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