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DataCompose – Composable data transformation primitives for PySpark

Hacker News

DataCompose – Composable data transformation primitives for PySpark

This is NOT a traditional library - it gives you production-ready data transformation primitives that you can modify to fit your exact needs. You own the code, with no external dependencies to manage or worry about breaking changes. Inspired by shadcn's component approach: instead of importing a black box, you get battle-tested PySpark functions that live in YOUR repository. Handle email validation, address parsing, phone formatting.

Visit productView on Hacker NewsAug 25, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, code · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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