Enhanced Late-Straggler Algorithm With On-Demand Etl For Big Data Retrieval
The growth of digital information is phenomenal. Digital documents dominate nearly every aspect of doing business to the point that it is hard to imagine doing without them. With an unprecedented potential lurking in its depths, the ongoing digital information revolution also presents risks and c...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60026/1/24%20Pages%20from%20ANWAR%20HUSSEIN%20ZAKARIA%20KATRAWI.pdf http://eprints.usm.my/60026/ |
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| Summary: | The growth of digital information is phenomenal. Digital documents dominate
nearly every aspect of doing business to the point that it is hard to imagine doing
without them. With an unprecedented potential lurking in its depths, the ongoing
digital information revolution also presents risks and challenges, mainly when dealing
with the extraction and analysis of digital data. The conventional method ETL of Big
Data processing consists of Extraction, Transformation, and Loading integrated into a
warehouse. Using this method without any optimization often leads to a delay in data
retrieval, known as the straggler problem, which is a situation that arises when tasks
are delayed due to low processing on some nodes. The straggler problem is considered
by many as a major problem, especially when the data resources are important and if
these resources are inefficiently used. Hence, detecting and, therefore, eliminating the
straggler problem early is crucial to enhancing the ETL performance. |
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