{"id":476666,"date":"2023-08-09T07:31:20","date_gmt":"2023-08-09T07:31:20","guid":{"rendered":""},"modified":"2023-09-05T11:13:12","modified_gmt":"2023-09-05T11:13:12","slug":"data-mapping","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/data-mapping\/","title":{"rendered":"Veri haritalama"},"content":{"rendered":"<p>Veri e\u015fleme, farkl\u0131 veri modelleri aras\u0131nda ba\u011flant\u0131 kuran \u00e7ok say\u0131da veri y\u00f6netimi i\u015fleminde kritik bir prosed\u00fcrd\u00fcr. Bir sistem veya formattaki verilerin anla\u015f\u0131lmas\u0131n\u0131, \u00e7evrilmesini ve ba\u015fka bir sistem veya formata aktar\u0131lmas\u0131n\u0131 sa\u011flayan \u00f6nemli bir s\u00fcre\u00e7tir. Bu i\u015flev, farkl\u0131 veri yap\u0131lar\u0131na sahip sistemleri birle\u015ftirirken veya farkl\u0131 veritabanlar\u0131 aras\u0131nda ba\u011flant\u0131 kurmaya \u00e7al\u0131\u015f\u0131rken \u00f6zellikle \u00f6nemlidir.<\/p>\n<h2>Veri Haritalaman\u0131n Evrimi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Veri e\u015fleme kavram\u0131n\u0131n k\u00f6kleri, verileri \u00e7e\u015fitli formatlar ve sistemler aras\u0131nda \u00e7evirmenin \u00e7ok \u00f6nemli oldu\u011fu veritaban\u0131 teknolojisinin ilk g\u00fcnlerine dayanmaktad\u0131r. Veri haritalaman\u0131n ilk s\u00f6z\u00fc, veritaban\u0131 y\u00f6netim sistemlerinin ortaya \u00e7\u0131k\u0131\u015f\u0131yla ayn\u0131 zamana denk gelen 1960&#039;lara kadar uzan\u0131yor. Verilerin sistemler aras\u0131nda ve tek sistem \u00e7er\u00e7evesinde sorunsuz bir \u015fekilde aktar\u0131lmas\u0131n\u0131 gerektiren yaz\u0131l\u0131m uygulamalar\u0131n\u0131n artmas\u0131yla birlikte veri haritalama ihtiyac\u0131 daha da belirgin hale geldi. Y\u0131llar ge\u00e7tik\u00e7e bu s\u00fcre\u00e7, geli\u015fmi\u015f haritalama ara\u00e7lar\u0131 ve algoritmalar\u0131n yard\u0131m\u0131yla manuel, s\u0131k\u0131c\u0131 bir g\u00f6revden otomatikle\u015ftirilmi\u015f bir g\u00f6reve d\u00f6n\u00fc\u015ft\u00fc.<\/p>\n<h2>Konuyu A\u00e7mak: Veri E\u015fleme Nedir?<\/h2>\n<p>Veri e\u015fleme, veri entegrasyonu g\u00f6revlerinin temel ta\u015f\u0131d\u0131r. Bir kaynak sistem veya veritaban\u0131ndaki veri alanlar\u0131n\u0131n, hedef sistem veya veritaban\u0131ndaki kar\u015f\u0131l\u0131k gelen alanlarla e\u015fle\u015ftirildi\u011fi s\u00fcre\u00e7tir. Temelde, kaynak sistemdeki verilerin hedef sistemin yap\u0131s\u0131na veya format\u0131na uyacak \u015fekilde nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi veya manip\u00fcle edilmesi gerekti\u011fini anlatan bir &#039;\u00e7eviri k\u0131lavuzu&#039; g\u00f6revi g\u00f6r\u00fcr.<\/p>\n<p>Veri e\u015fleme s\u00fcreci a\u015fa\u011f\u0131dakiler gibi \u00e7e\u015fitli ad\u0131mlar\u0131 i\u00e7erir:<\/p>\n<ol>\n<li>\n<p><strong>Kaynak ve Hedef Sistemlerin Tan\u0131mlanmas\u0131:<\/strong> Veri haritalaman\u0131n ilk ad\u0131m\u0131 kaynak ve hedef sistemleri tan\u0131mlamakt\u0131r. Kaynak sistem orijinal verinin depoland\u0131\u011f\u0131 yerdir, hedef sistem ise verinin aktar\u0131lmas\u0131 gereken yerdir.<\/p>\n<\/li>\n<li>\n<p><strong>Veri Alanlar\u0131n\u0131 Tan\u0131mlama:<\/strong> Bir sonraki ad\u0131m, hem kaynak hem de hedef sistemlerdeki belirli veri alanlar\u0131n\u0131 tan\u0131mlamakt\u0131r. Bu alanlar adlar, adresler, e-posta kimlikleri ve di\u011fer ilgili veriler gibi \u00e7e\u015fitli veri t\u00fcrlerini i\u00e7erebilir.