Ray Data Snowflake Connector

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I have been trying to motivate my team towards using Ray with our snowflake setup. I recently came across this anyscale blog – "Introducing the Anyscale Snowflake Connector" which excited my further. After some searching on github, I could find the code for this connector as well. I am a complete complete newbie to ray and hence couldn’t get this connector to work consistently. However, I am thoroughly familiar with snowflake and its python connector and I have a complete understanding how a snowflake connector in ray should work, so I went ahead and wrote my connector while seeking motivation from the above codebase as well as from the ray custom datasource documentation. :drum: GitHub - prabodh1194/ray_snowflake :drum: Please go through my repo and fire away with questions and PRs and ideas on how can we go about using these. Thanks a ton. P.S.: I’ll be spending sometime this week to create an optimal write step which can directly write an arrow table to snowflake instead of taking the Pandas route.

Visit product View on Hacker NewsSep 25, 2023

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

85%85% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

17%17% 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.

Correct prediction on native model

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