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

1/9

artists

| CSV

Description:
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

playlists

| CSV

Description:
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

song_genres

| CSV

Description:
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

genres

| CSV

Description:
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

| CSV

Description:
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

songs

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

Analyzing Playlist and Social Features Using SQL

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