{"id":30088,"date":"2026-02-17T17:02:35","date_gmt":"2026-02-17T17:02:35","guid":{"rendered":"https:\/\/nekudos.com\/sap-table-growth-yonetimi\/"},"modified":"2026-02-17T17:02:50","modified_gmt":"2026-02-17T17:02:50","slug":"sap-table-growth-yonetimi","status":"publish","type":"post","link":"https:\/\/nekudos.com\/en\/sap-table-growth-yonetimi\/","title":{"rendered":"SAP Table Growth Y\u00f6netimiyle Performans ve Veri Verimlili\u011fi Art\u0131rma"},"content":{"rendered":"

SAP Table Growth Nas\u0131l Engellenir<\/strong><\/h1>\n
\n

Giri\u015f<\/strong><\/h3>\n

SAP sistemlerinde tablo b\u00fcy\u00fcmesi (sap table growth), artan i\u015flem hacmi ve veri saklama s\u00fcrelerinin uzamas\u0131yla birlikte ciddi bir performans riski olu\u015fturur. Database growth y\u00f6netilemez hale geldi\u011finde hem sistem kaynaklar\u0131 zorlan\u0131r hem de analiz s\u00fcre\u00e7leri yava\u015flar. Bu i\u00e7erikte, SAP tablosu b\u00fcy\u00fcmesini \u00f6nlemenin teknik yollar\u0131n\u0131, sistem parametrelerini ve kurumsal d\u00fczeyde al\u0131nabilecek \u00f6nlemleri ele al\u0131yoruz.<\/p>\n

\n

SAP Table Growth Nas\u0131l Engellenir tan\u0131m\u0131<\/strong><\/h3>\n

Sap table growth, SAP veritaban\u0131ndaki tablolar\u0131n gereksiz veya kontrols\u00fcz \u015fekilde b\u00fcy\u00fcmesini ifade eder. Bu b\u00fcy\u00fcme tipik olarak operasyonel verilerin ar\u015fivlenmemesi, indekslerin optimize edilmemesi veya sistemdeki i\u015flem kay\u0131tlar\u0131n\u0131n zamanla birikmesiyle ortaya \u00e7\u0131kar. Database growth’un kontrol alt\u0131nda tutulmas\u0131, sistem kararl\u0131l\u0131\u011f\u0131 ve uzun vadeli veri y\u00f6netim stratejileri i\u00e7in zorunludur.<\/p>\n

\n

sap table growth nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

SAP sisteminde her i\u015flem farkl\u0131 tablolara veri yazar. Zaman i\u00e7inde, bu tablolar log, i\u015flem, belge veya konfig\u00fcrasyon verileriyle dolarak b\u00fcy\u00fcr. Her tablo SAP\u2019nin \u201ctablespace\u201d yap\u0131s\u0131 alt\u0131nda fiziksel disk alan\u0131 kullan\u0131r. Table growth s\u00fcrecini anlamak, sistem parametrelerinin do\u011fru konfig\u00fcre edilmesini gerektirir.<\/p>\n

\n

Temel parametreler ve ayarlar<\/strong><\/h3>\n

Performans optimizasyonu i\u00e7in data aging<\/em>, archiving object<\/em>, table partitioning<\/em> ve buffering<\/em> parametreleri do\u011fru tan\u0131mlanmal\u0131d\u0131r. <\/p>\n

    \n
  • Data aging<\/strong>: HANA sistemlerinde verilerin ya\u015f\u0131na g\u00f6re bellekte tutuldu\u011fu s\u00fcreyi tan\u0131mlar. <\/li>\n
  • Partitioning<\/strong>: B\u00fcy\u00fck tablolar\u0131n fiziksel olarak b\u00f6l\u00fcnmesini sa\u011flar. <\/li>\n
  • Clean-up jobs<\/strong>: Sistem d\u00f6ng\u00fcs\u00fcnde gereksiz ge\u00e7ici kay\u0131tlar\u0131 siler. <\/li>\n<\/ul>\n

    Bu ayarlar\u0131n do\u011fru yap\u0131land\u0131r\u0131lmas\u0131 table growth\u2019un kontrol alt\u0131na al\u0131nmas\u0131nda temel ad\u0131md\u0131r.<\/p>\n

    \n

    S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h3>\n

    Yayg\u0131n bir hata, verilerin uzun s\u00fcreli i\u015flem tablosunda tutulmas\u0131d\u0131r. Bunun sonucu, tablo indekslerinin verimsiz hale gelmesi ve sorgu s\u00fcrelerinin uzamas\u0131d\u0131r.
    \nKa\u00e7\u0131nmak i\u00e7in ar\u015fivleme politikalar\u0131, job scheduling<\/em> ve data tiering<\/em> stratejileri birlikte kullan\u0131lmal\u0131d\u0131r. Verinin hangi seviyede ne kadar s\u00fcre tutulaca\u011f\u0131 sistematik olarak belirlenmelidir.<\/p>\n

