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Introduction:

Streaming data processing techniques have become increasingly important in the field of data analytics as the volume and velocity of data continue to grow exponentially. This has led to the development of various tools and technologies that enable organizations to process and analyze data in real-time, allowing for more timely and informed decision-making.

Masters 5 chapters table of content:

Chapter 1: Introduction
1.1 Introduction
1.2 Objective of Study
1.3 Limitation of Study
1.4 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Streaming Data Processing Techniques
2.2 Common Tools and Technologies used in Streaming Data Processing
2.3 Applications of Streaming Data Processing in Different Industries

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques

Chapter 4: Discussion of Findings
4.1 Analysis of Data Processing Techniques
4.2 Comparison of Different Tools and Technologies
4.3 Implications of Findings on the Field of Data Analytics

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Conclusion
5.3 Recommendations for Future Research

Thesis Overview on Streaming Data Processing Techniques:

The thesis “Streaming Data Processing Techniques” aims to provide a comprehensive overview of the various tools, technologies, and methods used in processing streaming data. The study will explore the challenges and opportunities associated with streaming data processing, as well as the implications of real-time data analysis on decision-making processes in organizations. The research will also examine the literature on streaming data processing and conduct an in-depth analysis of different tools and technologies available for real-time data processing. Overall, the thesis will contribute to the existing body of knowledge on streaming data processing and provide valuable insights for practitioners and researchers in the field of data analytics.

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