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2024
Stock Market Real-Time Data Analysis Using Kafka

Stock Market Real-Time Data Analysis Using Kafka

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An end-to-end data engineering solution for real-time stock market analysis, leveraging Apache Kafka, Python, and AWS for scalable, cloud-based data pipelines.

Data Engineer & Cloud Developer

Personal Project

PythonApache KafkaAWSS3AthenaGlue CrawlerGlue Data CatalogEC2kafka-pythons3fspandasReal-Time DataBig Data

Overview

I created this project to demonstrate my expertise in real-time data pipelines, cloud computing, and big data processing. It’s a practical application of Apache Kafka and Amazon Web Services (AWS) for financial data analysis, showcasing my ability to design and implement complex, scalable systems. This end-to-end data engineering solution streams stock market data in real-time using Kafka, processes it with Python, and stores it in AWS S3 for analysis with AWS Glue and Athena. Hosted on AWS EC2, the project highlights my skills in distributed messaging systems, cloud-based storage, and SQL querying, making it an ideal learning tool for real-time data engineering.

Features

  • Real-Time Streaming: Streams stock market data using Apache Kafka for live updates.
  • Data Ingestion: Produces data from sources (e.g., CSV) to Kafka topics with Python.
  • Data Consumption: Consumes Kafka messages and stores them as JSON in AWS S3.
  • Data Cataloging: Uses AWS Glue Crawler and Data Catalog for automated metadata management.
  • SQL Querying: Enables analysis of S3 data with Amazon Athena and SQL.
  • Scalable Architecture: Hosted on AWS EC2 for reliable, distributed processing.

Project Architecture

The architecture flow is as follows:

  • Producer: A Python script reads stock market data (e.g., from a CSV file) and sends it to a Kafka topic using kafka-python.
  • Kafka on EC2: Apache Kafka, hosted on an AWS EC2 instance, manages the real-time data stream, distributing messages to consumers.
  • Consumer: Another Python script consumes Kafka messages, processes them, and stores them as JSON files in an AWS S3 bucket using s3fs.
  • AWS Services:
    • AWS Glue Crawler: Automatically discovers and catalogs the data stored in S3.
    • AWS Glue Data Catalog: Maintains metadata for the data, enabling querying.
    • Amazon Athena: Allows SQL-based querying of the S3 data for analysis.

Technology Used

  • Python: Core language for data ingestion, processing, and scripting.
  • Apache Kafka: Real-time data streaming and distributed messaging system.
  • Amazon Web Services (AWS):
    • S3 (Simple Storage Service): Scalable storage for processed data.
    • Athena: SQL-based querying for data analysis.
    • Glue Crawler: Automated data discovery and cataloging.
    • Glue Data Catalog: Metadata management for S3 data.
    • EC2 (Elastic Compute Cloud): Hosting for Kafka and processing scripts.
  • Libraries:
    • kafka-python: Kafka producer and consumer implementations.
    • s3fs: Seamless interaction with AWS S3.
    • pandas: Data manipulation and CSV handling.

Design Screens

Architecture Diagram Athena Query Results

My Contributions

  • Pipeline Design: Architected the end-to-end real-time data pipeline with Kafka and AWS.
  • Data Ingestion: Wrote Python scripts using kafka-python to produce stock data to Kafka.
  • Data Processing: Developed consumer scripts to process Kafka streams and store data in S3 with s3fs.
  • Cloud Integration: Configured AWS EC2 for Kafka, S3 for storage, and Glue/Athena for analysis.
  • Optimization: Ensured scalability and reliability using AWS services and Kafka’s distributed system.

Challenges Faced

Overcame hurdles like setting up Kafka on EC2 with proper networking, handling large-scale data streams without lag, and integrating AWS Glue and Athena for seamless querying. Ensuring data consistency between Kafka and S3 under high throughput was a key challenge.

What I Learned

This project deepened my expertise in real-time data pipelines with Kafka, cloud computing with AWS, and big data processing with Python. I mastered distributed systems, S3 storage workflows, and SQL-based analytics, gaining hands-on experience in scalable data engineering.

Future Improvements

Plans include adding a real-time dashboard with visualization (e.g., using AWS QuickSight), integrating live stock APIs (e.g., Alpha Vantage), and implementing data partitioning in S3 for faster queries.

Impact

This project serves as a robust proof-of-concept for real-time financial data analysis, demonstrating scalable data engineering practices and cloud-based analytics, ideal for portfolio showcasing or educational purposes.

And more, including distributed system design, cloud optimization, and data pipeline automation.

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