilirsiniz. G\u00f6revler otomatik olarak yeni worker’lar aras\u0131nda yeniden da\u011f\u0131t\u0131lacakt\u0131r.
\n    *   tasks.max<\/code> Ayar\u0131:<\/strong> Bir konnekt\u00f6r\u00fcn \u00e7al\u0131\u015ft\u0131rabilece\u011fi maksimum g\u00f6rev say\u0131s\u0131n\u0131 art\u0131rmak, daha fazla paralellik sa\u011flayarak performans\u0131 art\u0131rabilir. Ancak, bu ayar\u0131 kayna\u011f\u0131n veya hedefin kapasitesine ve Kafka topic’inin b\u00f6l\u00fcm say\u0131s\u0131na g\u00f6re dikkatli bir \u015fekilde ayarlamak gerekir.
\n    *   Kafka Topic B\u00f6l\u00fcm Say\u0131s\u0131:<\/strong> Source konnekt\u00f6rler i\u00e7in, \u00fcretilen verinin yaz\u0131ld\u0131\u011f\u0131 Kafka topic’inin b\u00f6l\u00fcm say\u0131s\u0131, paralelli\u011fi do\u011frudan etkiler. Sink konnekt\u00f6rler i\u00e7in ise, okunan topic’in b\u00f6l\u00fcm say\u0131s\u0131, sink g\u00f6revlerinin say\u0131s\u0131n\u0131 s\u0131n\u0131rlayabilir.<\/p>\n
Pop\u00fcler Entegrasyon Senaryolar\u0131 ve \u00d6rnekler<\/h3>\n
Kafka Connect, \u00e7ok \u00e7e\u015fitli entegrasyon senaryolar\u0131nda kullan\u0131labilir. \u0130\u015fte baz\u0131 yayg\u0131n \u00f6rnekler:<\/p>\n
Veritaban\u0131 Entegrasyonu<\/h4>\n
*   Change Data Capture (CDC) ile Ger\u00e7ek Zamanl\u0131 Veri Ak\u0131\u015f\u0131:<\/strong>
\n    *   Senaryo:<\/strong> Bir e-ticaret uygulamas\u0131n\u0131n i\u015flem veritaban\u0131ndaki (\u00f6rne\u011fin, MySQL, PostgreSQL, SQL Server) sipari\u015f, \u00fcr\u00fcn veya m\u00fc\u015fteri de\u011fi\u015fikliklerini ger\u00e7ek zamanl\u0131 olarak yakalay\u0131p Kafka’ya aktarmak. Bu veriler daha sonra arama motorlar\u0131na, \u00f6nbelleklere veya analitik sistemlere beslenebilir.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> Debezium Source Konnekt\u00f6rleri (Debezium MySQL Konnekt\u00f6r, Debezium PostgreSQL Konnekt\u00f6r vb.) veritaban\u0131n\u0131n i\u015flem g\u00fcnl\u00fcklerini (binlog, WAL) okuyarak t\u00fcm insert, update, delete olaylar\u0131n\u0131 yakalar ve Kafka topic’lerine yazar. Bu, veritaban\u0131na sorgu y\u00fck\u00fc bindirmeden ger\u00e7ek zamanl\u0131 veri ak\u0131\u015f\u0131 sa\u011flar.
