Working with data is a core part of my daily dev life. But I’ve made my fair share of mistakes along the way. These are 6 common traps I’ve learned to avoid — and what I do differently now.
❌ 1. Assuming the data is “clean” by default
I used to think a well-structured CSV was enough. It’s not.
✅ Now I validate everything — with schemas (Pydantic, Zod, etc.), type checks, and sanity checks.
❌ 2. Diving into code before exploring the data
I’ve written complex queries and loops without understanding what the data looked like.
✅ Today, I always start with a quick look: print(), head(), group by, describe() — simple, but essential.
❌ 3. Using the wrong tool for the data size
I’ve tried to process 8GB of data with Pandas on my laptop. Didn’t end well.
✅ Now I pick the right tool: DuckDB, Polars, or BigQuery — depending on the volume.
❌ 4. Storing data without context
I’ve had JSON files lying around with zero documentation. Later, I had no idea where they came from or what they represented.
✅ I include metadata: source, date of extraction, transformations, and purpose.
❌ 5. Mixing raw and processed data
I’ve spent hours wondering if a dataset was the original or something I’d cleaned earlier.
✅ Now I separate my layers: raw/, clean/, final/. No more confusion.
❌ 6. Making ad hoc manual changes
Quick edits for testing are tempting. But when they creep into production? Ouch.
✅ I script all transformations, version my pipelines, and automate whenever possible.
📌 These days, I treat data like code: it deserves structure, versioning, and care.
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