    \n

    Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h3>\n

    Bir SAP ERP sisteminde FI_DOCLINE tablosu y\u0131llar i\u00e7inde h\u0131zla b\u00fcy\u00fcyebilir. Bu tabloya data aging rules<\/em> tan\u0131mlanarak son 12 aydan eski kay\u0131tlar ar\u015fiv katman\u0131na ta\u015f\u0131nabilir. Ayn\u0131 s\u00fcre\u00e7te background clean-up jobs<\/em> \u00e7al\u0131\u015ft\u0131r\u0131larak gereksiz log kay\u0131tlar\u0131 silinir. Bu yakla\u015f\u0131m database growth s\u00fcrecini dengeler, sistem performans\u0131n\u0131 optimize eder.<\/p>\n

    \n

    Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

    Table growth kontrol\u00fc mimari d\u00fczeyde, SAP HANA \u00fczerinde veri katmanlama (hot, warm, cold) stratejileriyle uygulan\u0131r. Hot veriler RAM \u00fczerinde tutulurken, cold veriler disk bazl\u0131 ar\u015fiv sistemlerinde saklan\u0131r.
    \nVeri ak\u0131\u015f mant\u0131\u011f\u0131 a\u00e7\u0131s\u0131ndan, ILM framework<\/em> (Information Lifecycle Management) kullan\u0131larak tablo eri\u015fim desenleri belirlenir. B\u00f6ylece sistem sorgular\u0131 eski veriyi hedeflemeden sadece operasyonel veri \u00fczerinde \u00e7al\u0131\u015f\u0131r.
    \nBu yakla\u015f\u0131m, veri okuma s\u00fcre\u00e7lerinde CPU t\u00fcketimini azalt\u0131r, storage kullan\u0131m\u0131n\u0131 dengeler ve SAP BTP \u00fczerindeki entegrasyon senaryolar\u0131nda i\u015flem maliyetini d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n

    \n

    \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
      \n
    • Performans:<\/strong> Gereksiz tablo b\u00fcy\u00fcmesi sorgu s\u00fcrelerini art\u0131r\u0131r. <\/li>\n
    • G\u00fcvenilirlik:<\/strong> Disk doluluk oran\u0131 y\u00fcksek sistemlerde veri b\u00fct\u00fcnl\u00fc\u011f\u00fc riski artar. <\/li>\n
    • Maliyet:<\/strong> Database growth, depolama maliyetlerini do\u011frudan y\u00fckseltir. <\/li>\n
    • \u00d6l\u00e7ekleme:<\/strong> Optimize edilmemi\u015f tablolar sistem geni\u015flemesini zorla\u015ft\u0131r\u0131r. <\/li>\n
    • Otomasyon:<\/strong> S\u00fcrekli b\u00fcy\u00fcyen tablolar, job scheduling yap\u0131s\u0131n\u0131n kararl\u0131l\u0131\u011f\u0131n\u0131 bozar. <\/li>\n
    • Karar alma:<\/strong> Analitik sistemler gereksiz veriyle doldu\u011funda raporlamalar gecikir. <\/li>\n
    • Operasyonel verimlilik:<\/strong> Temiz veri katmanlar\u0131yla \u00e7al\u0131\u015fan sistemlerde bak\u0131m ve g\u00fcncelleme i\u015flemleri \u00e7ok daha h\u0131zl\u0131d\u0131r.<\/li>\n<\/ul>\n
      \n

      Bu kavram NeKuDos Teknoloji i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

      NeKuDos Teknoloji, geli\u015ftirdi\u011fi SAP entegrasyon ve dan\u0131\u015fmanl\u0131k modellerinde table growth y\u00f6netimini mimari bir bile\u015fen olarak ele al\u0131r. Her projede ILM politikalar\u0131, veri ya\u015f\u0131 algoritmalar\u0131 ve BTP tabanl\u0131 proses otomasyonu birlikte tasarlan\u0131r.
      \nUzun vadeli SAP BTP kullan\u0131mlar\u0131nda veri ya\u015f\u0131 parametresi, i\u015flem yo\u011funlu\u011fu tahminine g\u00f6re yeniden hesaplan\u0131r. B\u00f6ylece NeKuDos Teknoloji\u2019nin sistem tasar\u0131mlar\u0131, tablo b\u00fcy\u00fcmesini \u00f6nleyerek s\u00fcrd\u00fcr\u00fclebilir veri yap\u0131lar\u0131 sa\u011flar.<\/p>\n