\n*   Veritaban\u0131na Veri Yazma:<\/strong>
\n    *   Senaryo:<\/strong> Kafka’da i\u015flenen veya toplanan verileri (\u00f6rne\u011fin, kullan\u0131c\u0131 etkile\u015fimleri, loglar, sens\u00f6r verileri) bir analitik veritaban\u0131na veya operasyonel bir veri deposuna yazmak.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> JDBC Sink Konnekt\u00f6r veya belirli bir veritaban\u0131 i\u00e7in \u00f6zel olarak yaz\u0131lm\u0131\u015f bir Sink Konnekt\u00f6r (\u00f6rne\u011fin, Cassandra Sink, MongoDB Sink) kullan\u0131l\u0131r. insert.mode<\/code> ve pk.mode<\/code> ayarlar\u0131yla veri tutarl\u0131l\u0131\u011f\u0131 ve idempotentlik sa\u011flanabilir.<\/p>\n
Dosya Sistemi Entegrasyonu<\/h4>\n
*   Log Dosyalar\u0131n\u0131 Kafka’ya Aktarma:<\/strong>
\n    *   Senaryo:<\/strong> Uygulama sunucular\u0131nda \u00fcretilen log dosyalar\u0131n\u0131 (JSON, CSV, metin) ger\u00e7ek zamanl\u0131 olarak Kafka’ya aktarmak ve merkezi bir log toplama sistemine (\u00f6rne\u011fin, ELK Stack) beslemek.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> File Stream Source Konnekt\u00f6r veya daha geli\u015fmi\u015f \u00e7\u00f6z\u00fcmler i\u00e7in bir log toplama arac\u0131n\u0131n (Fluentd, Logstash) Kafka Connect entegrasyonu kullan\u0131labilir.
\n*   Kafka’dan HDFS\/S3’e Veri Ar\u015fivleme:<\/strong>
\n    *   Senaryo:<\/strong> Kafka’daki y\u00fcksek hacimli verileri (\u00f6rne\u011fin, telemetri verileri, IoT sens\u00f6r verileri) uzun s\u00fcreli depolama ve b\u00fcy\u00fck veri analizi i\u00e7in bir veri g\u00f6l\u00fcne (HDFS veya Amazon S3) ar\u015fivlemek.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> HDFS Sink Konnekt\u00f6r veya S3 Sink Konnekt\u00f6r, Kafka topic’lerinden veriyi okuyarak belirli bir boyuta veya zamana ula\u015ft\u0131\u011f\u0131nda s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f dosyalara (Parquet, ORC, Avro) d\u00f6n\u00fc\u015ft\u00fcr\u00fcr ve veri g\u00f6l\u00fcne yazar.<\/p>\n
API ve Mikroservis Entegrasyonu<\/h4>\n
*   REST API’lerden Veri \u00c7ekme:<\/strong>
\n    *   Senaryo:<\/strong> \u00dc\u00e7\u00fcnc\u00fc taraf SaaS uygulamalar\u0131ndan veya dahili mikroservislerden REST API’leri arac\u0131l\u0131\u011f\u0131yla veri \u00e7ekmek ve Kafka’ya aktarmak.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> REST API Source Konnekt\u00f6rleri (Confluent Hub’da bulunabilir) veya \u00f6zel bir konnekt\u00f6r geli\u015ftirme. Bu konnekt\u00f6rler, belirli bir aral\u0131klarla API’yi sorgulayabilir ve yan\u0131tlar\u0131 Kafka’ya yazabilir.
\n*   Kafka Olaylar\u0131n\u0131 Mikroservislere Y\u00f6nlendirme:<\/strong>
\n    *   Senaryo:<\/strong> Kafka’da belirli bir olay\u0131n (\u00f6rne\u011fin, yeni sipari\u015f, kullan\u0131c\u0131 kayd\u0131) meydana gelmesi durumunda, bu olay\u0131 bir mikroservisin REST API’sine HTTP iste\u011fi olarak g\u00f6ndermek.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> HTTP Sink Konnekt\u00f6r veya \u00f6zel bir konnekt\u00f6r geli\u015ftirme. Bu konnekt\u00f6r, Kafka’dan gelen mesajlar\u0131 HTTP iste\u011fi g\u00f6vdesine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr ve hedef API’ye g\u00f6nderir.<\/p>\n
Veri Ambar\u0131\/G\u00f6l\u00fc Entegrasyonu<\/h4>\n
*   Kafka’dan Bulut Veri Ambarlar\u0131na Aktar\u0131m:<\/strong>
\n    *   Senaryo:<\/strong> Kafka’da ger\u00e7ek zamanl\u0131 olarak i\u015flenen veya toplanan verileri (\u00f6rne\u011fin, m\u00fc\u015fteri davran\u0131\u015f verileri, finansal i\u015flemler) Snowflake, Google BigQuery veya Amazon Redshift gibi bulut tabanl\u0131 veri ambarlar\u0131na y\u00fcklemek.
