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Stock Market Real-Time Data Pipeline with Apache Kafka & Cassandra

This project focuses on retrieving real-time stock market data using Python and storing it in a Cassandra database via Apache Kafka. The data is processed with Apache Kafka on AWS EC2 and then stored in a local Cassandra server.

Key Features

  • Data Engineering: Implement a data pipeline for processing real-time data streams.
  • Tech Stack: Utilize Python, AWS EC2, Apache Kafka, and CassandraDB.
  • Error Handling: Handle common errors and provide troubleshooting tips for a smooth workflow.
  • Future Enhancements: Incorporate data visualization, machine learning predictions, real-time alerts, and scalability.

Architecture

Pipeline Architecture

Environment Setup

Hardware Used

Local Machine:

  Ubuntu 22.04.1 LTS
  4 vCore, 4 GiB Memory, 32 GiB Storage

AWS EC2:

  Amazon Linux 2 Kernel 5.10
  t2 Family, 1 vCore, 1 GiB Memory

Prerequisites

Make sure you have the following prerequisites installed:

  • Python with kafka-python & cassandra-driver packages
  • AWS CLI
  • Java
  • Apache Kafka
  • Cassandra

Project Implementation

Follow these steps to implement the project:

  1. Launch an EC2 instance and install Apache Kafka.
  2. Create a Python script to retrieve real-time stock market data.
  3. Use Apache Kafka to produce the data to a topic.
  4. Create a Python script to consume the topic data and store it in CassandraDB.

Execution

Follow these steps to execute the project:

  1. Launch an EC2 instance and set up Apache Kafka.
  2. Start the Apache Kafka producer to produce data to a topic.
  3. Run the Python script to send real-time stock market data.
  4. Start the Python consumer script to consume and store data in CassandraDB.
  5. Use SQL queries to retrieve the data stored in CassandraDB.

Error Handling and Troubleshooting

Here are some common errors and troubleshooting tips for this project:

  • Apache Kafka Connection Error: If you encounter an error while connecting to Apache Kafka, ensure that the EC2 instance is running and that the Apache Kafka service is up and running. Also, check the security group settings to ensure that the required ports are open.
  • Cassandra Connection Error: If you encounter an error while connecting to CassandraDB, ensure that the Cassandra service is running on the local server. Also, check the firewall settings to ensure that the required ports are open.
  • Data Retrieval Error: If you encounter an error while retrieving stock market data, ensure that the data retrieval script is running correctly.
  • Data Storage Error: If you encounter an error while storing the data in CassandraDB, ensure that the required tables have been created and that the data is being stored in the correct format.
  • Data Query Error: If you encounter an error while querying the data stored in CassandraDB, ensure that the SQL query is correct and that the required tables exist.

For more information, refer to the log files or contact the author at contact.kishlayjeet@gmail.com.

Future Enhancements

Consider these future enhancements for the project:

  • Adding a data visualization layer using tools such as Matplotlib or Seaborn to visualize the stock market data stored in CassandraDB.
  • Incorporating a machine learning model to predict stock prices based on the stored data.
  • Implementing a real-time alert system to notify users of significant changes in the stock market.
  • Scaling the pipeline to handle larger amounts of data by adding more EC2 instances and increasing the size of CassandraDB clusters.

Conclusion

This project demonstrates the use of Python, AWS, Apache Kafka, Cassandra, and SQL to retrieve and store real-time stock market data. The pipeline created in this project can be adapted to process and store any real-time data stream efficiently.