Musify: Your
Real-World
Music Streaming
Data Playground
Musify is a ready-to-use synthetic dataset that mimics the experience of a real music streaming app. Ideal for content recommendations, viewer retention analysis, and feed personalization.
Overview
Musify is a realistic synthetic dataset that mirrors how users interact with a modern music streaming app. It captures the flow of listening sessions, playlists, likes, skips, and recommendations — all designed to help data scientists, engineers, and analysts experiment, test, and build smarter audio experiences. Whether you're working on user preference modeling, recommendation systems, or audio analysis, Musify gives you a safe, structured playground to dive deep into music data without privacy concerns.
The dataset includes everything from users and songs to artists, albums, genres, and playback histories. It's perfect for building and testing collaborative filtering systems, ranking algorithms, and engagement tracking tools. Whether you're preparing for interviews, building a data pipeline, or simulating app behavior, Musify helps you explore real-world scenarios with clean, comprehensive music platform data.
Full Streaming Lifecycle
Simulates a complete music streaming experience, including users, songs, albums, artists, and playback interactions.
Built for Development & Testing
Excellent for building and testing recommendation systems, skip prediction models, and playlist generators.
Backend Feature Testing
Useful for developers working on playback logic, personalization features, or backend audio catalog design.
Rich Engagement Data
Supports use cases in user engagement analysis, retention modeling, and content-based filtering.
Analytics & Research
Suitable for SQL practice, ETL development, time-series analytics, and A/B testing simulations.
How it Works
AI-Generated & Fully Synthetic
The Musify dataset is generated using advanced AI agents, creating a realistic yet entirely synthetic representation of music streaming interactions with zero real-world or personally identifiable data.
Realistic Simulation with Privacy
It simulates user accounts, song plays, playlist interactions, likes, and content engagement behaviors informed by industry-standard trends and public consumption patterns.
High-Quality & Safe for Use
Built using insights from public trends and industry data, the dataset delivers structured, high-quality data suitable for recommendation systems, analytics dashboards, and machine learning model training.
Dataset Schema
A comprehensive relational model representing a modern music streaming platform engineered for deep analysis and complex querying.
Users
Stores login, profile, and subscription info. All user actions are linked here.
Artists
Contains artist info, including bio and profile images. Each song and album is tied to an artist.
Albums
Represents music albums with metadata like title, release date, and cover art.
Songs
Stores track details, including title, duration, album, and artist info.
Genres
Contains music genres (e.g., Pop, Jazz, Hip-Hop) to classify songs and aid discovery.
Song Genres
Links songs to genres for flexible categorization.
Playlists
Represents user-created song collections, with metadata like titles and descriptions.
Playlist Songs
Connects songs to playlists, tracking which song is added to which playlist.
Playlist Follows
Tracks users following other users' playlists for a social music experience.
Songs Liked
Stores songs liked by users for personalized recommendations.
Listening History
Logs user playback history for session analytics and music suggestions.
Payments
Records payments made by users, mainly for subscriptions.
Subscriptions
Tracks user subscription details, including type and dates, alongside payments for premium access.
- CSV
- JSON
- Excel
- MySQL
- PostgreSQL
- SQL Server
- Snowflake