<\/p>\n<\/li>\n<li>\n<p><strong>E\u015fleme Kurallar\u0131 Olu\u015fturma:<\/strong> Veri alanlar\u0131n\u0131 belirledikten sonraki ad\u0131m, kaynak sistemdeki verilerin hedef sisteme ta\u015f\u0131nd\u0131\u011f\u0131nda nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi gerekti\u011fini tan\u0131mlayan e\u015fleme kurallar\u0131n\u0131n olu\u015fturulmas\u0131d\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>Test ve Do\u011frulama:<\/strong> E\u015fleme kurallar\u0131 olu\u015fturulduktan sonra, verilerin do\u011fru \u015fekilde d\u00f6n\u00fc\u015ft\u00fcr\u00fcld\u00fc\u011f\u00fcnden ve hedef sisteme aktar\u0131ld\u0131\u011f\u0131ndan emin olmak i\u00e7in bunlar\u0131n test edilmesi ve do\u011frulanmas\u0131 gerekir.<\/p>\n<\/li>\n<\/ol>\n<h2>Veri Haritalaman\u0131n Anatomisi: Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Veri e\u015fleme \u00f6z\u00fcnde, kullan\u0131c\u0131 veya veri bilimcisi taraf\u0131ndan tan\u0131mlanan ve bir sistemden (kaynaktan) gelen verilerin ba\u015fka bir sisteme (hedefe) aktar\u0131ld\u0131\u011f\u0131nda nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fi veya \u00e7evrilece\u011fi talimat\u0131n\u0131 veren kurallar veya y\u00f6nergeler \u00fczerinde \u00e7al\u0131\u015f\u0131r. Bu kurallar basit &#039;kopyalama&#039; talimatlar\u0131ndan hesaplamalar\u0131, birle\u015ftirmeleri veya di\u011fer i\u015flemleri i\u00e7erebilecek daha karma\u015f\u0131k d\u00f6n\u00fc\u015f\u00fcmlere kadar de\u011fi\u015febilir.<\/p>\n<p>Veri e\u015fleme genellikle \u00fc\u00e7 temel a\u015famadan ge\u00e7er:<\/p>\n<ol>\n<li>\n<p><strong>Kaynak Analizi:<\/strong> Bu a\u015famada kaynak verinin yap\u0131s\u0131 ve anlambilimi de\u011ferlendirilir.<\/p>\n<\/li>\n<li>\n<p><strong>D\u00f6n\u00fc\u015f\u00fcm:<\/strong> Bu a\u015fama, hedef sistemin yap\u0131s\u0131na ve gereksinimlerine uyacak \u015fekilde \u00f6nceden tan\u0131mlanm\u0131\u015f kurallara dayal\u0131 olarak verilerin fiili manip\u00fclasyonunu i\u00e7erir.<\/p>\n<\/li>\n<li>\n<p><strong>Y\u00fckleniyor:<\/strong> Son a\u015famada d\u00f6n\u00fc\u015ft\u00fcr\u00fclen veriler hedef sisteme y\u00fcklenir.<\/p>\n<\/li>\n<\/ol>\n<h2>Veri E\u015flemenin Temel \u00d6zellikleri<\/h2>\n<p>Veri e\u015fleme birka\u00e7 ay\u0131rt edici \u00f6zellik ile karakterize edilir:<\/p>\n<ul>\n<li><strong>Uyumluluk:<\/strong> Farkl\u0131 veri sistemlerinin ileti\u015fim kurmas\u0131na olanak tan\u0131yarak verilerin birlikte \u00e7al\u0131\u015fabilirli\u011fini sa\u011flar.<\/li>\n<li><strong>Veri D\u00f6n\u00fc\u015f\u00fcm\u00fc:<\/strong> Veriyi tan\u0131mlanm\u0131\u015f kurallara g\u00f6re d\u00f6n\u00fc\u015ft\u00fcrerek hedef sisteme uygun hale getirebilir.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik:<\/strong> Modern veri e\u015fleme ara\u00e7lar\u0131 b\u00fcy\u00fck hacimli verileri i\u015fleyebilir ve bu da onlar\u0131 \u00f6l\u00e7eklenebilir hale getirir.<\/li>\n<li><strong>Tan\u0131mlama hatas\u0131:<\/strong> Verilerdeki tutars\u0131zl\u0131klar\u0131 veya hatalar\u0131 tespit edebilir ve veri temizli\u011fine yard\u0131mc\u0131 olabilir.<\/li>\n<li><strong>Otomatik S\u00fcre\u00e7:<\/strong> \u00c7o\u011fu modern veri haritalama arac\u0131, otomatik veri haritalamaya izin vererek manuel m\u00fcdahaleyi azalt\u0131r ve verimlili\u011fi art\u0131r\u0131r.<\/li>\n<\/ul>\n<h2>Veri E\u015fleme T\u00fcrleri<\/h2>\n<p>Veri e\u015fleme, karma\u015f\u0131kl\u0131\u011fa ve gereken d\u00f6n\u00fc\u015f\u00fcm d\u00fczeyine ba\u011fl\u0131 olarak \u00e7e\u015fitli t\u00fcrlere ayr\u0131labilir:<\/p>\n<ol>\n<li>\n<p><strong>Do\u011frudan Haritalama:<\/strong> Bu, kaynak ve hedef alanlar aras\u0131nda basit, bire bir yaz\u0131\u015fmay\u0131 i\u00e7erir. Hi\u00e7bir d\u00f6n\u00fc\u015f\u00fcme gerek yoktur.