{"id":6219,"date":"2025-09-09T08:16:20","date_gmt":"2025-09-09T08:16:20","guid":{"rendered":"https:\/\/bentego.com\/?p=6219"},"modified":"2025-10-20T16:09:11","modified_gmt":"2025-10-20T16:09:11","slug":"dwh-vs-data-lake-vs-data-lakehouse-verinin-yeni-evi-neresi","status":"publish","type":"post","link":"https:\/\/bentego.com\/en\/dwh-vs-data-lake-vs-data-lakehouse-verinin-yeni-evi-neresi\/","title":{"rendered":"DWH vs Data Lake vs Data Lakehouse: Verinin Yeni Evi Neresi"},"content":{"rendered":"\n

Son y\u0131llarda veriyle \u00e7al\u0131\u015fan herkesin s\u0131k s\u0131k kar\u015f\u0131la\u015ft\u0131\u011f\u0131 bir soru var:
\u201cVerilerimizi nerede saklamal\u0131y\u0131z?\u201d
<\/strong>Bu sorunun cevab\u0131, zaman i\u00e7inde hem teknolojilerin geli\u015fmesiyle hem de ihtiya\u00e7lar\u0131n de\u011fi\u015fmesiyle epey \u015fekil de\u011fi\u015ftirdi. Bir d\u00f6nem t\u00fcm verimizi b\u00fcy\u00fck bir \u00f6zenle tasarlanan veri ambarlar\u0131nda (Data Warehouse)<\/strong> tutard\u0131k. Raporlar\u0131m\u0131z\u0131 \u00fcretir, analizlerimizi yapar, veriye g\u00fcvenirdik.<\/p>\n\n\n\n

Ama i\u015fler de\u011fi\u015fti.<\/p>\n\n\n\n

Veri sadece Excel tablolar\u0131 ya da veritaban\u0131 kay\u0131tlar\u0131ndan ibaret de\u011fil art\u0131k. Log dosyalar\u0131, sens\u00f6r verileri, sosyal medya i\u00e7erikleri, videolar, PDF\u2019ler\u2026<\/em> \u00dcstelik bu veriler sadece b\u00fcy\u00fcm\u00fcyor, ayn\u0131 zamanda \u00e7e\u015fitleniyor ve h\u0131zla ak\u0131yor. Tam bu noktada geleneksel DWH yap\u0131lar\u0131 yeterli olmamaya ba\u015flad\u0131. \u0130\u015fte o zaman sahneye Data Lake<\/strong>\u2019ler \u00e7\u0131kt\u0131.<\/p>\n\n\n\n

Data Lake: Veriye \u00d6zg\u00fcrl\u00fck<\/h2>\n\n\n\n

Data Lake, asl\u0131nda olduk\u00e7a basit bir fikre dayan\u0131yor:
Veriyi ne olursa olsun topla, sakla, sonra kullan.<\/em><\/p>\n\n\n\n

Yap\u0131land\u0131r\u0131lm\u0131\u015f, yar\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f ya da tamamen ham veriler\u2026 Hepsi tek bir yerde. Bu, \u00f6zellikle veri bilimciler<\/strong> i\u00e7in adeta bir cennetti. Art\u0131k veriyi i\u015flemeden \u00f6nce filtrelemeye, temizlemeye ya da \u015fekle sokmaya gerek kalmadan direkt eri\u015febiliyorlard\u0131.<\/p>\n\n\n\n

Yani klasik \u201c\u00f6nce ETL sonra analiz\u201d d\u00f6ng\u00fcs\u00fc k\u0131r\u0131ld\u0131.
Ama\u2026<\/p>\n\n\n\n

Data Lake\u2019in Sorunlar\u0131:<\/h2>\n\n\n\n
    \n
  • Veri kalitesinin kontrol\u00fc zorla\u015ft\u0131<\/li>\n\n\n\n
  • Ayn\u0131 verinin farkl\u0131 versiyonlar\u0131 kar\u0131\u015fmaya ba\u015flad\u0131<\/li>\n\n\n\n
  • Eri\u015fim ve g\u00fcvenlik a\u00e7\u0131klar\u0131 olu\u015ftu<\/li>\n\n\n\n
  • Sorgular yava\u015flad\u0131, performans d\u00fc\u015ft\u00fc<\/li>\n<\/ul>\n\n\n\n

    Zamanla bu devasa g\u00f6ller, neyin nerede oldu\u011funu kimsenin bilmedi\u011fi birer \u201cveri batakl\u0131\u011f\u0131\u201dna (data swamp)<\/strong> d\u00f6n\u00fc\u015fmeye ba\u015flad\u0131.<\/p>\n\n\n\n

    Lakehouse: \u0130ki D\u00fcnyan\u0131n En \u0130yisi<\/h2>\n\n\n\n

    \u0130\u015fte bu noktada veri d\u00fcnyas\u0131nda yeni bir kavram y\u00fckseldi:
    Data Lakehouse<\/strong> \u2014 Esneklik + Disiplin<\/em><\/p>\n\n\n\n

