Spotify Artist Streaming Analytics & BI Dashboard : PostgreSQL & Power BI : Kafa Al Ganiyu

Background & Project Overview

The global music streaming ecosystem generates billions of listening events daily. For this project, I engineered an end-to-end analytics workflow using the Spotify Artist Streaming Analytics Dataset (2015-2025) from Kaggle. The raw data consisted of over 24 individual monthly transaction files spanning late 2023 through late 2025, each requiring systematic data cleaning, standardization, and schema alignment.

Rather than loading unorganized CSV files directly into visualization tools, I established a robust PostgreSQL 18 relational database via pgAdmin 4. In PostgreSQL, I executed data validation, handled duplicates, built performant aggregation queries, and extracted multi-dimensional metrics. Finally, I connected Power BI directly to PostgreSQL to deliver an executive-grade, dark-themed business intelligence dashboard tailored for music industry stakeholders and artist managers.

Key Business Insights & Analytical Findings

Cross-analyzing streaming counts, release timestamps, genres, and geographic territories revealed crucial commercial insights:

Mid-Week Release Dominance Wednesday recorded the single highest track release volume (~12,400 songs), indicating a strategic industry shift to optimize mid-week algorithmic playlist inclusions before weekend listening peaks.

Artist Streaming Leaderboard Anthony Walker commanded the top position with 40M total streams, followed by Peter Hall (34M) and Rebecca Brown (28M), highlighting a concentrated top-tier listener engagement.

Record Label Market Share EMI (2,451M streams), Sony Music (2,421M streams), and Independent releases (2,384M streams) formed the top triumvirate, closely followed by Universal (2,276M) and Warner (2,242M).

Harmonic Mode Equilibrium Across 85,000+ tracks, musical mode distribution was remarkably balanced: 50.01% Minor mode (43,000 tracks) vs. 49.99% Major mode (42,000 tracks), demonstrating equal listener demand for both tonal varieties.

International Market Distribution Japan, France, and Mexico generated the highest aggregate stream volumes (~2B streams each), followed closely by Canada, the United Kingdom, Germany, India, and Brazil.

Genre Streaming Hierarchy Hip-Hop (190.6M streams) and R&B (162.5M streams) generated the highest streaming engagement, followed by Metal (160.4M) and Classical (155.8M).

SQL Query Engineering & Data Pipeline Architecture

1. Multi-Batch Data Preparation: Ingested 24+ monthly datasets, standardized datetime headers, validated numeric stream counts, and constructed unified master tables.

2. PostgreSQL Database Querying & Optimization: Wrote structured SQL queries in PostgreSQL 18 for high-speed grouping, aggregations, and ranking:

SELECT artist_name, SUM(stream_count) AS total_stream
FROM spotify_data_processed
GROUP BY artist_name
ORDER BY total_stream DESC
LIMIT 10;

SELECT genre, SUM(stream_count) AS total_stream
FROM spotify_data_processed
GROUP BY genre
ORDER BY total_stream DESC;

3. Power BI Database Connector & Dark Theme Visualization: Loaded PostgreSQL tables directly into Microsoft Power BI, designed custom Spotify brand palettes (#1DB954 green, dark gray canvas), and built interactive cross-filtering visuals.