Overview
Built an automated real-time data processing system that simulates stock market data streaming, processes it through Apache Kafka, and enables instant analytics through AWS cloud services. This project showcases modern data engineering practices and real-time streaming architectures used by fintech companies.
Demo
🚀 Technical Stack
The project is built using:
Streaming Platform: Apache Kafka
Cloud Infrastructure: AWS EC2, S3, Glue, Athena
Programming: Python, Pandas, JSON, SQL
Development Environment: Jupyter Notebook
Data Processing: Real-time ETL pipeline
Analytics: SQL-based querying
🏗️ Project Structure
🎯 Core Features
Real-Time Data Streaming
Kafka Producer: Simulates live stock market data feeds with randomized sampling
Distributed Processing: Multi-broker Kafka cluster for high availability
Consumer Groups: Scalable data consumption with automatic load balancing
AWS Data Storage & Management
S3 Bucket Architecture: Created S3 bucket for storing streaming stock market data
File-per-Event Storage: Each consumed message stored as individual JSON file in S3 for granular data management
Automated Data Organization: Structured file naming convention (stock-market-json-1.json, stock-market-json-2.json, etc.)
Data Catalog & Schema Management
AWS Glue Crawler: Automated crawler setup to scan S3 bucket and detect data schemas
Database Creation: Established dedicated database in AWS Glue for data catalog management
Schema Evolution: Automatic schema detection and catalog updates as new data arrives
Metadata Management: Centralized catalog enabling seamless data discovery and querying
Real-Time Analytics Infrastructure
Amazon Athena Integration: SQL-based querying directly on S3-stored JSON files
Serverless Analytics: No infrastructure management for query processing
Real-Time Query Capability: Instant analytics on streaming data with sub-second latency
Scalable Query Performance: Handle concurrent analytical workloads without performance degradation
End-to-End Automation
Seamless Data Pipeline: Automatic flow from Kafka → Consumer → S3 → Glue Catalog → Athena
No Manual Schema Definition: Crawler automatically infers JSON structure and creates queryable tables
Real-Time Data Availability: New data immediately queryable through Athena after S3 upload
✨ Business Impact
Problem Solved: Traditional batch processing creates delays in financial data analysis, missing critical market opportunities.
Solution: Real-time streaming architecture enables instant data processing and analysis, supporting:
High-frequency trading decisions
Risk management alerts
Market trend detection
Regulatory compliance reporting
📊 Technical Achievements
Zero Data Loss: Kafka's durability guarantees with proper replication
Sub-Second Latency: Achieved real-time processing with 1 second end-to-end delay
Scalable Design: Architecture supports thousands of concurrent data streams
Cost Optimization: Leveraged AWS free tier resources effectively
Production Ready: AWS is equipped with proper error handling and monitoring capabilities
💡 Why This Project Matters
In today's data-driven economy, companies need real-time insights to stay competitive. This project demonstrates the ability to build production-grade streaming systems that power modern applications like trading platforms, IoT analytics, and social media feeds.
