High performance data processing systems in cloud
Whenever the term “Big Data” was mentioned, it was often closely associated with technologies like Apache Hadoop and the “NoSQL” class of databases such as MongoDB and Neo4j. It was possible to stream real-time data analytics using these technologies with ease and these analytics usually accomplishe...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2015
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| Online Access: | http://hdl.handle.net/10356/62851 |
| _version_ | 1826118278229000192 |
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| author | Tan, Xuan Min |
| author2 | He Bingsheng |
| author_facet | He Bingsheng Tan, Xuan Min |
| author_sort | Tan, Xuan Min |
| collection | NTU |
| description | Whenever the term “Big Data” was mentioned, it was often closely associated with technologies like Apache Hadoop and the “NoSQL” class of databases such as MongoDB and Neo4j. It was possible to stream real-time data analytics using these technologies with ease and these analytics usually accomplished in 20 minutes or less. Over the past recent years, there were many such open source technologies emerged in the market but how many of them were really efficient and suitable for processing iterative data like graph. Some of the graph processing systems such as GraphLab and Apache Giraph were inspired by Bulk Synchronous Parallel (BSP) model while others like Hadoop follows the Google’s MapReduce framework. In this project, both BSP model and MapReduce framework were intensively studied using two prominent open source projects, Hadoop and Giraph. A series of large graph processing were executed on both systems and their results were analyzed. The experiments show that Giraph is more surpassing in processing iterative data. |
| first_indexed | 2024-10-01T04:40:46Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/62851 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:40:46Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/628512023-03-03T20:24:28Z High performance data processing systems in cloud Tan, Xuan Min He Bingsheng School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Data::Data structures DRNTU::Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems Whenever the term “Big Data” was mentioned, it was often closely associated with technologies like Apache Hadoop and the “NoSQL” class of databases such as MongoDB and Neo4j. It was possible to stream real-time data analytics using these technologies with ease and these analytics usually accomplished in 20 minutes or less. Over the past recent years, there were many such open source technologies emerged in the market but how many of them were really efficient and suitable for processing iterative data like graph. Some of the graph processing systems such as GraphLab and Apache Giraph were inspired by Bulk Synchronous Parallel (BSP) model while others like Hadoop follows the Google’s MapReduce framework. In this project, both BSP model and MapReduce framework were intensively studied using two prominent open source projects, Hadoop and Giraph. A series of large graph processing were executed on both systems and their results were analyzed. The experiments show that Giraph is more surpassing in processing iterative data. Bachelor of Engineering (Computer Science) 2015-04-30T02:48:00Z 2015-04-30T02:48:00Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62851 en Nanyang Technological University 46 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Data::Data structures DRNTU::Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems Tan, Xuan Min High performance data processing systems in cloud |
| title | High performance data processing systems in cloud |
| title_full | High performance data processing systems in cloud |
| title_fullStr | High performance data processing systems in cloud |
| title_full_unstemmed | High performance data processing systems in cloud |
| title_short | High performance data processing systems in cloud |
| title_sort | high performance data processing systems in cloud |
| topic | DRNTU::Engineering::Computer science and engineering::Data::Data structures DRNTU::Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems |
| url | http://hdl.handle.net/10356/62851 |
| work_keys_str_mv | AT tanxuanmin highperformancedataprocessingsystemsincloud |