Content Performance & Music Catalog Analytics Dataset
Group Category: Dataset
Product Category: Database Design & Development
Sub Category: PostgreSQL
Evaluate content performance, measure listener preferences, and optimize music catalogs with structured, SQL-ready analytics datasets.
Business Overview:
This dataset focuses on catalog and content performance, offering relational data across songs, albums, artists, and genres. It enables detailed analysis of music consumption trends and supports catalog optimization efforts.
Product Highlights:
- Well-structured CSV datasets (
songs,albums,artists,genres,listening history) - Allows analysis of content hierarchy and performance
- Ideal for editorial teams, licensing strategy, and A&R functions
- Supports SQL practice on normalized catalog data
Learning Outcomes:
- Analyze genre and album-level engagement
- Evaluate completion rates and song performance
- Practice many-to-many mapping through song-genre tables
- Understand relational data modeling in media analytics
genres
| CSVDefines the available music genres along with optional descriptions. This file supports genre-based filtering, classification, and music preference modeling.
Key Features:
- 70 genre entries with descriptions
- Enables categorization of songs and content clusters
- Useful for genre trend analysis or tagging pipelines
Dataset Document
| PDFThis documentation presents a dataset that links song metadata, artist details, album releases, and genre associations. It supports insights into content popularity, catalog reach, and musical trends.
Highlights:
- Describes 6 CSV tables including songs, artists, albums, and genres
- Suitable for music trend discovery, content licensing decisions, and catalog optimization
- Ideal for music editors, content strategists, and data scientists in the media domain
albums
| CSVContains structured metadata about music albums including titles, release dates, and artist associations. This file supports catalog organization, artist discography analysis, and release tracking.
Key Features:
- 3,197 album records with titles and release data
- Direct linkage to artists via foreign key
- Enables chronological or artist-based album exploration
artists
| CSVIncludes descriptive records of music artists, such as names, bios, and profiles. Useful for analyzing content contributors, artist catalog depth, and metadata completeness.
Key Features:
- 1,097 artist records with rich metadata
- Supports artist profiling and contribution aggregation
- Ideal for catalog attribution and artist-based queries
song_genres
| CSVMaps songs to their respective genres, allowing for multi-genre tagging and classification. Enables thematic analysis and genre-based recommendation features.
Key Features:
- 9,383 records linking songs to genres
- Supports multi-genre mapping per song
- Enables genre analytics, filtering, and mood analysis
songs
| CSVIncludes detailed song-level metadata like title, duration, album, and artist references. This file supports playback analytics, catalog navigation, and track popularity insights.
Key Features:
- 9,383 song entries with duration and creator metadata
- Links to both
albumsandartiststables - Ideal for catalog indexing, filtering, and performance comparison
user_listening_history
| CSVCaptures timestamped listening events for users across songs. Enables behavioral modeling, listening trend analysis, and peak usage discovery.
Key Features:
- Tracks listening sessions with user and song references
- Timestamped to support daily, weekly, or seasonal analysis
- Core dataset for behavioral and engagement analytics
$1.54 $1.00 35% OFF
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