\n    *   \u00c7\u00f6z\u00fcm:<\/strong> \u0130lgili veri ambar\u0131 i\u00e7in \u00f6zel olarak geli\u015ftirilmi\u015f Sink Konnekt\u00f6rler (Snowflake Sink Konnekt\u00f6r, BigQuery Sink Konnekt\u00f6r) kullan\u0131l\u0131r. Bu konnekt\u00f6rler, genellikle veri ambarlar\u0131n\u0131n toplu y\u00fckleme (bulk loading) mekanizmalar\u0131n\u0131 kullanarak y\u00fcksek performansl\u0131 veri aktar\u0131m\u0131 sa\u011flar.<\/p>\n
Bu senaryolar, Kafka Connect’in ne kadar esnek ve \u00e7ok y\u00f6nl\u00fc bir ara\u00e7 oldu\u011funu g\u00f6stermektedir. Do\u011fru konnekt\u00f6r se\u00e7imi ve yap\u0131land\u0131rmas\u0131 ile neredeyse her t\u00fcrl\u00fc mevcut sistemi Kafka ekosistemine entegre etmek m\u00fcmk\u00fcnd\u00fcr.<\/p>\n
En \u0130yi Uygulamalar ve Dikkat Edilmesi Gerekenler<\/h3>\n
Kafka Connect ile entegrasyonlar\u0131 ba\u015far\u0131l\u0131 ve s\u00fcrd\u00fcr\u00fclebilir k\u0131lmak i\u00e7in baz\u0131 en iyi uygulamalar\u0131 takip etmek ve dikkat edilmesi gereken noktalar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurmak \u00f6nemlidir.<\/p>\n
G\u00fcvenlik<\/h4>\n
*   Kimlik Do\u011frulama (Authentication):<\/strong> Kafka Connect worker’lar\u0131 ile Kafka broker’lar\u0131 aras\u0131ndaki ileti\u015fimi g\u00fcvence alt\u0131na almak i\u00e7in SASL (Simple Authentication and Security Layer) kullan\u0131n. Kaynak ve hedef sistemlere ba\u011flan\u0131rken de uygun kimlik do\u011frulama mekanizmalar\u0131n\u0131 (\u00f6rne\u011fin, veritaban\u0131 kullan\u0131c\u0131 adlar\u0131\/parolalar\u0131, API anahtarlar\u0131, OAuth) yap\u0131land\u0131r\u0131n. Hassas bilgileri (parolalar) do\u011frudan yap\u0131land\u0131rma dosyalar\u0131nda saklamak yerine, g\u00fcvenli bir \u015fekilde y\u00f6netmek i\u00e7in s\u0131r y\u00f6netim sistemleri (Vault, Kubernetes Secrets) kullan\u0131n.
\n*   Yetkilendirme (Authorization):<\/strong> Kafka Connect worker’lar\u0131n\u0131n belirli Kafka topic’lerine okuma\/yazma eri\u015fimine sahip oldu\u011fundan emin olmak i\u00e7in Kafka ACL’lerini (Access Control Lists) yap\u0131land\u0131r\u0131n.
\n*   \u015eifreleme (Encryption):<\/strong> Kafka Connect worker’lar\u0131 ile Kafka broker’lar\u0131 aras\u0131ndaki ileti\u015fimi ve kaynak\/hedef sistemlerle olan ileti\u015fimi SSL\/TLS kullanarak \u015fifreleyin.<\/p>\n
Performans Optimizasyonu<\/h4>\n
*   tasks.max<\/code> Ayar\u0131:<\/strong> Konnekt\u00f6r\u00fcn paralelli\u011fini art\u0131rmak i\u00e7in tasks.max<\/code> de\u011ferini art\u0131r\u0131n. Ancak, bu de\u011feri Kafka topic’inin b\u00f6l\u00fcm say\u0131s\u0131n\u0131 ve kaynak\/hedef sistemin e\u015fzamanl\u0131 ba\u011flant\u0131\/i\u015fleme kapasitesini a\u015fmayacak \u015fekilde ayarlay\u0131n.
