Stock Market Kafka Real Time Data Engineering Project

Welcome to the Real-time Stock Market Prediction project! This project utilizes Kafka along with AWS services such as S3, Glue, Athena, EC2, and Kafka to predict stock market trends in real-time.

Overview

The Real-time Stock Market Prediction project aims to provide accurate predictions of stock market trends using real-time data streams. Leveraging Kafka for data streaming and processing, along with AWS services like S3 for data storage, Glue for data cataloging and ETL processes, Athena for querying data, and EC2 for hosting applications, this project offers a comprehensive solution for stock market analysis and prediction.

We are going to use different technologies such as Python, Amazon Web Services (AWS), Apache Kafka, Glue, Athena, and SQL.

Architecture

Technology Used

• Programming Language - Python
• Amazon Web Service (AWS)

1. S3 (Simple Storage Service)
2. Athena
3. Glue Crawler
4. Glue Catalog
5. EC2
6. Apache Kafka

Features

• Real-time data streaming and processing using Kafka
• Data storage and retrieval with AWS S3
• Data cataloging and ETL processes facilitated by AWS Glue
• Querying data using AWS Athena
• Scalable application hosting on AWS EC2

Getting Started

To get started with the Real-time Stock Market Prediction project, follow these steps:

Clone the Repository

git clone https://github.com/vishnuthumpudi/Real-time-Stock-Market-Prediction-Using-kafka.git

Installation and Configuration

1. Set up Kafka: Install and configure Kafka on your local machine or use a cloud-based Kafka service.

2. Set up AWS Services: Set up AWS S3, Glue, Athena, EC2, and Kafka services in your AWS account. Configure access and permissions accordingly.

3. Configure Project Settings: Update configuration files with the appropriate credentials and settings for accessing AWS services and Kafka.

Running the Project

1. Start Kafka Producer: Start the Kafka producer to ingest real-time stock market data.

2. Data Processing: Use AWS Glue for data cataloging and ETL processes to transform and store the data in S3.

3. Querying Data: Utilize AWS Athena for querying data stored in S3 to perform analysis and generate insights.

4. Real-time Prediction: Deploy and run the real-time prediction application on AWS EC2 to predict stock market trends based on streaming data from Kafka.

Contributing

Contributions to the Real-time Stock Market Prediction project are welcome! Feel free to submit bug reports, feature requests, or pull requests to enhance the functionality and performance of the project.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Special thanks to the Apache Kafka project and AWS for providing powerful tools and services that make real-time stock market prediction possible.

Thank you for exploring the Real-time Stock Market Prediction project! We hope you find it valuable for analyzing and predicting stock market trends in real-time.