<\/p>\n<\/li>\n<li>\n<p><strong>D\u00f6n\u00fc\u015f\u00fcm Haritalamas\u0131:<\/strong> Bu, bir veya daha fazla kaynak alandan gelen verilerin hedef alana uyacak \u015fekilde de\u011fi\u015ftirildi\u011fi karma\u015f\u0131k d\u00f6n\u00fc\u015f\u00fcmleri i\u00e7erir.<\/p>\n<\/li>\n<li>\n<p><strong>Karma\u015f\u0131k Haritalama:<\/strong> Bu, kaynak verileri hedef yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in birden fazla kural veya i\u015flemin kullan\u0131lmas\u0131n\u0131 i\u00e7erir.<\/p>\n<\/li>\n<\/ol>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Karma\u015f\u0131kl\u0131k D\u00fczeyi<\/th>\n<th>D\u00f6n\u00fc\u015f\u00fcm Gerekli<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Do\u011frudan Haritalama<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>HAYIR<\/td>\n<\/tr>\n<tr>\n<td>D\u00f6n\u00fc\u015f\u00fcm Haritalamas\u0131<\/td>\n<td>Orta<\/td>\n<td>Evet<\/td>\n<\/tr>\n<tr>\n<td>Karma\u015f\u0131k Haritalama<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Evet<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Veri E\u015flemede Kullan\u0131m Durumlar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>Veri e\u015fleme, veri entegrasyonu, veri ge\u00e7i\u015fi, veri ambar\u0131 ve ETL (\u00c7\u0131karma, D\u00f6n\u00fc\u015ft\u00fcrme, Y\u00fckleme) s\u00fcre\u00e7leri gibi \u00e7ok say\u0131da senaryoda uygulama alan\u0131 bulur. Verilerin belirli formatlarda do\u011fru \u015fekilde raporlanmas\u0131 gereken uyumluluk senaryolar\u0131nda da kritik \u00f6neme sahiptir.<\/p>\n<p>Veri haritalamadaki yayg\u0131n zorluklar \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>Verilerin Karma\u015f\u0131kl\u0131\u011f\u0131:<\/strong> Veriler genellikle karma\u015f\u0131k ve yap\u0131land\u0131r\u0131lmam\u0131\u015f olabilir, bu da haritalamay\u0131 zorlu bir g\u00f6rev haline getirir.<\/li>\n<li><strong>Veri Hacmi:<\/strong> B\u00fcy\u00fck hacimli veriler haritalama s\u00fcrecini karma\u015f\u0131kla\u015ft\u0131rabilir ve daha uzun i\u015flem s\u00fcrelerine yol a\u00e7abilir.<\/li>\n<li><strong>Veri do\u011frulu\u011fu:<\/strong> Verilerdeki hatalar, yanl\u0131\u015f haritalamaya ve ard\u0131ndan yanl\u0131\u015f analiz veya raporlamaya yol a\u00e7abilir.<\/li>\n<\/ul>\n<p>Modern veri haritalama ara\u00e7lar\u0131n\u0131n, makine \u00f6\u011freniminin ve yapay zekan\u0131n ortaya \u00e7\u0131k\u0131\u015f\u0131, bu zorluklara \u00e7\u00f6z\u00fcm bulunmas\u0131n\u0131 sa\u011flad\u0131. Bu ara\u00e7lar karma\u015f\u0131k, yap\u0131land\u0131r\u0131lmam\u0131\u015f verileri i\u015fleyebilir, b\u00fcy\u00fck hacimli verileri verimli bir \u015fekilde i\u015fleyebilir ve verilerdeki hatalar\u0131 tan\u0131mlay\u0131p d\u00fczeltebilir.<\/p>\n<h2>Veri E\u015flemeyi Benzer Kavramlarla Kar\u015f\u0131la\u015ft\u0131rma<\/h2>\n<p>Veri e\u015fleme, di\u011fer veri y\u00f6netimi s\u00fcre\u00e7leriyle ortak noktalara sahiptir ancak belirli i\u015flevleri nedeniyle \u00f6ne \u00e7\u0131kar:<\/p>\n<table>\n<thead>\n<tr>\n<th>Konsept<\/th>\n<th>Ana \u0130\u015flevsellik<\/th>\n<th>Veri E\u015fleme ile Benzerlikler<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Veri D\u00f6n\u00fc\u015f\u00fcm\u00fc<\/td>\n<td>Verileri belirli bir yap\u0131ya uyacak \u015fekilde de\u011fi\u015ftirme<\/td>\n<td>Her ikisi de verinin format\u0131n\u0131 veya yap\u0131s\u0131n\u0131 de\u011fi\u015ftirmeyi