Can ChatGPT build a Data Platform faster than a developer?
summary
A timed race reconstructs an energy generation dashboard from ElectricityMap data and shows concrete differences: one approach ingests with Airbyte → BigQuery → Looker, flattens ~20 nested objects, stores historical data and builds a stacked, colored visualization; the other has ChatGPT generate HTML/JS/CSS that pulls the API directly, avoids storage, and produces working UI but miscolors stacks and needs repeated prompts. Results vary: ChatGPT runs in ≈25–45 minutes inconsistently; developer build is stable (finished in ~26 minutes) and scales better.
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chapters (7)
- 0:00Introduction and context
- 2:45Experiment setup
- 10:31Live race demonstration
- 14:44Results and inconsistencies
- 16:33Key differences analyzed
- 22:36Hybrid approach benefits
- 23:59Conclusion and Q&A
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