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Introduction:

Distributed systems have become a crucial component in the processing of big data due to the rapidly increasing volume, velocity, and variety of data generated from various sources. As a result, there is a growing need for efficient and scalable solutions to process and analyze this vast amount of data. This thesis aims to explore the design and implementation of distributed systems for big data processing, with a focus on improving performance, scalability, and fault tolerance.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to distributed systems
2.2 Big data processing techniques
2.3 Distributed computing frameworks
2.4 Scalability and fault tolerance in distributed systems
2.5 Data partitioning and replication
2.6 Parallel processing and distributed computing algorithms
2.7 Performance optimization in distributed systems
2.8 Challenges in big data processing
2.9 Case studies of distributed systems for big data processing
2.10 Emerging trends in distributed systems for big data processing

Chapter 3: System Design and Methodology
3.1 System architecture design
3.2 Data ingestion and storage
3.3 Data processing and analysis
3.4 Fault tolerance and data recovery mechanisms
3.5 Scalability and load balancing strategies
3.6 Security and privacy considerations
3.7 Performance evaluation metrics
3.8 Testing and validation methodologies

Chapter 4: System Implementation
4.1 Technology stack selection
4.2 Data modeling and schema design
4.3 Distributed computing framework setup
4.4 Data processing pipeline implementation
4.5 Fault tolerance and data replication setup
4.6 Performance tuning and optimization techniques
4.7 Scalability testing and benchmarking
4.8 Security implementation and compliance

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for future research
5.4 Recommendations for practitioners
5.5 Conclusion

Thesis Overview:

Distributed systems have gained significant importance in the field of big data processing due to the challenges posed by the massive amount of data being generated every day. This thesis aims to explore the design and implementation of distributed systems for big data processing, with a focus on enhancing performance, scalability, and fault tolerance. The research will begin with an introduction to the topic, followed by a review of relevant literature in the field. The subsequent chapters will delve into system design and methodology, system implementation, and finally, the conclusion and summary of the project. The goal is to provide insights into the key factors influencing the design and implementation of distributed systems for big data processing, and to present practical solutions to address the challenges faced in this domain.

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