Run ETL Pipelines and Store Data in GitHub

Hacker News

Hey HN! We built an extension to GitHub that scales repos to handle large files. One of the side effects of this is that you can use GitHub actions to run data ETL pipelines and store the results in the same repo as your ETL code. No need to manage S3 credentials or setup a bunch of extra tools for many ETL jobs. I wrote a quick tutorial on how that works here: https://about.xethub.com/blog/simple-etl-pipelines-git-xet-github-actions This is our starter repo if you want to try this yourself: https://github.com/xetdata/easy-etl-template

Visit product View on Hacker NewsJan 9, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

60%60% 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

52%52% 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

48%48% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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