EmoStream

Real-time emoji streaming and visualization system using Kafka and Spark

Overview

EmoStream is a scalable real-time emoji streaming and visualization system designed for live sports events. The project demonstrates expertise in distributed systems, stream processing, and high-concurrency architectures.

Architecture

Event-Driven Design

  • Pub-Sub Architecture: Decoupled producer-consumer model
  • Apache Kafka: Distributed event streaming platform
  • Spark Streaming: Real-time data processing
  • Flask: Lightweight web framework for visualization

Scalability Features

  • Horizontal Scaling: Kafka cluster for high throughput
  • Stream Processing: Spark for parallel data processing
  • Load Balancing: Distribution across multiple consumers

Technical Implementation

Components

  1. Producers: Generate emoji events based on user interactions
  2. Kafka Brokers: Manage event streams with high availability
  3. Spark Streaming: Process and aggregate emoji data in real-time
  4. Flask Endpoint: Serve processed data to visualization clients

Performance Testing

  • Locust: High-concurrency load testing
  • Stress Testing: Validated performance under heavy traffic
  • Monitoring: Real-time metrics and alerting

Technical Stack

  • Streaming: Apache Kafka
  • Processing: Apache Spark Streaming
  • Backend: Flask (Python)
  • Load Testing: Locust
  • Big Data: Hadoop ecosystem

Use Cases

Live Sports Events

  • Real-time fan engagement visualization
  • Sentiment analysis during matches
  • Interactive audience participation

General Applications

  • Social media sentiment tracking
  • Live event engagement metrics
  • Real-time polling and feedback systems

Key Learnings

EmoStream demonstrates proficiency in:

  • Distributed Systems: Building fault-tolerant architectures
  • Stream Processing: Handling high-velocity data
  • Performance Optimization: Ensuring low latency at scale
  • Big Data Technologies: Working with Kafka, Spark, and Hadoop

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