    Bu yap\u0131, veri ambarlar\u0131n\u0131n d\u00fczenli, g\u00fcvenli yap\u0131s\u0131yla Data Lake\u2019lerin esnekli\u011fini birle\u015ftiriyor. Hem SQL ile kolay sorgular yapabiliyor, hem de veri bilimciler Python\/R ile ayn\u0131 veriyi \u00f6zg\u00fcrce i\u015fleyebiliyor.<\/p>\n\n\n\n

    Bu mimariyi m\u00fcmk\u00fcn k\u0131lan teknolojiler de bo\u015f durmad\u0131 tabii. Son y\u0131llarda ad\u0131n\u0131 s\u0131k\u00e7a duydu\u011fumuz baz\u0131 ara\u00e7lar bu alanda \u00f6ne \u00e7\u0131kt\u0131:<\/p>\n\n\n\n

      \n
    • Apache Iceberg<\/strong>: B\u00fcy\u00fck veri k\u00fcmelerinde \u015fema y\u00f6netimi ve performansl\u0131 sorgular i\u00e7in birebir. ACID deste\u011fi mevcut.<\/li>\n\n\n\n
    • Delta Lake<\/strong>: Veride versiyonlama, zaman i\u00e7inde geriye d\u00f6nme, ACID deste\u011fi gibi \u00f6zelliklerle olduk\u00e7a g\u00fc\u00e7l\u00fc.<\/li>\n\n\n\n
    • Apache Hudi<\/strong>: Ger\u00e7ek zamanl\u0131 veri ak\u0131\u015f\u0131yla u\u011fra\u015fanlar i\u00e7in ideal; yaz\u0131lan veriyi an\u0131nda g\u00fcncelleyebiliyor.<\/li>\n<\/ul>\n\n\n\n

      T\u00fcm bu ara\u00e7lar, tek bir ama\u00e7 i\u00e7in var: Ayn\u0131 veriye farkl\u0131 rollerin (analist, m\u00fchendis, veri bilimci) farkl\u0131 \u015fekillerde eri\u015febilmesini sa\u011flamak.<\/p>\n\n\n\n

      Hangi Yap\u0131, Ne Zaman?<\/h2>\n\n\n\n

      Her yap\u0131n\u0131n kendine g\u00f6re art\u0131lar\u0131 var. Tek bir \u201cdo\u011fru\u201d yok asl\u0131nda, ama baz\u0131 senaryolarda hangisinin \u00f6ne \u00e7\u0131kt\u0131\u011f\u0131 belli:<\/p>\n\n\n\n

        \n
      • Sadece raporlama yap\u0131yorsan\u0131z<\/strong>\u00a0ve veriniz olduk\u00e7a d\u00fczenliyse, klasik DWH h\u00e2l\u00e2 i\u015f g\u00f6r\u00fcyor.<\/li>\n\n\n\n
      • Veri \u00e7e\u015fitlili\u011finiz y\u00fcksekse<\/strong>, yani hem log, hem resim, hem tablo kullan\u0131yorsan\u0131z, Data Lake sizin i\u00e7in iyi bir ba\u015flang\u0131\u00e7 olabilir.<\/li>\n\n\n\n
      • Verinizle hem analiz, hem modelleme yapacaksan\u0131z<\/strong>, tek platformda esneklik ve g\u00fcven ar\u0131yorsan\u0131z, en mant\u0131kl\u0131 tercih:\u00a0Data Lakehouse<\/em>.<\/li>\n<\/ul>\n\n\n\n

        Son S\u00f6z: Verinin Evi Art\u0131k De\u011fi\u015fti<\/h2>\n\n\n\n

        Veriyle \u00e7al\u0131\u015fan ekipler art\u0131k sadece neyi bildiklerine de\u011fil, ne zaman ve nas\u0131l eri\u015febildiklerine<\/em> g\u00f6re fark yarat\u0131yor. O y\u00fczden veri mimarisini se\u00e7erken sadece bug\u00fcn\u00fc de\u011fil, yar\u0131n\u0131 da d\u00fc\u015f\u00fcnmek gerekiyor.<\/p>\n\n\n\n

        Kimi zaman klasik, kimi zaman deneysel ama her zaman esnek ve g\u00fcvenilir bir yap\u0131 kurmak art\u0131k m\u00fcmk\u00fcn. Verinin yeni evi, disiplinle \u00f6zg\u00fcrl\u00fc\u011f\u00fcn bulu\u015ftu\u011fu yer: Lakehouse<\/em>.<\/p>\n\n\n\n

