Data stream processing and real-time analytics
Table Of Contents
1.1 Motivation and Objectives
1.2 Applications of Real-time Data Stream Processing
2. Literature Review
2.1 Stream Processing Architectures and Frameworks
2.2 Real-time Analytics and Data Visualization
3. Data Stream Ingestion and Processing
3.1 Data Source Integration and Connectivity
3.2 Stream Processing Pipelines and Workflows
4. Real-time Analytics and Insights
4.1 Continuous Query Processing and Aggregation
4.2 Pattern Recognition and Anomaly Detection
5. Scalability and Fault Tolerance
5.1 Distributed Computing and Parallel Processing
5.2 Fault Recovery and Resilience Mechanisms
Thesis Abstract
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.
Thesis Overview
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