Analyzing Music Discovery and Recommendation Engine Using SQL
Group Category: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
Discover music listening patterns and improve your SQL skills with real user data.
Business Overview:
Analyzing Music Discovery and Recommendation Engine Using SQL helps you explore how users interact with music on a streaming platform. You’ll use real-like data to find out what genres people prefer, how playlists are curated, and how listening habits change over time. This product is ideal for people who want to learn SQL by solving real business problems in the music industry and better understand how recommendation systems work.
Product Highlights:
- Engaging SQL guide with 5 real-world use cases focused on music discovery and user listening behavior
- Showcase clear sample outputs to help you understand expected results from each query
- Based on a dynamic dataset of 15000+ records covering users, songs, genres, playlists, and more
- A strong portfolio project for showcasing SQL and content analytics skills
Learning Outcomes:
By working through this product, you’ll be able to:
- Write SQL queries to analyze music listening trends and user preferences
- Discover how different users prefer different genres and playlists
- Understand seasonal listening habits and genre shifts
- Use SQL to support music discovery, curation, and personalization strategies
- Build confidence in analyzing real-world media datasets using SQL
Use Case Document
| PDFDescription:
This PDF presents a structured set of SQL use cases focused on understanding music discovery and enhancing recommendation systems on streaming platforms. It combines listener behavior, genre trends, and playlist data to uncover personalization insights and user taste profiles.
- Contains clearly defined use cases with objectives, business impact, and expected SQL outputs
- Guides analysts in mapping genre preferences, cross-genre hits, and playlist diversity
- Useful for building and improving recommendation engines and content discovery systems
- Supports seasonal analysis, user profiling, and playlist quality scoring
- Complements relational datasets across users, songs, genres, and playlists for a full-funnel analytics view
artists
| CSVDescription:
This dataset provides key details about musical artists, serving as a reference for linking songs, playlists, and engagement analytics.
- Includes artist IDs and names
- Enables mapping of songs, albums, and playlist content to creators
- Useful for analyzing artist reach, trends, and fan-based engagement
- Supports recommendation systems and curated content features
- Complements genre, song, and listening data for full artist profiling
genres
| CSVDescription:
This file defines the primary genre taxonomy for music classification across the platform.
- Contains genre IDs and names
- Enables genre tagging for songs and user preference analysis
- Useful for genre-level engagement, recommendation, and reporting
- Supports filtering and segmentation in discovery algorithms
- Complements song_genres and songs datasets for multi-genre mapping
playlist_songs
| CSVDescription:
This dataset tracks which songs appear in which playlists, forming the basis of playlist analytics and content curation.
- Includes playlist IDs and song IDs
- Enables analysis of playlist diversity, popularity, and song repetition
- Useful for detecting trends, optimizing playlists, and cross-playlist overlap
- Supports content recommendations based on playlist patterns
- Complements user engagement and song metadata datasets
playlists
| CSVDescription:
This file provides metadata about playlists created by users or curators, useful for engagement and discovery analysis.
- Contains playlist IDs, names, creator info, and timestamps
- Supports analysis of playlist creation trends and content lifecycle
- Useful for identifying active curators, trending playlists, and follow activity
- Enables playlist profiling and recommendation personalization
- Complements playlist_songs and user_playlist_follows datasets
song_genres
| CSVDescription:
This file maps individual songs to one or more genres, enabling multi-label genre analysis for content classification and discovery.
- Includes song IDs and corresponding genre IDs
- Supports complex filtering, diversity scoring, and personalized recommendations
- Useful for exploring cross-genre trends and tagging completeness
- Enables richer song profiling and content navigation
- Complements songs and genres datasets for dynamic discovery
songs
| CSVDescription:
This CSV contains key metadata for all songs in the catalog, including duration and artist links.
- Includes song IDs, names, durations, and artist/album references
- Supports song-level performance, preference modeling, and session analysis
- Useful for trend detection, playlist fit, and content recommendations
- Enables search, filter, and personalization logic
- Pairs with listening history and user feedback datasets
user_listening_history
| CSVDescription:
This dataset logs every user-song interaction, forming the backbone of behavioral analysis and playback insights.
- Includes user IDs, song IDs, and timestamped listening events
- Supports frequency, recency, and engagement metrics
- Useful for churn analysis, session modeling, and personalized content delivery
- Enables listener profiling and active user segmentation
- Complements playlists, preferences, and subscription data
user_playlist_follows
| CSVDescription:
This file captures the relationship between users and playlists they follow, representing social and discovery signals.
- Includes user IDs and playlist IDs
- Enables tracking of popular playlists and active follower bases
- Useful for analyzing content curation impact and social music dynamics
- Supports recommendation logic and influencer playlist strategies
- Complements playlist and user metadata for full engagement mapping
users
| CSVDescription:
This CSV provides user-level demographic and registration data, foundational for customer profiling and segmentation.
- Includes user IDs, registration details, and basic demographics
- Supports cohort analysis, growth trends, and user lifecycle modeling
- Useful for personalization, segmentation, and retention strategies
- Enables linking with playlists, listening history, and interactions
- Complements subscription, content, and activity datasets
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