spotify-data-analyze

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spotify-data-analyze

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A serverless data pipeline that logs my Spotify listening history to BigQuery using Cloud Run, then visualizes trends with Looker Studio. Built with Python, Flask, Docker, and GCP..

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# Spotify Listening Logger

This is a personal data project focusing on **data engineering** and **analytics engineering** skills.

- **Data engineering**: ETL pipelines, orchestration, and cloud infrastructure  
- **Analytics engineering**: **BigQuery SQL**, data modeling, building analysis-ready datasets

It automatically logs my Spotify listening history every minute and stores it in **BigQuery** for analysis.

Links: 
- [Blog Post](https://www.kellyjadams.com/post/spotify-listening-logger)

## Why I Built This

At the end of the year, I want to compare what's logged in BigQuery with my annual **Spotify Wrapped**.

I wanted to explore how to:

- Build a serverless data pipeline using **Cloud Run**, **Docker**, and **GitHub Actions**
- Manage secrets securely with `.env` files and **GitHub Secrets**
- Schedule and orchestrate updates using **Cloud Scheduler**
- Structure and transform data for analysis in **BigQuery**
- Design pipelines that are scalable and support near real-time insights

This project brings together core **cloud** and **analytics engineering** tools to build something end-to-end—from ingestion to analysis.

## Key Features

- **Data Ingestion & Deployment**
  - Polls Spotify’s now-playing endpoint every minute using a serverless Cloud Run app
  - Built with a containerized Python app using Flask + Spotipy
  - Deployed manually using a shell script; Cloud Scheduler handles orchestration by triggering the Cloud Run endpoint every minute.
- **Data Storage & Modeling**
  - Streams listening history into **BigQuery**
  - Cleans and deduplicates plays for session-level analysis
  - Stores detailed metadata: artist, album, genre, popularity, duration
- **Analysis & Visualization**
  - Designed analysis-ready datasets using **BigQuery SQL**

## Technical Skills

- **Data Pipeline Design**: Built a serverless ETL pipeline from Spotify API to BigQuery using Python and Cloud Run
- **Cloud-Native ETL**: Extracted, transformed, and loaded data on a schedule using Cloud Scheduler and containerized Flask app
- **Python**: Wrote ingestion and transformation logic using the `Spotipy` library
- **REST API**: Created a lightweight endpoint to trigger ingestion using Flask
- **BigQuery**: Designed table schema and streamed structured data for analysis
- **Docker + Cloud Run**: Packaged and deployed the app as a scalable container
- **Environment variable management**: Handled secrets securely using `.env` and GitHub Secrets

## Project Structure

```
spotify-data-analyze/
├── analysis/
│   ├── queries/
│   ├── views/
│   │   ├── deduped_plays_pacific.sql
├── cloud/
│   └── playback/
│       ├── main.py
│       ├── Dockerfile
│       ├── requirements.txt
│       └── deploy.sh
├── scripts/
│   ├── create_bigquery_table.py            
│   ├── delete_bigquery_table.py            
│   └── load_env.py                
├── .env                           
└── .github/workflows/
    └── cloud-deploy.yml
```

## Environment & Deployment

This project uses a `.env` file for Spotify and GCP credentials. These variables are injected during Cloud Run deployment.

I deployed the app manually using:

```bash
cd cloud/playback
./deploy.sh
```

I automated ingestion, by setting up a **Cloud Scheduler** job to hit the app endpoint every minute.

The data is stored in a **BigQuery** table with fields like `track`, `artist`, `genre`, and `popularity`. See `create_bigquery_table.py` for schema setup.

## Next Steps

Below are my next steps:
- Automate CI/CD deployment via GitHub Actions
- Finalize my Looker Studio Dashboard that automatically retrieves data from my BigQuery tables
- Analyze stats using BigQuery SQL queries

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