Building a Robust Data Scraper

In the data-driven world of political analysis, having access to the latest polling data is crucial. My latest project was born out of a desire to tinker with web scraping while embracing industry-standard tooling. This wasn’t just an isolated exercise but a precursor to a series of ambitious projects I have envisioned in the political polling arena.

Production Quality Code

A primary goal of this project is to demonstrate what production-quality code should look like. This meant writing clean, maintainable, and efficient code that could be scaled and improved over time. My experience as a data scientist has led me to appreciate a rigorous approach to coding, focusing on a clear delineation of function, a highly modular development style and well documented code base.

Testing

To ensure that the tool continues working under any future scenarios, I developed an extensive testing suite. This suite covered everything from unit tests that validated individual components to integration tests that ensured the scraper and the endpoint communicated correctly. Testing is often undervalued, but it is the cornerstone of reliable code. The tests were automated, running on various datasets to simulate real-world conditions.

Continuous Integration and Deployment

The modern software lifecycle is incomplete without Continuous Integration (CI) and Continuous Deployment (CD). For this project, I utilized GitHub pipelines to automate the workflow. Each commit triggered a series of actions, from running tests to deploying the code to the production environment. This CI/CD pipeline ensured that any updates or fixes were swiftly and safely implemented, minimizing downtime and maintaining the tool’s integrity.

Documentation

Comprehensive documentation is the hallmark of any serious software tool. With this in mind, I meticulously documented the entire process on ReadTheDocs, ensuring that anyone from a future collaborator to an enthusiastic learner could understand and utilize the tool effectively. This documentation covers everything from setup to troubleshooting, providing a clear roadmap for the tool’s usage.

A Peek into the Repository

The GitHub repository serves as the central hub for the tool. It’s where you can find the source code, the testing suite, and the pipeline configurations. Each piece of the code is commented and organized for ease of navigation. Feel free to dive into the repository and explore the inner workings of the scraper:

• Source Code: GitHub Repo
• Testing Suite: GitHub Repo - Tests
• Pipeline Configurations: GitHub Repo - CI/CD Pipeline

ReadTheDocs

For a more structured and detailed guide, the ReadTheDocs documentation is your go-to resource. It offers step-by-step instructions, best practices, and troubleshooting tips:

• Full Documentation: ReadTheDocs Documentation

Conclusion

Sometimes the best way to develop a skill set, and to demonstrate the utility of current industry standard tooling is to play around with a toy problem in a sandbox environment, and to see what you can learn from this. I’ve had a lot of fun in doing so, and I hope you have enjoyed reading about this process!

Whether you’re a fellow data scientist, a political analyst, or simply a curious mind, I invite you to explore the repository, peruse the documentation, and perhaps, suggest improvements to the project.