dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Jeuring, J.T | |
dc.contributor.author | Kfoury, A. | |
dc.date.accessioned | 2021-08-26T18:00:21Z | |
dc.date.available | 2021-08-26T18:00:21Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/41256 | |
dc.description.abstract | In a world where data is getting bigger and time is valuable, new needs arise to analyze big data in a timely manner while keeping costs controlled. There are certainly existing methods and frameworks to do so. However, these methods present their own drawbacks and weaknesses like compatibility or scalability. There are currently hardly any methods or frameworks that tackle cost efficiency, performance, scalability, and compatibility all at once. In collaboration with Core Life Analytics on a graduation project, this thesis aims to fulfill or help accomplish that need. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 3758858 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | A Framework to Process Large Quantities of Data Using Cloud Computing | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | AWS, Amazon Web Services, Parallelization, Cloud Computing, | |
dc.subject.courseuu | Business Informatics | |