\n*   Batch Boyutu ve Buffer Belle\u011fi:<\/strong> Sink konnekt\u00f6rleri i\u00e7in batch.size<\/code> ve buffer.memory<\/code> gibi ayarlar\u0131 optimize edin. Daha b\u00fcy\u00fck batch boyutlar\u0131, genellikle daha az a\u011f trafi\u011fi ve daha y\u00fcksek verim sa\u011flar, ancak gecikmeyi art\u0131rabilir.
\n*   Poll Aral\u0131klar\u0131:<\/strong> Source konnekt\u00f6rler i\u00e7in poll.interval.ms<\/code> (veri kayna\u011f\u0131ndan ne s\u0131kl\u0131kla veri \u00e7ekilece\u011fi) ayar\u0131n\u0131 dikkatli se\u00e7in. \u00c7ok d\u00fc\u015f\u00fck bir de\u011fer, kaynak sistem \u00fczerinde gereksiz y\u00fck olu\u015fturabilir; \u00e7ok y\u00fcksek bir de\u011fer ise gecikmeyi art\u0131r\u0131r.
\n*   Veri Format\u0131:<\/strong> Avro gibi ikili formatlar, JSON gibi metin tabanl\u0131 formatlara g\u00f6re daha kompakt ve daha h\u0131zl\u0131 i\u015flenebilir. Schema Registry ile birlikte Avro kullanmak, hem performans hem de veri b\u00fct\u00fcnl\u00fc\u011f\u00fc a\u00e7\u0131s\u0131ndan avantajl\u0131d\u0131r.<\/p>\n
Veri \u015eemas\u0131 Y\u00f6netimi<\/h4>\n
*   Schema Registry Kullan\u0131m\u0131:<\/strong> Avro gibi \u015fema tabanl\u0131 formatlar kullan\u0131rken Confluent Schema Registry’yi Kafka Connect ile entegre edin. Schema Registry, veri \u015femalar\u0131n\u0131 merkezi olarak y\u00f6netir, evrimini destekler ve veri uyumsuzluklar\u0131n\u0131 \u00f6nler. Bu, veri kalitesini ve tutarl\u0131l\u0131\u011f\u0131n\u0131 sa\u011flamak i\u00e7in kritik \u00f6neme sahiptir.
\n*   \u015eema Evrimi Stratejileri:<\/strong> \u015eema de\u011fi\u015fikliklerini (alan ekleme, \u00e7\u0131karma, yeniden adland\u0131rma) y\u00f6netmek i\u00e7in uyumlu \u015fema evrimi stratejileri (\u00f6rne\u011fin, geriye d\u00f6n\u00fck uyumluluk) kullan\u0131n.<\/p>\n
Hata Tolerans\u0131 ve Kurtarma<\/h4>\n
*   Idempotent Konnekt\u00f6rler:<\/strong> Veri kayb\u0131n\u0131 veya yinelenen verileri \u00f6nlemek i\u00e7in idempotent yazma yetene\u011fine sahip Sink konnekt\u00f6rleri tercih edin veya yap\u0131land\u0131r\u0131n. Bu, ayn\u0131 verinin birden \u00e7ok kez g\u00f6nderilmesi durumunda bile hedef sistemde yaln\u0131zca bir kez i\u015flenmesini sa\u011flar.
\n*   Dead Letter Queue (DLQ):<\/strong> Yukar\u0131da bahsedildi\u011fi gibi, i\u015flenemeyen mesajlar\u0131 \u00f6zel bir DLQ topic’ine y\u00f6nlendirerek ana veri ak\u0131\u015f\u0131n\u0131n kesintisiz devam etmesini sa\u011flay\u0131n ve hatal\u0131 mesajlar\u0131 daha sonra analiz edin.