i\u00e7erir<\/td>\n<\/tr>\n<tr>\n<td>Veri g\u00f6\u00e7\u00fc<\/td>\n<td>Verileri bir sistemden di\u011ferine ta\u015f\u0131ma<\/td>\n<td>Her ikisi de bir kaynaktan hedefe veri aktar\u0131m\u0131n\u0131 i\u00e7erir<\/td>\n<\/tr>\n<tr>\n<td>Veri Entegrasyonu<\/td>\n<td>Farkl\u0131 kaynaklardan gelen verileri birle\u015fik bir g\u00f6r\u00fcn\u00fcmde birle\u015ftirme<\/td>\n<td>Her ikisi de farkl\u0131 sistemlerden verilerin birle\u015ftirilmesini i\u00e7erir<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Veri Haritalamada Gelecek Perspektifleri ve Teknolojiler<\/h2>\n<p>Veri ortam\u0131 daha karma\u015f\u0131k hale geldik\u00e7e veri haritalaman\u0131n rol\u00fc geni\u015flemeye ve geli\u015fmeye devam ediyor. Yapay zeka ve makine \u00f6\u011freniminin y\u00fckseli\u015fiyle birlikte, karma\u015f\u0131k veri yap\u0131lar\u0131n\u0131 ve b\u00fcy\u00fck hacimli verileri kolayl\u0131kla i\u015fleyebilen daha karma\u015f\u0131k, otomatikle\u015ftirilmi\u015f veri haritalama ara\u00e7lar\u0131n\u0131 \u00f6ng\u00f6rebiliriz. Verilerin an\u0131nda d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesine ve y\u00fcklenmesine olanak tan\u0131yan geli\u015fmi\u015f ak\u0131\u015f teknolojilerinin sa\u011flad\u0131\u011f\u0131 ger\u00e7ek zamanl\u0131 veri haritalamas\u0131na y\u00f6nelik de b\u00fcy\u00fcyen bir e\u011filim var.<\/p>\n<h2>Proxy Sunucular\u0131n\u0131n Etkile\u015fimi ve Veri E\u015fleme<\/h2>\n<p>Proxy sunucular\u0131 dolayl\u0131 olarak veri e\u015flemeye ba\u011flanabilir. Proxy sunucusu, kaynak arayan istemci ile bu kaynaklar\u0131 sa\u011flayan sunucu aras\u0131nda arac\u0131 g\u00f6revi g\u00f6r\u00fcr. Veri a\u00e7\u0131s\u0131ndan zengin uygulamalarla u\u011fra\u015f\u0131rken, farkl\u0131 sunuculardan al\u0131nan verilerin istemci uygulamas\u0131 taraf\u0131ndan t\u00fcketilebilmesinden \u00f6nce entegre edilmesi veya ortak bir formata d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi gerekebilir. Burada veri e\u015fleme \u00f6nemli bir rol oynar.<\/p>\n<p>Ek olarak, e\u015fleme i\u015flemi bazen hassas verileri i\u00e7erebilece\u011finden, proxy sunucular veri aktar\u0131m\u0131 s\u0131ras\u0131nda ekstra bir g\u00fcvenlik katman\u0131 sa\u011flayabilir. Proxy sunucusu, trafi\u011fi anonimle\u015ftirerek, verileri \u015fifreleyerek ve veri aktar\u0131m\u0131 i\u00e7in g\u00fcvenli bir t\u00fcnel sa\u011flayarak bu verilerin korunmas\u0131na yard\u0131mc\u0131 olabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ol>\n<li><a href=\"https:\/\/www.talend.com\/resources\/what-is-data-mapping\/\" target=\"_new\" rel=\"noopener nofollow\">Veri E\u015flemeye Giri\u015f<\/a><\/li>\n<li><a href=\"https:\/\/www.datamation.com\/big-data\/data-mapping-for-dummies.html\" target=\"_new\" rel=\"noopener nofollow\">Yeni Ba\u015flayanlar i\u00e7in Veri E\u015fleme<\/a><\/li>\n<li><a href=\"https:\/\/www.sciencedirect.com\/topics\/computer-science\/data-mapping\" target=\"_new\" rel=\"noopener nofollow\">Veri E\u015flemeye Ayr\u0131nt\u0131l\u0131 Genel Bak\u0131\u015f<\/a><\/li>\n<li><a href=\"https:\/\/www.guru99.com\/etl-extract-load-process.html\" target=\"_new\" rel=\"noopener nofollow\">ETL S\u00fcrecinde Veri E\u015fleme<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/blog\/\" target=\"_new\" rel=\"noopener\">Veri Koruma i\u00e7in Proxy Sunucular\u0131n\u0131n Kullan\u0131m\u0131<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468117,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476666","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Data Mapping: An Indispensable Component of Data Management<\/mark>","faq_items":[{"question":"What is data mapping?","answer":"<p>Data mapping is a critical procedure in numerous data management operations that establishes a connection between distinct data models. It's an essential process that allows data from one system or format to be understood, translated, and transferred into another system or format.