Thesis Abstract

Abstract
The rapid growth of technology and the increasing reliance on data-driven decision-making have led to a surge in the collection and analysis of large datasets. This has resulted in the need for more efficient ways to process, store, and analyze data. One of the most promising technologies that have emerged to address these challenges is the field of distributed computing. Distributed computing involves the use of multiple computers working together to solve a problem or perform a task. By distributing the workload across multiple machines, distributed computing can significantly increase processing power and speed, making it ideal for handling large datasets. This research project focuses on the application of distributed computing in the context of big data analytics. The primary objective of this research is to investigate the effectiveness of distributed computing techniques in improving the performance of big data analytics tasks. This will involve studying different distributed computing frameworks and algorithms to determine their impact on the speed, scalability, and efficiency of data processing. The research will also explore the challenges and limitations associated with distributed computing in the context of big data analytics. To achieve these objectives, the research will involve a comprehensive literature review of existing studies on distributed computing and big data analytics. This will help identify the current state of the art in the field and provide a solid theoretical foundation for the research. The study will also involve practical experiments using real-world datasets to evaluate the performance of different distributed computing techniques. The findings of this research will contribute to the existing body of knowledge on distributed computing and big data analytics by providing insights into the potential benefits and limitations of using distributed computing for data processing. The results of the study will be valuable for researchers, practitioners, and organizations looking to leverage distributed computing technologies for big data analytics applications. In conclusion, this research project aims to explore the application of distributed computing in enhancing the performance of big data analytics tasks. By investigating different distributed computing frameworks and algorithms, the research seeks to provide valuable insights into the effectiveness of distributed computing techniques in handling large datasets. The findings of this study will have implications for the future development and implementation of distributed computing solutions for big data analytics.

Thesis Overview