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Building an Airtable Alternative with DuckDB and Python for African Market Data

9 min read  · 1,752 wordsBy Orandi Felix

The entire system I'm describing fits in under 300MB RAM when idle, runs comfortably on a 2015 MacBook Air, and stores six months of African startup data (50k+ records) in 47MB on disk.

I once tried enforcing FLOAT for funding amounts. It failed spectacularly when encountering "USD 1M (approx)" or "500k–700k". TEXT plus a simple regex cleanup (`round_amount_clean = re.findall(r"\d+\.?\d*", amount_str)[0]` if present) works better.

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