The costs and benefits of turning data into information using big data systems

Abstract

This thesis explains problems of and solutions for storing and processing big volumes and streaming types of data in a cost-e ective way for enterprise companies. There are several problems like operational, infrastructural and usability problems about these new concepts of Big Data. The basic data processing concepts are not new, but the data generation volumes and velocities are pushing the limits of centralized architectures. Distributed systems using distributed programming models such as the Hadoop framework are used today to handle big data problems. This thesis will try to combine the structural, architectural and nancial issues to address big data storage and processing problems and will give practical examples based on real-life experiences from several big data applications in di erent sectors including mobile telecommunications, nance and oil-gas elds.

Bu tez kurumsal rmalardaki b uy uk miktarlardaki verinin, i slenmeside ve saklanmas ndaki zorluklar ve c oz umlerini a c klmaktad r. Yeni ortaya ckan b uy uk veri kavram nda i sletimsel, mimari ve kullan msal olarak bir cok problem bulunmaktad r. Temel veri i sleme y ontemleri yeni olmamas na kar s n, olu san veri miktar ve h z , merkezi mimarilerin s n rlar n zorlamaktad r. Hadoop gibi, da g t k i sleme modellerini kullanan, da g t k mimariler g un um uzde b uy uk verinin i slenmesi ve saklanmas nda anahtar rol oynamaktad rlar. Bu tez b uy uk veriyi i slemede, yap sal, mimari ve nansal olarak sorun te skil eden durumlar n ger cek hayat deneyimleri uzerinden, telekominikasyon, nans ve petrol ra nerileri gibi sekt or bazl c oz umlerini a c klamay ama clamaktad r.