As developers, we sometimes build small side projects that don’t have to “solve” anything — they just help us understand data better. Mine started with something as ordinary as a drink menu.
I was looking at how restaurant menus constantly change — new flavors, new sizes, seasonal items — and thought: what if menus were treated like structured data instead of random text?
Starting Small
I started by scraping and manually collecting a few drink items, just to test how consistent menu data really is. Each item had a name, price, description, and sometimes nutrition info. That’s already a small dataset.
To make it easy to store and update, I used JSON. Here’s what an early structure looked like:
{
"drink": "Cherry Limeade",
"category": "Soda",
"price": 2.49,
"size": ["Small", "Medium", "Large"],
"flavors": ["Cherry", "Lime"],
"available": true
}
Simple, but flexible. From there, I built a small script to track updates, similar to how you’d monitor API changes.
Why It Was Interesting
The exercise wasn’t about building an app — it was about understanding data relationships in something everyday. Menus are actually perfect examples of structured but inconsistent data.
Some insights that stood out:
Many menus use similar naming patterns — easy for mapping.
Seasonal items behave like “feature flags.”
Prices vary slightly but follow predictable size patterns.
It’s like building a mini CMS or inventory system, except the dataset happens to be drinks.
Tools I Used
I kept it simple — Python for parsing, Markdown for documentation, and JSON for data storage. No database needed for a small experiment like this. I also used Git to version changes, just to see how often “menu updates” would appear like code commits.
What I Learned
Treating real-world data as structured information gives you a better eye for patterns. It’s not just for APIs or e-commerce — even a drink menu can teach you something about data integrity, versioning, and consistency.
For example, after a few iterations, I realized it’s easy to predict when certain drinks might appear again based on past data. That’s a weirdly satisfying insight — a mix of coding and curiosity.
If you’re experimenting with small datasets or want to learn data structuring through something fun, a menu is a great place to start.
You can even explore how a real menu is organized at the Swig menu — it’s a good example of diverse items, pricing tiers, and seasonal updates that make for interesting data practice.
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