Realtime-Data-Pipeline-for-Stack-Market-Analysis

Real-time Data Pipeline for Stock Market Analysis

Overview

This project demonstrates the development of a real-time data pipeline designed to ingest, process, and analyze stock market data. Using cutting-edge tools like Apache Kafka, PostgreSQL, and Python, the pipeline captures stock data in real-time and stores it in a robust data architecture, enabling timely analysis and insights.

Project Highlights

• Data Ingestion: Real-time stock data is fetched using APIs from Alpha Vantage and ingested into the pipeline.
• Stream Processing: Apache Kafka, deployed on Confluent Cloud, acts as the streaming platform to handle data flow between producers and consumers.
• Data Storage: Aiven’s integration with PostgreSQL allows for seamless data storage, enabling efficient query execution and data retrieval.
• Real-time Analysis: The pipeline is designed to process data on-the-fly, providing up-to-the-minute insights into stock performance.

  Technologies Used

• Python: Scripting for producer and consumer operations.
• Apache Kafka: Streaming platform for real-time data handling.
• APIs (Alpha Vantage): Data source for stock market data.
• Aiven: Managed service to connect Kafka with PostgreSQL.
• PostgreSQL: Database for storing and analyzing the ingested data.

  Architecture Overview

  1. Producer Script: Written in Python, the producer script connects to the Alpha Vantage API to fetch live stock data and sends it to Kafka.
  2. Kafka Streams: Apache Kafka serves as the streaming backbone, receiving data from the producer and forwarding it to a PostgreSQL database.

1. Consumer Script: Another Python script reads data from Kafka topics and loads it into PostgreSQL for analysis and visualization.
2. Data Analysis: SQL queries and aggregations on the PostgreSQL database allow for insightful analysis of real-time stock data.

   Learning Outcomes

   • Stream Processing with Kafka: Developed skills in handling real-time data using Apache Kafka, setting up topics, and managing data streams.
   • ETL Workflow: Gained experience in ETL (Extract, Transform, Load) processes, integrating APIs, streaming platforms, and databases.
   • Data Integration: Successfully connected various technologies to build a cohesive data pipeline, demonstrating the ability to work with cloud services and real-time data.

     Getting Started

     Prerequisites

   • Python 3.x
   • Apache Kafka (via Confluent Cloud)
   • PostgreSQL (via Aiven)
   • API key for Alpha Vantage

     Installation

3. Clone the repository:

bash

git clone https://github.com/yourusername/stock-market-pipeline.git

1. Install the required Python packages:

bash

pip install -r requirements.txt

1. Set up Kafka topics and configure the PostgreSQL connection as per the instructions in the config.yaml file.

Running the Project

1. Run the producer script to start streaming data to Kafka:

bash

python producer.py

1. Run the consumer script to store data in PostgreSQL:

bash

python consumer.py

Future Work

Visualization: Integrate with Tableau or Power BI for dynamic data visualization. Scalability: Expand the pipeline to handle multiple data sources and enhance fault tolerance.

About Me

I am a data scientist passionate about building real-time data solutions that drive business insights. This project showcases my skills in data engineering, particularly in stream processing, ETL workflows, and integrating modern data technologies.