        Peki Biz Bu Durumu Bentego\u2019da Nas\u0131l Ele Al\u0131yoruz?<\/h2>\n\n\n\n

        Veri d\u00fcnyas\u0131 h\u0131zla evriliyor. Biz de Bentego<\/strong> olarak bu de\u011fi\u015fime sadece ayak uydurmakla kalm\u0131yor, ayn\u0131 zamanda kurumlar\u0131n bu d\u00f6n\u00fc\u015f\u00fcmde do\u011fru ad\u0131mlar\u0131 atmas\u0131na destek oluyoruz.<\/em><\/p>\n\n\n\n

        Her kurumun veriyle kurdu\u011fu ili\u015fki farkl\u0131. Kimi h\u00e2l\u00e2 temel raporlama ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lamaya \u00e7al\u0131\u015f\u0131yor, kimi ise \u00e7oklu veri kaynaklar\u0131n\u0131 bir araya getirerek daha tutarl\u0131 analizler \u00fcretmenin yollar\u0131n\u0131 ar\u0131yor.
        \u0130\u015fte tam bu noktada devreye giriyoruz.<\/p>\n\n\n\n

        Bentego olarak neler yap\u0131yoruz?<\/strong><\/p>\n\n\n\n

        Kurumlar\u0131n mevcut veri yap\u0131s\u0131n\u0131 analiz ediyoruz.<\/strong>\u00a0Hangi platformlar kullan\u0131l\u0131yor, ne t\u00fcr veriler toplan\u0131yor, s\u00fcre\u00e7ler ne kadar oturmu\u015f?<\/p>\n\n\n\n

        Gelecekteki ihtiya\u00e7lar\u0131n\u0131 birlikte belirliyoruz.<\/strong>\u00a0Sadece bug\u00fcn\u00fc de\u011fil, \u00f6n\u00fcm\u00fczdeki y\u0131llarda kar\u015f\u0131la\u015fabilecekleri veri ihtiya\u00e7lar\u0131n\u0131 da g\u00f6z \u00f6n\u00fcnde bulunduruyoruz.<\/p>\n\n\n\n

        En uygun veri mimarisini tasarl\u0131yoruz.<\/strong>\u00a0Bu bazen mevcut DWH yap\u0131s\u0131n\u0131 g\u00fcncellemek, bazen modern bir Lakehouse mimarisine ge\u00e7mek, bazen de hibrit bir yakla\u015f\u0131m\u0131 hayata ge\u00e7irmek anlam\u0131na geliyor.<\/p>\n\n\n\n

        Uygulama ve ge\u00e7i\u015f s\u00fcrecini birlikte y\u00fcr\u00fct\u00fcyoruz.<\/strong>\u00a0Tavsiye vermekle kalm\u0131yor, mimariyi kuruyor, devreye al\u0131yor, ekipleri s\u00fcrece d\u00e2hil ediyoruz.<\/p>\n\n\n\n

        Amac\u0131m\u0131z, kurumlar\u0131n veriye sadece sahip olmas\u0131<\/em> de\u011fil; o veriyi anlaml\u0131, eri\u015filebilir ve etkili<\/em> bi\u00e7imde kullanabilmesini sa\u011flamak.<\/p>\n\n\n\n

        Sonu\u00e7 olarak\u2026<\/h2>\n\n\n\n

        Bug\u00fcn\u00fcn veri d\u00fcnyas\u0131nda tek bir do\u011fru yok. Ama do\u011fru yakla\u015f\u0131m\u0131 birlikte bulmak m\u00fcmk\u00fcn.
        <\/strong>E\u011fer siz de veri mimarinizi yeniden \u015fekillendirmeyi d\u00fc\u015f\u00fcn\u00fcyorsan\u0131z, Bentego olarak her ad\u0131mda yan\u0131n\u0131zday\u0131z.<\/strong><\/p>\n\n\n\n

        Veri sadece depolanmak i\u00e7in de\u011fil, de\u011fer yaratmak i\u00e7in vard\u0131r.
        <\/em>Gelin birlikte, verinin ger\u00e7ek g\u00fcc\u00fcn\u00fc ortaya \u00e7\u0131karal\u0131m.<\/p>\n","protected":false},"excerpt":{"rendered":"

        Son y\u0131llarda veriyle \u00e7al\u0131\u015fan herkesin s\u0131k s\u0131k kar\u015f\u0131la\u015ft\u0131\u011f\u0131 bir soru var:\u201cVerilerimizi nerede saklamal\u0131y\u0131z?\u201dBu sorunun cevab\u0131, zaman i\u00e7inde hem teknolojilerin geli\u015fmesiyle hem de […]<\/p>\n","protected":false},"author":2,"featured_media":1011,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[95,183,185,184],"class_list":["post-6219","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-data","tag-data-lake","tag-data-lakehouse","tag-dwh"],"yoast_head":"\nDWH vs Data Lake vs Data Lakehouse: Verinin Yeni Evi Neresi - Bentego<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bentego.com\/en\/dwh-vs-data-lake-vs-data-lakehouse-verinin-yeni-evi-neresi\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DWH vs Data Lake vs Data Lakehouse: Verinin Yeni Evi Neresi - 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