Real-Time Stock Price Analysis Pipeline This project demonstrates a scalable data engineering pipeline that collects, processes, and visualizes real-time stock market data. The pipeline integrates APIs, processes data in real-time using Apache Kafka and Python, and delivers insights through visual dashboards and Python-generated graphs. real-time-stock-analysis-pipeline/

│ ├── README.md # Project overview ├── requirements.txt # Python dependencies ├── data/ # Sample and processed data │ ├── sample_data.csv
│ ├── processed_data.csv ├── src/ # Core Python scripts │ ├── fetch_data.py
│ ├── process_data.py
│ ├── load_data.py
│ ├── visualize_data.py
│ ├── airflow_dag.py
├── dashboards/ # Tableau/Power BI dashboards │ ├── tableau_dashboard.twb │ ├── power_bi_dashboard.pbix ├── scripts/ # Kafka producer/consumer scripts │ ├── kafka_producer.py │ ├── kafka_consumer.py ├── config/ # Configuration files │ ├── api_keys.json
│ ├── db_config.yaml └── docs/ # Documentation and presentations ├── architecture_diagram.png
├── dataset_description.md
└── presentation.pdf

git clone https://github.com/evans25575/real-time-stock-analysis-pipeline.git cd real-time-stock-analysis-pipeline pip install -r requirements.txt

Real-Time Stock Price Analysis Pipeline

📌 Project Overview

This project demonstrates a scalable data engineering pipeline designed to collect, process, and visualize real-time stock market data. The pipeline integrates APIs, processes data in real-time using Apache Kafka and Python, and presents insights through visual dashboards and Python-generated graphs. It is ideal for applications such as live market analysis, trading strategies, and financial data exploration.

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🔥 Features

• Real-Time Data Ingestion: Collects live stock market data using APIs.
• Streaming Processing: Utilizes Apache Kafka for data streaming and processing.
• Data Transformation: Transforms raw data into structured, analyzable formats.
• Data Storage: Stores processed data in CSV files for easy access and further analysis.
• Visualizations: Provides dashboards using Tableau/Power BI and Python-generated graphs.
• Automated Pipelines: Includes Airflow DAGs for scheduling and managing ETL processes.

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📁 Project Structure

real-time-stock-analysis-pipeline/
│
├── README.md               # Project overview (You're reading this!)
├── requirements.txt         # Python dependencies
├── data/                    # Sample and processed data
│   ├── sample_data.csv
│   └── processed_data.csv
├── src/                     # Core Python scripts
│   ├── fetch_data.py         # Fetches real-time data from APIs
│   ├── process_data.py       # Processes and transforms raw data
│   ├── load_data.py          # Loads data to storage
│   ├── visualize_data.py     # Generates visualizations
│   └── airflow_dag.py        # Automates ETL process using Airflow
├── dashboards/              # Tableau/Power BI dashboards
│   ├── tableau_dashboard.twb
│   └── power_bi_dashboard.pbix
├── scripts/                 # Kafka producer/consumer scripts
│   ├── kafka_producer.py
│   └── kafka_consumer.py
├── config/                  # Configuration files
│   ├── api_keys.json
│   └── db_config.yaml
├── docs/                    # Documentation and presentations
│   ├── architecture_diagram.png
│   ├── dataset_description.md
│   └── presentation.pdf

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🚀 Installation

1. Clone the repository:

 git clone https://github.com/evans25575/real-time-stock-analysis-pipeline.git
 cd real-time-stock-analysis-pipeline

2. Install dependencies:

 pip install -r requirements.txt

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📌 Usage

1. Fetch Data:

 python src/fetch_data.py

2. Process Data:

 python src/process_data.py

3. Visualize Data:

 python src/visualize_data.py

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📊 Visualization

Visual dashboards are created using:

• Tableau: dashboards/tableau_dashboard.twb
• Power BI: dashboards/power_bi_dashboard.pbix

Python-generated graphs are saved in the data/processed_data.csv folder.

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📚 Documentation

Detailed documentation is available in the docs/ folder, including:

• architecture_diagram.png: Visual representation of the pipeline.
• dataset_description.md: Description of the datasets used.
• presentation.pdf: Project presentation for stakeholders.

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📄 License

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

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🤝 Contributing

Contributions are welcome! Feel free to submit issues, fork the repository, and make pull requests.

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📧 Contact Information

For questions or suggestions, feel free to reach out at: kiplaevans2018@gmail.com

python src/fetch_data.py python src/process_data.py python src/visualize_data.py Feel free to contribute or reach out with any questions! Contact: kiplaevans2018@gmail.com