Data stream processing and real-time analytics
Table Of Contents
<p> 1.1 Motivation and Objectives<br> 1.2 Applications of Real-time Data Stream Processing<br>2. Literature Review<br> 2.1 Stream Processing Architectures and Frameworks<br> 2.2 Real-time Analytics and Data Visualization<br>3. Data Stream Ingestion and Processing<br> 3.1 Data Source Integration and Connectivity<br> 3.2 Stream Processing Pipelines and Workflows<br>4. Real-time Analytics and Insights<br> 4.1 Continuous Query Processing and Aggregation<br> 4.2 Pattern Recognition and Anomaly Detection<br>5. Scalability and Fault Tolerance<br> 5.1 Distributed Computing and Parallel Processing<br> 5.2 Fault Recovery and Resilience Mechanisms<br></p>
Thesis Abstract
<p> This project focuses on the development of a data stream processing system capable of real-time analytics for handling continuous data streams. The system will employ distributed computing and stream processing frameworks to enable rapid analysis and extraction of insights from high-velocity data sources. The project aims to address the challenges of real-time data processing and provide a scalable solution for applications such as IoT, financial trading, and monitoring systems. <br></p>Thesis Overview
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