Billboard Chart Data Project: Automating Billboard Chart Collection
Updated: Jul 20
Not every data project begins with a business problem. Sometimes it starts with a question. For me, it was simple:
"Where can I find a complete history of Billboard magazine charts?"
The more I searched, the more I realised there wasn't a dataset that met my needs. Most focused almost entirely on the Billboard Hot 100, while others covered only a handful of charts or lacked confidence in their completeness. I wasn't just interested in one chart—I wanted to analyse decades of music trends across multiple genres and chart types.
Rather than trying to stitch together several third-party datasets, I decided to build one myself.
That decision became the foundation of a much larger project: creating an automated data pipeline capable of collecting historical Billboard chart data that could be trusted, maintained, and expanded over time.
Why This Project?
Although the end goal was music analytics and playlist creation, the real challenge wasn't analysing the data...it was acquiring it.
I needed a solution that could:
Collect historical data spanning almost 60 years.
Support dozens of Billboard charts with different publication dates and chart sizes.
Produce consistent, analytics-ready data.
Recover automatically if interrupted.
Scale easily as new charts or years became available.
Instead of building a one-off scraper, I wanted something I could continue using for years.
The Approach:
I designed a configurable Python data pipeline that automated the collection of historical Billboard chart data.
The Pipeline:
Read a configurable list of Billboard chart definitions.
Generated every required weekly chart date.
Downloaded each historical chart automatically.
Extracted chart rankings, artists and song titles.
Removed duplicate records.
Continuously saved progress throughout execution.
Generated analytics-ready CSV files.
Produced JSON execution logs containing run statistics, progress information and metadata for validation.
One design decision proved particularly valuable...Rather than processing every year for a single chart, I chose to process one year across every chart. While either approach would ultimately produce the same data, organising the output by year resulted in cleaner CSV files that were significantly easier to validate, import into Excel using Power Query, and use as incremental data sources for later analysis.
Sometimes the best technical solution isn't the fastest, it's the one that makes everything downstream simpler.
Challenges Along The Way:
Building the pipeline meant solving more than just web scraping.
Each Billboard chart has its own history. Some date back to 1958, while others were introduced decades later. The pipeline therefore needed to understand which weeks existed for each chart instead of assuming a common timeline.
Long-running execution also became an important consideration. Collecting decades of weekly chart data across dozens of charts could take hours, so reliability mattered just as much as speed.
To address this, I built features including:
Automatic checkpointing
Resume functionality
Continuous autosaving
Duplicate prevention
Progress monitoring
Execution logging
These additions meant interrupted runs could continue from the last successful checkpoint rather than starting over, making the pipeline robust enough to collect millions of records with minimal manual intervention.
The Results:
The completed pipeline successfully collected approximately 2.1+ million historical Billboard chart records across 30+ chart types, producing a consistent dataset ready for analysis.
More importantly, it created a reusable framework that can be used year after year rather than solving a one-time problem.
Adding another year of data—or even an entirely new Billboard chart—requires minor configuration changes rather than rewriting the code.
What I Learned:
This project changed the way I think about building data solutions.
I learned that configurable systems are almost always better than hard-coded ones.
I learned that automation without reliability isn't enough; features like checkpointing, duplicate detection and execution logging are just as important as collecting the data itself.
Most importantly, I started thinking about the people consuming the data long before the analysis began. Decisions around file structure, naming conventions and processing strategy were all driven by how easily the output could be imported into Excel, transformed in Power Query and used for future reporting and visualisation.
Building the pipeline was only half the job.
Designing it for analysis was what made it valuable.
Looking Ahead:
The pipeline is already pretty robust, but there are several enhancements I'd like to make in the future.
These include automatically retrying incomplete or failed weeks, recognising seasonal charts so unnecessary requests can be skipped, and scheduling weekly executions so new Billboard data is collected automatically as it becomes available.
Those improvements would make the pipeline even more efficient while preserving the reliability that's already been built into the solution.
Final Thoughts:
On the surface, this project is about collecting Billboard chart data.
In reality, it's about designing a reliable data acquisition pipeline that transforms fragmented source data into a trusted analytical asset.
That's a challenge that extends well beyond music, and it's an approach I'll continue applying to future data projects.
Call To Action:
If this project sparked your interest or you're working on something similar, I’d love to connect on LinkedIn. And if you know someone looking for a data analyst who’s passionate about solving real problems with data...let’s talk!
