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undergraduate thesis
Big Data tehnologije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. urn:nbn:hr:168:102080
Dundović, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Dundović, I. (2015). Big Data tehnologije (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:102080

Dundović, Ivan. "Big Data tehnologije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:102080

Dundović, Ivan. "Big Data tehnologije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:102080

Dundović, I. (2015). 'Big Data tehnologije', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:102080

Dundović I. Big Data tehnologije [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2015 [cited 2025 January 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:102080

I. Dundović, "Big Data tehnologije", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:168:102080

Metadata
TitleBig Data tehnologije
Title (english)Big Data technologies
AuthorIvan Dundović
MentorDamir Pintar (mentor)
Committee memberDamir Pintar (predsjednik povjerenstva)
Committee memberBoris Vrdoljak (član povjerenstva)
Committee memberMarko Banek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2015-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Veliki podatci uključuju skupove podataka koji veličinom nadilaze mogućnosti uobičajeno korištenih računalnih alata da prikupe, upravljaju i procesiraju te podatke dovoljno brzo. Definiraju ih tri dimenzije: veličina, brzina i raznovrsnost. Relacijske baze podataka pokazale su se neprikladnim za rad s velikim podatcima. Nerelacijske baze podataka, s druge strane su skalabilnije, pokazuju bolje performanse i rješavaju mnoge probleme za čije rješavanje relacijske baze podataka nisu dizajnirane, poput upravljanja velikim, često nestrukturiranim, podacima. Za rad s velikim podatcima razvijene su mnoge tehnike, tehnologije i alati, među njima i Apache Hadoop. Apache Hadoop, ili kraće Hadoop, je radni okvir za distribuirano spremanje i procesiranje velikih količina podataka na spletu običnih računala kojih može biti od jednog do nekoliko tisuća. Hadoop datotečni sustav, YARN platforma za upravljanje resursima te MapReduce programski model, koji su u radu detaljno objašnjeni, čine osnovu Hadoopa. U petom poglavlju, koristeći Hadoop radni okvir na malom spletu računala, implementiran je MapReduce program koji sortira skup cijelih brojeva iz ulazne datoteke.
Abstract (english)
Big Data usually includes data sets whose size exceeds the capabilities of commonly used computer tools to capture, manage, and process data in a reasonable amount of time. Big Data is defined as three-dimensional, with those being volume, velocity, and variety. Relational databases are inadequate for processing such large data sets. Non-relational databases, on the other hand, are more scalable, show better performance and solve many problems that relational databases are not designed to solve. Many techiques, technologies, and tools were developed to manage Big Data, one of them being Apache Hadoop. Apache Hadoop, or just Hadoop, is framework for distributed storage and processing of large data sets on computer clusters composed of one to several thousand computers built from commodity hardware. Hadoop filesystem (HDFS), YARN platform for resource management and MapReduce programming model, which are covered in the paper in detail, form the base of Hadoop. In the fifth chapter, using Hadoop framework and MapReduce programming model on a small computer cluster, MapReduce software is implemented for sorting integer numbers that are read from input file.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:102080
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
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File originBorn digital
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Created on2018-02-06 08:37:15