Analyzing Content Performance Using SQL

Group Category: Use Case

Product Category: Database Design & Development

Sub Category: PostgreSQL

Discover content trends and improve your SQL skills with real music streaming data.

Business Overview:

Analyzing Content Performance Using SQL helps you understand how music content performs on a streaming platform. Using realistic data from a simulated music service, you'll explore which songs and genres are most played, which albums get the most engagement, and what content goes unnoticed. This guide is designed for those who want to use SQL to answer real business questions and improve decisions around music promotion, playlist creation, and content strategy.

Product Highlights:

A comprehensive PDF guide featuring 5 practical SQL use cases
Includes sample outputs to show expected analytical results
Built on a realistic music streaming dataset of over 25,000 rows with relational tables (songs, artists, albums, genres, and listening history)
Perfect for SQL learners, content analysts, and product teams in media or entertainment
Valuable addition to portfolios demonstrating content analytics and SQL reporting skills

Learning Outcomes:

By working through this product, you’ll be able to:

Write useful SQL queries to measure music content performance
Identify top songs, most-liked content, and underplayed tracks
Analyze user engagement by genre and album
Use SQL to support decisions around content strategy and promotion
Build confidence in interpreting real-world datasets with SQL

1/7

albums

| CSV

Description:
This CSV captures metadata about music albums, enabling aggregation of songs under common releases for catalog and timeline analysis.

Includes album IDs, names, release dates, and artist associations
Supports album-level performance tracking and content lifecycle reporting
Useful for artist discography mapping and release trend analysis
Complements song, artist, and genre datasets for full media catalog insights
Enables timeline comparisons for new vs legacy content

song_genres

| CSV

Description:
This dataset links songs to one or more genres, supporting flexible categorization and multi-genre analysis in a music recommendation context.

Includes song IDs and corresponding genre IDs
Enables songs to be classified under multiple genre dimensions
Useful for diversity analysis, cross-genre playlist generation, and filtering
Supports genre-driven discovery, marketing, and engagement metrics
Connects directly to songs and genres for tagging enrichment

songs

| CSV

Description:
This file contains metadata about individual songs, providing the core unit for listening, preference, and performance analysis.

Includes song IDs, names, durations, and album/artist links
Supports tracking of plays, likes, and playlist appearances
Useful for trend detection, content ranking, and playback reporting
Integrates with user activity data for personalized experience building
Complements albums, artists, and genres for full content profiling

genres

| CSV

Description:
This CSV defines the genre taxonomy used throughout the platform, serving as a reference for musical classification and discovery systems.

Includes genre IDs and genre names
Supports song tagging, filtering, and recommendation clustering
Useful for analyzing user preferences and genre-based engagement
Complements song_genres and songs datasets for personalized curation
Enables genre-level performance tracking and audience targeting

artists

| CSV

Description:
This dataset provides key information about musical artists, forming a foundational reference for content, playlist, and performance analysis.

Contains artist IDs, names, and optional profile data
Enables linking across songs, albums, and user engagement metrics
Useful for artist popularity tracking, portfolio analysis, and search/filter logic
Supports personalized content feeds and artist-based recommendations
Complements genre tagging and fanbase segmentation

songs_liked

| CSV

Description:
This CSV captures the relationship between users and songs they’ve liked, providing insight into personal preferences and content resonance.

Includes user IDs and liked song IDs with timestamps
Enables building of user profiles based on music preferences
Useful for popularity metrics, recommendation engines, and retention modeling
Supports sentiment tagging and fanbase scoring at the song level
Complements user and song datasets for deep personalization

user_listening_history

| CSV

Description:
This file logs detailed user interactions with songs over time, acting as the foundation for behavioral analysis and recommendation modeling.

Contains user IDs, song IDs, and timestamps of plays
Enables frequency and recency analysis for engagement tracking
Useful for building daily active user metrics, session timelines, and listening heatmaps
Supports content ranking, skip detection, and personalized recommendations
Complements user and song metadata for full playback insight

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