MuseDash is an interactive music analytics dashboard built to analyze historical streaming data from Zip-pot-ify, a fictional but nationwide music platform. The project showcases our ability to design and deploy a full data engineering and analytics pipeline — from raw data ingestion to dynamic visualizations — highlighting regional listening trends, artist popularity, genre breakdowns, and time-based metrics.
This project was developed collaboratively as a portfolio piece to demonstrate modern data engineering and data visualization practices.
- Angelika Brown — LinkedIn
- Isiah Armstrong — LinkedIn
- James Heller — LinkedIn
- Kunle Adeyanju — LinkedIn
| Layer | Tools & Technologies |
|---|---|
| Data Ingestion & Storage | AWS S3 (data storage) |
| Data Processing | PySpark (distributed processing), Pandas (data wrangling) |
| Visualization | Altair, Plotly |
| Application Layer | Streamlit (interactive dashboard) |
| Version Control & Collaboration | GitHub |
Our pipeline processes millions of rows of listening data efficiently, using a combination of cloud storage, distributed computing, and interactive front-end visualization.
Workflow:
- Data Storage: Raw Data files stored in AWS S3.
- Data Processing & Enrichment: Data is loaded into PySpark where it is cleaned, filtered, and transformed at Scale
- For visulaizion, the processed data is converted into Pandas DataFrames
- We call AI APIs to supplement and enrich the data, such as generating music genre information for artis, which was not avaliable in the source dataset.
- Analytics: Generated metrics such as:
- Most streamed artists/songs by region
- Genre popularity trends over time
- Listening activity heatmaps
- Visualization: Interactive charts and maps using Altair & Plotly.
- Dashboard Deployment: Streamlit app providing filtering, search, and drill-down capabilities.
📌 Pipeline Diagram:
- Choropleth Maps — visualize listening habits across U.S. states.
- Artist & Genre Filters — deep dive into specific music categories.
- Time-based Trends — track popularity shifts over time.
- Responsive Design — fast filtering with Streamlit caching for smooth UI.