API to Generate SQL on Top of Cloud Data Warehouses

Hacker News

Here is the query to create a segment of users who have abandoned their cart in Snowflake. select * from users where "email" in (select "email" from (select events.* from (select * from cart_events where "tag" = 'added_to_cart') as events join (select "email", max("event_ts") as max_event_ts from (select * from cart_events where "tag" = 'cart_checkout') group by "email") as otherEvents on events."email" = otherEvents."email" where (events."event_ts" > otherEvents.max_event_ts and timestampdiff(minute, events."event_ts", current_timestamp()) > 2880) union (select * from (select * from cart_events where "tag" = 'added_to_cart') as events where "email" not in (select "email" from (select * from cart_events where "tag" = 'cart_checkout')) and timestampdiff(minute, events."event_ts", current_timestamp()) > 2880))) But can non-technical folks write this query on top of Snowflake/BigQuery? We launched our Audiences feature to solve this problem for non-technical users who still rely on the data warehouse. Here is our YC Launch https://www.ycombinator.com/launches/IHo-castled-build-audiences-on-your-data-warehouse-without-sql As we talked to more developers, we realised that many are trying to build products on top of their data warehouse for their customers or internal teams. But there is no easy way to make the data warehouse accessible to users who cannot write SQL. To solve this problem, we are launching Audiences API, which enables developers to build products that generate user segments on top of data warehouses without SQL and then sync them to external destinations like Google Ads, Salesforce, etc. Here is how the product will look once integrated with the Audience API. https://www.youtube.com/watch?v=yocbTTr-twU

Visit product View on Hacker NewsMar 23, 2023

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

88%88% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

67%67% 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 · Strong signals: google, users, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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