\n*   Offset Y\u00f6netimi:<\/strong> Kafka Connect, offset’leri otomatik olarak Kafka’da y\u00f6netir. Bu, bir worker’\u0131n \u00e7\u00f6kmesi durumunda bile, yeniden ba\u015flat\u0131ld\u0131\u011f\u0131nda kald\u0131\u011f\u0131 yerden devam etmesini sa\u011flar. Offset topic’lerinin replikasyon fakt\u00f6r\u00fcn\u00fcn y\u00fcksek oldu\u011fundan emin olun.<\/p>\n
\u0130zleme ve Uyar\u0131<\/h4>\n
*   Kapsaml\u0131 \u0130zleme:<\/strong> Kafka Connect worker’lar\u0131n\u0131n ve konnekt\u00f6rlerinin durumunu, performans metriklerini (i\u015flenen mesaj say\u0131s\u0131, gecikme, hata oranlar\u0131) s\u00fcrekli olarak izleyin. Prometheus, Grafana, Confluent Control Center gibi ara\u00e7lar bu ama\u00e7la kullan\u0131labilir.
\n*   Proaktif Uyar\u0131lar:<\/strong> \u00d6nemli metrikler belirli e\u015fik de\u011ferlerini a\u015ft\u0131\u011f\u0131nda (\u00f6rne\u011fin, g\u00f6rev ba\u015far\u0131s\u0131z oldu\u011funda, gecikme artt\u0131\u011f\u0131nda, hata oran\u0131 y\u00fckseldi\u011finde) otomatik uyar\u0131lar (e-posta, Slack, PagerDuty) kurun.<\/p>\n
Konnekt\u00f6r Geli\u015ftirme<\/h4>\n
*   \u00d6zel Konnekt\u00f6rler:<\/strong> Mevcut konnekt\u00f6rler belirli bir sistem veya senaryo i\u00e7in yetersiz kald\u0131\u011f\u0131nda, Kafka Connect API’sini kullanarak \u00f6zel Source veya Sink konnekt\u00f6rleri geli\u015ftirebilirsiniz. Bu, Java ile yap\u0131l\u0131r ve Kafka Connect’in geni\u015fletilebilirli\u011fini g\u00f6sterir. \u00d6zel konnekt\u00f6r geli\u015ftirirken, hata y\u00f6netimi, \u00f6l\u00e7eklenebilirlik ve performans konular\u0131na dikkat edin.
\n*   Topluluk Kat\u0131l\u0131m\u0131:<\/strong> A\u00e7\u0131k kaynak konnekt\u00f6rler kullan\u0131yorsan\u0131z, topluluk forumlar\u0131n\u0131 ve belgeleri takip edin, sorunlar\u0131 bildirin ve hatta katk\u0131da bulunun.<\/p>\n
Bu en iyi uygulamalar\u0131 ve dikkat edilmesi gereken noktalar\u0131 uygulayarak, Kafka Connect tabanl\u0131 entegrasyon \u00e7\u00f6z\u00fcmlerinizin sa\u011flam, g\u00fcvenilir, \u00f6l\u00e7eklenebilir ve y\u00f6netilebilir olmas\u0131n\u0131 sa\u011flayabilirsiniz.<\/p>\n
Sonu\u00e7<\/h3>\n
Mevcut sistemleri entegre etmek, modern i\u015fletmelerin dijital d\u00f6n\u00fc\u015f\u00fcm yolculu\u011funun temel bir bile\u015fenidir. Apache Kafka, ger\u00e7ek zamanl\u0131 veri ak\u0131\u015f\u0131 i\u00e7in g\u00fc\u00e7l\u00fc bir platform sa\u011flarken, Kafka Connect bu platformun de\u011ferini, \u00e7e\u015fitli veri kaynaklar\u0131 ve hedeflerle kesintisiz ba\u011flant\u0131 kurarak katlamaktad\u0131r. Bu makalede ele ald\u0131\u011f\u0131m\u0131z gibi, Kafka Connect, haz\u0131r konnekt\u00f6rler, da\u011f\u0131t\u0131k ve hata toleransl\u0131 mimarisi, \u00f6l\u00e7eklenebilirlik yetenekleri ve esnek yap\u0131land\u0131rma se\u00e7enekleri sayesinde, geleneksel entegrasyon zorluklar\u0131n\u0131n \u00fcstesinden gelmek i\u00e7in ideal bir \u00e7\u00f6z\u00fcmd\u00fcr.<\/p>\n
