PostgreSQL & Power BI : Portfolio Case Study

Spotify Artist Streaming Analytics & BI Dashboard

End-to-End Music Industry Analytics using Kaggle Spotify Dataset (2015-2025), Multi-Month ETL Data Staging, PostgreSQL Database Queries, and Power BI Visuals
Spotify Streaming Analytics Dashboard Power BI

Power BI Interactive Executive Dashboard Overview

Comprehensive music streaming intelligence dashboard built in Microsoft Power BI and powered by a structured PostgreSQL relational database. Click to expand image.

85,000+
Analyzed Tracks
24+ Mos
Multi-Batch Staging
10+ Billion
Streams Aggregated
100%
PostgreSQL & BI Integration

✦ 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 & Findings

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.
Artist Streaming Leaderboard Anthony Walker commanded the top position with 40M total streams, followed by Peter Hall (34M) and Rebecca Brown (28M).
Record Label Market Share EMI (2,451M streams), Sony Music (2,421M), and Independent releases (2,384M) formed the top triumvirate.
Harmonic Mode Equilibrium Across 85,000+ tracks, musical mode distribution was remarkably balanced: 50.01% Minor mode vs 49.99% Major mode.

✦ 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;

✦ PostgreSQL Query Execution Gallery

Click on any query screenshot below to view the PostgreSQL 18 / pgAdmin execution outputs:

Top Artists Query
Top Artists↗
Genre Streaming Query
Genre Ranking↗
Record Label Query
Record Labels↗
Country Volume Query
Country Analysis↗

Project Information

Project Title
Spotify Artist Streaming Analytics
Platform / Tools
PostgreSQL 18, pgAdmin 4, Power BI, SQL
Dataset Source
Spotify Analytics 2015-2025 (Kaggle)
Status
Completed Project

Skills & Technologies

PostgreSQL 18 pgAdmin 4 Advanced SQL Microsoft Power BI Power Query ETL Data Cleaning Relational Modeling

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