Thesis Abstract

The project aims to design and implement a scalable big data processing framework to address the challenges of processing and analyzing large volumes of data in distributed computing environments. The framework will provide efficient data ingestion, storage, processing, and querying capabilities for handling diverse data types, such as structured, semi-structured, and unstructured data. The project will focus on the development of distributed computing algorithms, fault tolerance mechanisms, and resource management strategies to enable the framework to scale horizontally and handle increasing data loads. The framework will be designed to support a wide range of big data applications, including real-time analytics, batch processing, and machine learning, and will contribute to the advancement of big data technologies in various domains, such as finance, healthcare, and e-commerce.

Thesis Overview