\u0130htiya\u00e7 analizi ve planlamadan ba\u015flayarak, uygun konnekt\u00f6rlerin se\u00e7ilmesi, yap\u0131land\u0131r\u0131lmas\u0131, ba\u015flat\u0131lmas\u0131 ve izlenmesine kadar her ad\u0131m, ba\u015far\u0131l\u0131 bir entegrasyon stratejisinin anahtar\u0131d\u0131r. Debezium ile ger\u00e7ek zamanl\u0131 CDC, JDBC konnekt\u00f6rleri ile veritaban\u0131 entegrasyonu, S3\/HDFS konnekt\u00f6rleri ile veri g\u00f6l\u00fc ar\u015fivleme ve hatta \u00f6zel konnekt\u00f6rlerle API entegrasyonu gibi pop\u00fcler senaryolar, Kafka Connect’in geni\u015f uygulama alan\u0131n\u0131 g\u00f6zler \u00f6n\u00fcne sermektedir.<\/p>\n
G\u00fcvenlik, performans optimizasyonu, \u015fema y\u00f6netimi, hata tolerans\u0131 ve kapsaml\u0131 izleme gibi en iyi uygulamalar\u0131n benimsenmesi, entegrasyon \u00e7\u00f6z\u00fcmlerinizin uzun vadede s\u00fcrd\u00fcr\u00fclebilir ve g\u00fcvenilir olmas\u0131n\u0131 sa\u011flayacakt\u0131r. Kafka Connect, i\u015fletmelerin veri silolar\u0131n\u0131 ortadan kald\u0131rmas\u0131na, veri ak\u0131\u015flar\u0131n\u0131 merkezile\u015ftirmesine ve ger\u00e7ek zamanl\u0131 i\u015f zekas\u0131 ile operasyonel \u00e7evikli\u011fi art\u0131rmas\u0131na olanak tan\u0131r.<\/p>\n
Sonu\u00e7 olarak, Kafka Connect, modern veri mimarilerinin ayr\u0131lmaz bir par\u00e7as\u0131d\u0131r ve mevcut sistemleriniz ile Kafka ekosistemi aras\u0131nda sa\u011flam, \u00f6l\u00e7eklenebilir ve y\u00f6netilebilir bir k\u00f6pr\u00fc kurmak isteyen her kurulu\u015f i\u00e7in vazge\u00e7ilmez bir ara\u00e7t\u0131r. Do\u011fru yakla\u015f\u0131m ve ara\u00e7larla, veri entegrasyonu art\u0131k bir engel de\u011fil, rekabet avantaj\u0131 sa\u011flayan bir f\u0131rsat haline gelebilir.<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Mevcut Sistemleri Kafka Connect ile Entegre Etme Rehberi Giri\u015f G\u00fcn\u00fcm\u00fcz\u00fcn h\u0131zla de\u011fi\u015fen dijital d\u00fcnyas\u0131nda, i\u015fletmelerin kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en b\u00fcy\u00fck zorluklardan biri, farkl\u0131 teknolojilerle geli\u015ftirilmi\u015f, \u00e7e\u015fitli veri formatlar\u0131nda bilgi \u00fcreten ve t\u00fcketen mevcut sistemleri birbirine entegre etmektir.","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":[1],"tags":[],"class_list":{"0":"post-40711","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nMevcut Sistemleri Kafka Connect ile Entegre Etme Rehberi - 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