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Development of Collaborative Music Streaming Platform with Dynamic Playlist Management
  1. case
  2. Development of Collaborative Music Streaming Platform with Dynamic Playlist Management

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Development of Collaborative Music Streaming Platform with Dynamic Playlist Management

profil-software.com
Media
Information technology

Key Challenges Identified

Existing music streaming platforms lack team collaboration features, dynamic playlist optimization, and office-hour-specific playback capabilities. The solution must address scalability for multi-user management, real-time song rating systems, and algorithmic playlist prioritization while maintaining data resilience and user engagement.

About the Client

A collaborative music streaming platform enabling team-based playlist management and dynamic song rating for office environments

Project Goals and Objectives

  • Implement dynamic playlist algorithms for song rating-based prioritization
  • Develop multi-user collaboration features for playlist management
  • Create time-based playback scheduling for core working hours
  • Ensure system scalability for large datasets and concurrent users
  • Design intuitive UI for real-time collaboration tracking

Core System Functionalities

  • Multi-user group playlist creation and management
  • Real-time song rating system with algorithmic playback prioritization
  • Core hour prime-time playback scheduling
  • Daily playlist randomization/shuffling mechanism
  • Activity tracking dashboard for user interactions

Technology Stack Requirements

Python
React
Kubernetes
PostgreSQL

System Integration Needs

  • Music streaming API (e.g., Spotify/Apple Music)
  • OAuth2 authentication providers
  • Cloud storage for playlist data

Performance & Security Requirements

  • Support 10,000+ concurrent users
  • 99.9% system availability SLA
  • Real-time data synchronization across users
  • GDPR-compliant data protection

Business Impact Projections

Enables 30% increased employee engagement through collaborative music curation, reduces playlist redundancy by 50% through intelligent shuffling algorithms, and improves system scalability to support enterprise-level deployments with 24/7 uptime reliability.

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