Bi

Bidirectional ETL between spreadsheets and your warehouse

Hacker News

Bidirectional ETL between spreadsheets and your warehouse

Hey HN! Ian, Vinesh, and Kunal here. We’re building Bracket (YC W22), a bidirectional ETL platform — software that syncs data two ways between your data stack and the spreadsheet tools that your business teams rely on (e.g. Google Sheets, Airtable, and Notion databases), with the ability to add transformation logic (like SQL statements or MongoDB aggregation statements) in between. ( https://www.usebracket.com/ ) In other words, if you want to join columns from two Postgres tables, sync the result to an Airtable table, and allow your Airtable users to edit certain fields from either table back to Postgres, you can do that with Bracket. We built this for teams that would otherwise need to cobble together ETL & rETL pipelines, or spin up a bunch of Zapier, Pipedream, or other event-based triggers. Here are some of the use cases among our current users: -Turn feature flags on / off for their users from a G Sheet -Edit customer order data in Notion -Adjust trial periods for customers in Airtable -Edit user info (like email) based on input sent to customer success teams -Track and edit the status of support tickets See a demo of a sync between Google Sheets and Snowflake here: https://www.loom.com/share/d253876247ce4e0b8636beec681f0e64 Transparently, we are figuring out pricing and care way more about feedback which is why we don’t have a pricing page — we don’t want that to be an impediment to getting your input and building a great product. We’d love to hear your thoughts, feedback, and use cases. Especially if you’ve been looking for a bidirectional ETL tool now or in the past, we’d love to hear what tools you want to connect!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, email · Missing: mac, agents, macos
87%87% 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: pipe, io · Missing: https docs, excited, just released
74%74% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
29%29% 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
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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