      \n

      SAP dan\u0131\u015fmanlar\u0131, IT y\u00f6neticileri, CTOlar i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
        \n
      1. Sorun:<\/strong> Bir \u00fcretim \u015firketinde SD tablosu son bir y\u0131lda %60 b\u00fcy\u00fcm\u00fc\u015ft\u00fcr. <\/li>\n
      2. Ba\u011flam:<\/strong> SAP HANA sistemi \u00fczerinde raporlar yava\u015f \u00e7al\u0131\u015fmakta, database growth kontrols\u00fcz durumdad\u0131r. <\/li>\n
      3. Kavram\u0131n uygulanmas\u0131:<\/strong> Dan\u0131\u015fman ekip data aging<\/em> kurallar\u0131n\u0131 devreye al\u0131r, tabloyu partitioning ile b\u00f6l\u00fcmlere ay\u0131r\u0131r. <\/li>\n
      4. Sonu\u00e7:<\/strong> Tablo eri\u015fim s\u00fcresi %40 h\u0131zlan\u0131r, disk kullan\u0131m\u0131 %25 azal\u0131r. <\/li>\n
      5. \u0130\u015f etkisi:<\/strong> Raporlama ve analiz s\u00fcre\u00e7leri h\u0131zlan\u0131r; karar alma d\u00f6ng\u00fcleri operasyonel hale gelir. <\/li>\n<\/ol>\n

        Bu gibi senaryolarda sap table growth\u2019un sistematik kontrol\u00fc \u015firketlerin uzun vadeli veri y\u00f6netim politikas\u0131nda kritik rol oynar.<\/p>\n

        \n

        S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n

        Yayg\u0131n hatalar:<\/strong> <\/p>\n

          \n
        • Ar\u015fivleme objelerinin tan\u0131mlanmamas\u0131 <\/li>\n
        • Index g\u00fcncellemelerinin g\u00f6z ard\u0131 edilmesi <\/li>\n
        • Transaction log\u2019lar\u0131n\u0131n temizlenmemesi <\/li>\n<\/ul>\n

          En iyi uygulamalar:<\/strong> <\/p>\n

            \n
          • ILM framework kullan\u0131m\u0131 <\/li>\n
          • SAP HANA data tiering stratejilerinin uygulanmas\u0131 <\/li>\n
          • Otomatik ar\u015fivleme job\u2019lar\u0131n\u0131n olu\u015fturulmas\u0131 <\/li>\n
          • SAP BTP \u00fczerinde real-time izleme dashboard\u2019lar\u0131n\u0131n entegrasyonu <\/li>\n<\/ul>\n
            \n

            Sonu\u00e7<\/strong><\/h3>\n

            SAP table growth y\u00f6netimi, veri mimarisi ve sistem s\u00fcreklili\u011fi a\u00e7\u0131s\u0131ndan temel bir konudur. Do\u011fru parametreler, zaman\u0131nda ar\u015fivleme ve yap\u0131sal optimizasyonlar hem database growth riskini azalt\u0131r hem de operasyonel verimlili\u011fi art\u0131r\u0131r.
            \nNeKuDos Teknoloji\u2019nin yakla\u015f\u0131m\u0131, bu s\u00fcreci yaln\u0131zca teknik bir i\u015flem de\u011fil, kurumsal veri y\u00f6netiminin s\u00fcrd\u00fcr\u00fclebilir bile\u015feni olarak modeller. SAP ekosisteminde tablo b\u00fcy\u00fcmesini \u00f6nlemek, i\u015f s\u00fcreklili\u011fi ve performans i\u00e7in stratejik bir zorunluluktur.<\/p>","protected":false},"excerpt":{"rendered":"

            SAP Table Growth Nas\u0131l Engellenir Giri\u015f SAP sistemlerinde tablo b\u00fcy\u00fcmesi (sap table growth), artan i\u015flem hacmi ve veri saklama s\u00fcrelerinin uzamas\u0131yla birlikte ciddi bir performans riski olu\u015fturur. Database growth y\u00f6netilemez hale geldi\u011finde hem sistem kaynaklar\u0131 zorlan\u0131r hem de analiz s\u00fcre\u00e7leri yava\u015flar. Bu i\u00e7erikte, SAP tablosu b\u00fcy\u00fcmesini \u00f6nlemenin teknik yollar\u0131n\u0131, sistem parametrelerini ve kurumsal d\u00fczeyde al\u0131nabilecek…<\/p>","protected":false},"author":2,"featured_media":30089,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[122],"tags":[],"class_list":["post-30088","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sap","category-122","description-off"],"yoast_head":"\nSAP Table Growth Y\u00f6netimiyle Performans ve Veri Verimlili\u011fi Art\u0131rma - NeKuDos Teknoloji<\/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:\/\/nekudos.com\/en\/sap-table-growth-yonetimi\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAP Table Growth Y\u00f6netimiyle Performans ve Veri Verimlili\u011fi Art\u0131rma - NeKuDos Teknoloji\" \/>\n<meta property=\"og:description\" content=\"SAP Table Growth Nas\u0131l Engellenir Giri\u015f SAP sistemlerinde tablo b\u00fcy\u00fcmesi (sap table growth), artan i\u015flem hacmi ve veri saklama s\u00fcrelerinin uzamas\u0131yla birlikte ciddi bir performans riski olu\u015fturur. 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