Analyzing Playlist and Social Features Using SQL
Group Category: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
Explore playlist trends and boost your SQL skills with real music platform data.
Business Overview:
Analyzing Playlist and Social Features Using SQL helps you understand how users create and engage with playlists and liked songs on a music platform. Using simulated data, you’ll explore things like which playlists are most followed, which songs appear in many playlists, and how user preferences vary. This product is great for anyone wanting to use SQL to study social features and content engagement on streaming platforms.
Product Highlights:
- Interactive PDF guide featuring 5 practical use cases focused on playlists, likes, and user activity
- Sample outputs included to help visualize each result
- Built on a realistic dataset of 25,000+ rows covering playlists, users, songs, genres, and social interactions
- Designed for SQL learners, content teams, and media analysts looking to understand user-driven content
- Great for showcasing skills in social engagement analysis and music-tech data projects
Learning Outcomes:
By working through this product, you’ll be able to:
- Write SQL queries that analyze user engagement and playlist performance
- Identify trends in popular songs and playlists
- Understand user music preferences and how they interact with content
- Use data to support better discovery, content curation, and platform cleanup
- Improve your ability to handle multi-table datasets in SQL
song_genres
| CSVDescription:
This file links songs to one or more genres, enriching each track’s metadata with classification tags.
- Contains song IDs and nd detegenre mappings
- Facilitates multi-genre representation and exploration
- Essential for genre-based filtering and playlist building
- Enables analysis of genre diversity within songs and users
- Supports hybrid genre trection and user affinity scoring
Use Case Document
| PDFDescription:
This PDF provides a focused SQL-based framework for analyzing playlist engagement and social behavior on music streaming platforms. It explores use cases related to user interactions, playlist quality, content discovery, and social influence through likes and follows.
- Contains defined use cases with clear objectives, SQL concepts, and expected outcomes
- Guides analysts in connecting playlists, songs, and social activity using joins and aggregation
- Useful for identifying popular playlists, inactive content, and user preference diversity
- Supports product and social teams in improving discovery, recommendations, and content quality
- Complements large-scale streaming datasets for evaluating community-driven music behavior
artists
| CSVDescription:
This file maintains unique artist records, forming the basis for mapping musical content and fan engagement.
- Contains artist IDs, names, and profile data
- Serves as the core reference for linking songs, albums, and playlists
- Essential for creator-level insights and content cataloging
- Enables segmentation by artist performance, genre, and activity
- Supports artist profiling, recognition, and trend analysis
genres
| CSVDescription:
This dataset defines the genre taxonomy used across the music platform, enabling multi-dimensional classification.
- Contains genre IDs and names
- Forms the base for tagging songs and playlists with genre data
- Essential for building filtering systems and user preference models
- Enables genre-level analytics for trends, diversity, and engagement
- Supports personalization engines and discovery systems
playlist_songs
| CSVDescription:
This file tracks the association between songs and playlists, supporting playlist content analytics.
- Contains playlist and song IDs
- Enables playlist diversity, repeat content, and curation evaluation
- Essential for analyzing playlist quality and content relevance
- Supports engagement scoring and discovery enhancement
- Complements user-follow and listening history analysis
playlists
| CSVDescription:
This dataset captures metadata for playlists, enabling analysis of user-created and system-generated music collections.
- Contains playlist IDs, names, creator info, and timestamps
- Core for linking followers, content, and listening behavior
- Essential for evaluating playlist trends and user curation habits
- Enables segmentation by activity level, genre, or popularity
- Supports playlist recommendation and growth tracking
songs
| CSVDescription:
This file contains detailed metadata on individual songs, forming the core of content performance and playback analysis.
- Includes song IDs, names, durations, artist and album IDs
- Essential for joining with listening history, genres, and user interactions
- Enables track-level insights into popularity, engagement, and duration trends
- Supports building of content profiles, search optimization, and recommendations
- Complements artist and genre data for full music content analysis
user_playlist_follows
| CSVDescription:
This file tracks which users follow which playlists, offering insights into social listening and discovery behavior.
- Includes user and playlist IDs
- Essential for analyzing social trends and playlist virality
- Enables targeting of influential curators and community playlists
- Supports recommendation logic and discovery optimization
- Complements playlist metadata and user behavior analysis
songs_liked
| CSVDescription:
This dataset logs user likes on songs, capturing direct feedback and personal preference indicators.
- Contains user IDs, song IDs, and timestamps
- Essential for measuring song appeal and user engagement
- Supports personalized recommendation algorithms
- Enables segmentation by song popularity and user sentiment
- Complements listening history and playlist analysis
users
| CSVDescription:
This file maintains detailed demographic and account information for users, forming the foundation for personalization and behavior tracking.
- Contains user IDs, names, registration dates, and other identifiers
- Serves as the central join point for listening, preferences, and playlists
- Essential for cohort analysis, user segmentation, and retention modeling
- Enables insights by region, registration time, and user lifecycle stage
- Supports personalization systems and user-centric performance reports
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