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Automated data extraction from REST APIs in Python

Hacker News

Automated data extraction from REST APIs in Python

Dear HN community, I’m Adrian, one of the creators of dlt, the most popular OSS python library for building data ingestion pipelines. It automates all the tedious data engineering tasks, with features like schema evolution, data normalization and incremental loading. Recently we released what we call the “REST API source” which provides our users with a declarative interface to define what data to pull from an API. Simply define a python dictionary with your API endpoints, some authentication, where you want to load your data into and dlt will handle the rest!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks, apis · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive
26%26% 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
18%18% 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.

Incorrect prediction on native model

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