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