<\/p>"},{"question":"What is the history of data mapping?","answer":"<p>The concept of data mapping has its roots in the early days of database technology, around the 1960s, where it was crucial to translate data between various formats and systems. Over the years, this process has evolved from a manual, tedious task to an automated one, with the help of sophisticated mapping tools and algorithms.<\/p>"},{"question":"How does data mapping work?","answer":"<p>Data mapping works through rules or guidelines, which instruct how data from one system (source) is to be transformed or translated when it is transferred to another system (target). It usually works through three key stages: source analysis, transformation, and loading.<\/p>"},{"question":"What are the key features of data mapping?","answer":"<p>Key features of data mapping include compatibility (it allows different data systems to communicate), data transformation (it can transform data based on defined rules), scalability (modern data mapping tools can handle large volumes of data), error identification (it can identify discrepancies or errors in data), and automation (most modern data mapping tools allow for automated data mapping).<\/p>"},{"question":"What types of data mapping exist?","answer":"<p>Data mapping can be categorized into several types such as direct mapping (simple, one-to-one correspondence between source and target fields), transformation mapping (complex transformations where data from one or more source fields is manipulated to fit the target field), and complex mapping (using multiple rules or operations to transform source data to the target structure).<\/p>"},{"question":"How is data mapping used and what are the related problems and solutions?","answer":"<p>Data mapping finds application in numerous scenarios like data integration, data migration, data warehousing, and ETL processes. Challenges in data mapping include the complexity of data, large data volumes, and data accuracy. Modern data mapping tools, machine learning, and artificial intelligence have enabled solutions to these challenges.<\/p>"},{"question":"What is the future of data mapping?","answer":"<p>With the rise of AI and machine learning, we can anticipate more sophisticated, automated data mapping tools that can handle complex data structures and large volumes of data with ease. There is also a growing trend towards real-time data mapping, enabled by advanced streaming technologies.<\/p>"},{"question":"How are proxy servers associated with data mapping?","answer":"<p>Proxy servers can be indirectly linked to data mapping. When dealing with data-rich applications, the data retrieved from different servers might need to be integrated or transformed to a common format. Here, data mapping plays a key role. Proxy servers can provide an extra layer of security during data transfer, as the mapping process may sometimes involve sensitive data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476666\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468117"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}